Linear Algebra
In addition to (and as part of) its support for multidimensional arrays, Julia provides native implementations of many common and useful linear algebra operations which can be loaded with using LinearAlgebra
. Basic operations, such as tr
, det
, and inv
are all supported:
julia> A = [1 2 3; 4 1 6; 7 8 1] 3×3 Array{Int64,2}: 1 2 3 4 1 6 7 8 1 julia> tr(A) 3 julia> det(A) 104.0 julia> inv(A) 3×3 Array{Float64,2}: 0.451923 0.211538 0.0865385 0.365385 0.192308 0.0576923 0.240385 0.0576923 0.0673077
As well as other useful operations, such as finding eigenvalues or eigenvectors:
julia> A = [4. 17.; 2. 2.] 2×2 Array{Float64,2}: 4.0 17.0 2.0 2.0 julia> eigvals(A) 2element Array{Complex{Float64},1}: 1.0  5.0im 1.0 + 5.0im julia> eigvecs(A) 2×2 Array{Complex{Float64},2}: 0.9459050.0im 0.945905+0.0im 0.166924+0.278207im 0.1669240.278207im
In addition, Julia provides many factorizations which can be used to speed up problems such as linear solve or matrix exponentiation by prefactorizing a matrix into a form more amenable (for performance or memory reasons) to the problem. See the documentation on factorize
for more information. As an example:
julia> A = [1.5 2 4; 3 1 6; 10 2.3 4] 3×3 Array{Float64,2}: 1.5 2.0 4.0 3.0 1.0 6.0 10.0 2.3 4.0 julia> factorize(A) LU{Float64,Array{Float64,2}} L factor: 3×3 Array{Float64,2}: 1.0 0.0 0.0 0.15 1.0 0.0 0.3 0.132196 1.0 U factor: 3×3 Array{Float64,2}: 10.0 2.3 4.0 0.0 2.345 3.4 0.0 0.0 5.24947
Since A
is not Hermitian, symmetric, triangular, tridiagonal, or bidiagonal, an LU factorization may be the best we can do. Compare with:
julia> B = [1.5 2 4; 2 1 3; 4 3 5] 3×3 Array{Float64,2}: 1.5 2.0 4.0 2.0 1.0 3.0 4.0 3.0 5.0 julia> factorize(B) BunchKaufman{Float64,Array{Float64,2}} D factor: 3×3 Tridiagonal{Float64,Array{Float64,1}}: 1.64286 0.0 ⋅ 0.0 2.8 0.0 ⋅ 0.0 5.0 U factor: 3×3 UnitUpperTriangular{Float64,Array{Float64,2}}: 1.0 0.142857 0.8 ⋅ 1.0 0.6 ⋅ ⋅ 1.0 permutation: 3element Array{Int64,1}: 1 2 3
Here, Julia was able to detect that B
is in fact symmetric, and used a more appropriate factorization. Often it's possible to write more efficient code for a matrix that is known to have certain properties e.g. it is symmetric, or tridiagonal. Julia provides some special types so that you can "tag" matrices as having these properties. For instance:
julia> B = [1.5 2 4; 2 1 3; 4 3 5] 3×3 Array{Float64,2}: 1.5 2.0 4.0 2.0 1.0 3.0 4.0 3.0 5.0 julia> sB = Symmetric(B) 3×3 Symmetric{Float64,Array{Float64,2}}: 1.5 2.0 4.0 2.0 1.0 3.0 4.0 3.0 5.0
sB
has been tagged as a matrix that's (real) symmetric, so for later operations we might perform on it, such as eigenfactorization or computing matrixvector products, efficiencies can be found by only referencing half of it. For example:
julia> B = [1.5 2 4; 2 1 3; 4 3 5] 3×3 Array{Float64,2}: 1.5 2.0 4.0 2.0 1.0 3.0 4.0 3.0 5.0 julia> sB = Symmetric(B) 3×3 Symmetric{Float64,Array{Float64,2}}: 1.5 2.0 4.0 2.0 1.0 3.0 4.0 3.0 5.0 julia> x = [1; 2; 3] 3element Array{Int64,1}: 1 2 3 julia> sB\x 3element Array{Float64,1}: 1.7391304347826084 1.1086956521739126 1.4565217391304346
The \
operation here performs the linear solution. The leftdivision operator is pretty powerful and it's easy to write compact, readable code that is flexible enough to solve all sorts of systems of linear equations.
Special matrices
Matrices with special symmetries and structures arise often in linear algebra and are frequently associated with various matrix factorizations. Julia features a rich collection of special matrix types, which allow for fast computation with specialized routines that are specially developed for particular matrix types.
The following tables summarize the types of special matrices that have been implemented in Julia, as well as whether hooks to various optimized methods for them in LAPACK are available.
Type  Description 

Symmetric 
Symmetric matrix 
Hermitian 
Hermitian matrix 
UpperTriangular 
Upper triangular matrix 
UnitUpperTriangular 
Upper triangular matrix with unit diagonal 
LowerTriangular 
Lower triangular matrix 
UnitLowerTriangular 
Lower triangular matrix with unit diagonal 
UpperHessenberg 
Upper Hessenberg matrix 
Tridiagonal 
Tridiagonal matrix 
SymTridiagonal 
Symmetric tridiagonal matrix 
Bidiagonal 
Upper/lower bidiagonal matrix 
Diagonal 
Diagonal matrix 
UniformScaling 
Uniform scaling operator 
Elementary operations
Matrix type  + 
 
* 
\ 
Other functions with optimized methods 

Symmetric 
MV 
inv , sqrt , exp


Hermitian 
MV 
inv , sqrt , exp


UpperTriangular 
MV  MV 
inv , det


UnitUpperTriangular 
MV  MV 
inv , det


LowerTriangular 
MV  MV 
inv , det


UnitLowerTriangular 
MV  MV 
inv , det


UpperHessenberg 
MM 
inv , det


SymTridiagonal 
M  M  MS  MV 
eigmax , eigmin

Tridiagonal 
M  M  MS  MV  
Bidiagonal 
M  M  MS  MV  
Diagonal 
M  M  MV  MV 
inv , det , logdet , /

UniformScaling 
M  M  MVS  MVS  / 
Legend:
Key  Description 

M (matrix)  An optimized method for matrixmatrix operations is available 
V (vector)  An optimized method for matrixvector operations is available 
S (scalar)  An optimized method for matrixscalar operations is available 
Matrix factorizations
Matrix type  LAPACK  eigen 
eigvals 
eigvecs 
svd 
svdvals 

Symmetric 
SY  ARI  
Hermitian 
HE  ARI  
UpperTriangular 
TR  A  A  A  
UnitUpperTriangular 
TR  A  A  A  
LowerTriangular 
TR  A  A  A  
UnitLowerTriangular 
TR  A  A  A  
SymTridiagonal 
ST  A  ARI  AV  
Tridiagonal 
GT  
Bidiagonal 
BD  A  A  
Diagonal 
DI  A 
Legend:
Key  Description  Example 

A (all)  An optimized method to find all the characteristic values and/or vectors is available  e.g. eigvals(M)

R (range)  An optimized method to find the il th through the ih th characteristic values are available 
eigvals(M, il, ih) 
I (interval)  An optimized method to find the characteristic values in the interval [vl , vh ] is available 
eigvals(M, vl, vh) 
V (vectors)  An optimized method to find the characteristic vectors corresponding to the characteristic values x=[x1, x2,...] is available 
eigvecs(M, x) 
The uniform scaling operator
A UniformScaling
operator represents a scalar times the identity operator, λ*I
. The identity operator I
is defined as a constant and is an instance of UniformScaling
. The size of these operators are generic and match the other matrix in the binary operations +
, 
, *
and \
. For A+I
and AI
this means that A
must be square. Multiplication with the identity operator I
is a noop (except for checking that the scaling factor is one) and therefore almost without overhead.
To see the UniformScaling
operator in action:
julia> U = UniformScaling(2); julia> a = [1 2; 3 4] 2×2 Array{Int64,2}: 1 2 3 4 julia> a + U 2×2 Array{Int64,2}: 3 2 3 6 julia> a * U 2×2 Array{Int64,2}: 2 4 6 8 julia> [a U] 2×4 Array{Int64,2}: 1 2 2 0 3 4 0 2 julia> b = [1 2 3; 4 5 6] 2×3 Array{Int64,2}: 1 2 3 4 5 6 julia> b  U ERROR: DimensionMismatch("matrix is not square: dimensions are (2, 3)") Stacktrace: [...]
If you need to solve many systems of the form (A+μI)x = b
for the same A
and different μ
, it might be beneficial to first compute the Hessenberg factorization F
of A
via the hessenberg
function. Given F
, Julia employs an efficient algorithm for (F+μ*I) \ b
(equivalent to (A+μ*I)x \ b
) and related operations like determinants.
Matrix factorizations
Matrix factorizations (a.k.a. matrix decompositions) compute the factorization of a matrix into a product of matrices, and are one of the central concepts in linear algebra.
The following table summarizes the types of matrix factorizations that have been implemented in Julia. Details of their associated methods can be found in the Standard Functions section of the Linear Algebra documentation.
Type  Description 

BunchKaufman 
BunchKaufman factorization 
Cholesky 
Cholesky factorization 
CholeskyPivoted 
Pivoted Cholesky factorization 
LDLt 
LDL(T) factorization 
LU 
LU factorization 
QR 
QR factorization 
QRCompactWY 
Compact WY form of the QR factorization 
QRPivoted 
Pivoted QR factorization 
LQ 
QR factorization of transpose(A)

Hessenberg 
Hessenberg decomposition 
Eigen 
Spectral decomposition 
GeneralizedEigen 
Generalized spectral decomposition 
SVD 
Singular value decomposition 
GeneralizedSVD 
Generalized SVD 
Schur 
Schur decomposition 
GeneralizedSchur 
Generalized Schur decomposition 
Standard Functions
Linear algebra functions in Julia are largely implemented by calling functions from LAPACK. Sparse factorizations call functions from SuiteSparse.
Base.:*
Method
*(A::AbstractMatrix, B::AbstractMatrix)
Matrix multiplication.
Examples
julia> [1 1; 0 1] * [1 0; 1 1] 2×2 Array{Int64,2}: 2 1 1 1source
Base.:\
Method
\(A, B)
Matrix division using a polyalgorithm. For input matrices A
and B
, the result X
is such that A*X == B
when A
is square. The solver that is used depends upon the structure of A
. If A
is upper or lower triangular (or diagonal), no factorization of A
is required and the system is solved with either forward or backward substitution. For nontriangular square matrices, an LU factorization is used.
For rectangular A
the result is the minimumnorm least squares solution computed by a pivoted QR factorization of A
and a rank estimate of A
based on the R factor.
When A
is sparse, a similar polyalgorithm is used. For indefinite matrices, the LDLt
factorization does not use pivoting during the numerical factorization and therefore the procedure can fail even for invertible matrices.
Examples
julia> A = [1 0; 1 2]; B = [32; 4]; julia> X = A \ B 2element Array{Float64,1}: 32.0 18.0 julia> A * X == B truesource
LinearAlgebra.SingularException
Type
SingularException
Exception thrown when the input matrix has one or more zerovalued eigenvalues, and is not invertible. A linear solve involving such a matrix cannot be computed. The info
field indicates the location of (one of) the singular value(s).
LinearAlgebra.PosDefException
Type
PosDefException
Exception thrown when the input matrix was not positive definite. Some linear algebra functions and factorizations are only applicable to positive definite matrices. The info
field indicates the location of (one of) the eigenvalue(s) which is (are) less than/equal to 0.
LinearAlgebra.dot
Function
dot(x, y) x ⋅ y
Compute the dot product between two vectors. For complex vectors, the first vector is conjugated.
dot
also works on arbitrary iterable objects, including arrays of any dimension, as long as dot
is defined on the elements.
dot
is semantically equivalent to sum(dot(vx,vy) for (vx,vy) in zip(x, y))
, with the added restriction that the arguments must have equal lengths.
x ⋅ y
(where ⋅
can be typed by tabcompleting \cdot
in the REPL) is a synonym for dot(x, y)
.
Examples
julia> dot([1; 1], [2; 3]) 5 julia> dot([im; im], [1; 1]) 0  2im julia> dot(1:5, 2:6) 70 julia> x = fill(2., (5,5)); julia> y = fill(3., (5,5)); julia> dot(x, y) 150.0source
LinearAlgebra.cross
Function
cross(x, y) ×(x,y)
Compute the cross product of two 3vectors.
Examples
julia> a = [0;1;0] 3element Array{Int64,1}: 0 1 0 julia> b = [0;0;1] 3element Array{Int64,1}: 0 0 1 julia> cross(a,b) 3element Array{Int64,1}: 1 0 0source
LinearAlgebra.factorize
Function
factorize(A)
Compute a convenient factorization of A
, based upon the type of the input matrix. factorize
checks A
to see if it is symmetric/triangular/etc. if A
is passed as a generic matrix. factorize
checks every element of A
to verify/rule out each property. It will shortcircuit as soon as it can rule out symmetry/triangular structure. The return value can be reused for efficient solving of multiple systems. For example: A=factorize(A); x=A\b; y=A\C
.
Properties of A

type of factorization 

Positivedefinite  Cholesky (see cholesky ) 
Dense Symmetric/Hermitian  BunchKaufman (see bunchkaufman ) 
Sparse Symmetric/Hermitian  LDLt (see ldlt ) 
Triangular  Triangular 
Diagonal  Diagonal 
Bidiagonal  Bidiagonal 
Tridiagonal  LU (see lu ) 
Symmetric real tridiagonal  LDLt (see ldlt ) 
General square  LU (see lu ) 
General nonsquare  QR (see qr ) 
If factorize
is called on a Hermitian positivedefinite matrix, for instance, then factorize
will return a Cholesky factorization.
Examples
julia> A = Array(Bidiagonal(fill(1.0, (5, 5)), :U)) 5×5 Array{Float64,2}: 1.0 1.0 0.0 0.0 0.0 0.0 1.0 1.0 0.0 0.0 0.0 0.0 1.0 1.0 0.0 0.0 0.0 0.0 1.0 1.0 0.0 0.0 0.0 0.0 1.0 julia> factorize(A) # factorize will check to see that A is already factorized 5×5 Bidiagonal{Float64,Array{Float64,1}}: 1.0 1.0 ⋅ ⋅ ⋅ ⋅ 1.0 1.0 ⋅ ⋅ ⋅ ⋅ 1.0 1.0 ⋅ ⋅ ⋅ ⋅ 1.0 1.0 ⋅ ⋅ ⋅ ⋅ 1.0
This returns a 5×5 Bidiagonal{Float64}
, which can now be passed to other linear algebra functions (e.g. eigensolvers) which will use specialized methods for Bidiagonal
types.
LinearAlgebra.Diagonal
Type
Diagonal(A::AbstractMatrix)
Construct a matrix from the diagonal of A
.
Examples
julia> A = [1 2 3; 4 5 6; 7 8 9] 3×3 Array{Int64,2}: 1 2 3 4 5 6 7 8 9 julia> Diagonal(A) 3×3 Diagonal{Int64,Array{Int64,1}}: 1 ⋅ ⋅ ⋅ 5 ⋅ ⋅ ⋅ 9source
Diagonal(V::AbstractVector)
Construct a matrix with V
as its diagonal.
Examples
julia> V = [1, 2] 2element Array{Int64,1}: 1 2 julia> Diagonal(V) 2×2 Diagonal{Int64,Array{Int64,1}}: 1 ⋅ ⋅ 2source
LinearAlgebra.Bidiagonal
Type
Bidiagonal(dv::V, ev::V, uplo::Symbol) where V <: AbstractVector
Constructs an upper (uplo=:U
) or lower (uplo=:L
) bidiagonal matrix using the given diagonal (dv
) and offdiagonal (ev
) vectors. The result is of type Bidiagonal
and provides efficient specialized linear solvers, but may be converted into a regular matrix with convert(Array, _)
(or Array(_)
for short). The length of ev
must be one less than the length of dv
.
Examples
julia> dv = [1, 2, 3, 4] 4element Array{Int64,1}: 1 2 3 4 julia> ev = [7, 8, 9] 3element Array{Int64,1}: 7 8 9 julia> Bu = Bidiagonal(dv, ev, :U) # ev is on the first superdiagonal 4×4 Bidiagonal{Int64,Array{Int64,1}}: 1 7 ⋅ ⋅ ⋅ 2 8 ⋅ ⋅ ⋅ 3 9 ⋅ ⋅ ⋅ 4 julia> Bl = Bidiagonal(dv, ev, :L) # ev is on the first subdiagonal 4×4 Bidiagonal{Int64,Array{Int64,1}}: 1 ⋅ ⋅ ⋅ 7 2 ⋅ ⋅ ⋅ 8 3 ⋅ ⋅ ⋅ 9 4source
Bidiagonal(A, uplo::Symbol)
Construct a Bidiagonal
matrix from the main diagonal of A
and its first super (if uplo=:U
) or subdiagonal (if uplo=:L
).
Examples
julia> A = [1 1 1 1; 2 2 2 2; 3 3 3 3; 4 4 4 4] 4×4 Array{Int64,2}: 1 1 1 1 2 2 2 2 3 3 3 3 4 4 4 4 julia> Bidiagonal(A, :U) # contains the main diagonal and first superdiagonal of A 4×4 Bidiagonal{Int64,Array{Int64,1}}: 1 1 ⋅ ⋅ ⋅ 2 2 ⋅ ⋅ ⋅ 3 3 ⋅ ⋅ ⋅ 4 julia> Bidiagonal(A, :L) # contains the main diagonal and first subdiagonal of A 4×4 Bidiagonal{Int64,Array{Int64,1}}: 1 ⋅ ⋅ ⋅ 2 2 ⋅ ⋅ ⋅ 3 3 ⋅ ⋅ ⋅ 4 4source
LinearAlgebra.SymTridiagonal
Type
SymTridiagonal(dv::V, ev::V) where V <: AbstractVector
Construct a symmetric tridiagonal matrix from the diagonal (dv
) and first sub/superdiagonal (ev
), respectively. The result is of type SymTridiagonal
and provides efficient specialized eigensolvers, but may be converted into a regular matrix with convert(Array, _)
(or Array(_)
for short).
Examples
julia> dv = [1, 2, 3, 4] 4element Array{Int64,1}: 1 2 3 4 julia> ev = [7, 8, 9] 3element Array{Int64,1}: 7 8 9 julia> SymTridiagonal(dv, ev) 4×4 SymTridiagonal{Int64,Array{Int64,1}}: 1 7 ⋅ ⋅ 7 2 8 ⋅ ⋅ 8 3 9 ⋅ ⋅ 9 4source
SymTridiagonal(A::AbstractMatrix)
Construct a symmetric tridiagonal matrix from the diagonal and first sub/superdiagonal, of the symmetric matrix A
.
Examples
julia> A = [1 2 3; 2 4 5; 3 5 6] 3×3 Array{Int64,2}: 1 2 3 2 4 5 3 5 6 julia> SymTridiagonal(A) 3×3 SymTridiagonal{Int64,Array{Int64,1}}: 1 2 ⋅ 2 4 5 ⋅ 5 6source
LinearAlgebra.Tridiagonal
Type
Tridiagonal(dl::V, d::V, du::V) where V <: AbstractVector
Construct a tridiagonal matrix from the first subdiagonal, diagonal, and first superdiagonal, respectively. The result is of type Tridiagonal
and provides efficient specialized linear solvers, but may be converted into a regular matrix with convert(Array, _)
(or Array(_)
for short). The lengths of dl
and du
must be one less than the length of d
.
Examples
julia> dl = [1, 2, 3]; julia> du = [4, 5, 6]; julia> d = [7, 8, 9, 0]; julia> Tridiagonal(dl, d, du) 4×4 Tridiagonal{Int64,Array{Int64,1}}: 7 4 ⋅ ⋅ 1 8 5 ⋅ ⋅ 2 9 6 ⋅ ⋅ 3 0source
Tridiagonal(A)
Construct a tridiagonal matrix from the first subdiagonal, diagonal and first superdiagonal of the matrix A
.
Examples
julia> A = [1 2 3 4; 1 2 3 4; 1 2 3 4; 1 2 3 4] 4×4 Array{Int64,2}: 1 2 3 4 1 2 3 4 1 2 3 4 1 2 3 4 julia> Tridiagonal(A) 4×4 Tridiagonal{Int64,Array{Int64,1}}: 1 2 ⋅ ⋅ 1 2 3 ⋅ ⋅ 2 3 4 ⋅ ⋅ 3 4source
LinearAlgebra.Symmetric
Type
Symmetric(A, uplo=:U)
Construct a Symmetric
view of the upper (if uplo = :U
) or lower (if uplo = :L
) triangle of the matrix A
.
Examples
julia> A = [1 0 2 0 3; 0 4 0 5 0; 6 0 7 0 8; 0 9 0 1 0; 2 0 3 0 4] 5×5 Array{Int64,2}: 1 0 2 0 3 0 4 0 5 0 6 0 7 0 8 0 9 0 1 0 2 0 3 0 4 julia> Supper = Symmetric(A) 5×5 Symmetric{Int64,Array{Int64,2}}: 1 0 2 0 3 0 4 0 5 0 2 0 7 0 8 0 5 0 1 0 3 0 8 0 4 julia> Slower = Symmetric(A, :L) 5×5 Symmetric{Int64,Array{Int64,2}}: 1 0 6 0 2 0 4 0 9 0 6 0 7 0 3 0 9 0 1 0 2 0 3 0 4
Note that Supper
will not be equal to Slower
unless A
is itself symmetric (e.g. if A == transpose(A)
).
LinearAlgebra.Hermitian
Type
Hermitian(A, uplo=:U)
Construct a Hermitian
view of the upper (if uplo = :U
) or lower (if uplo = :L
) triangle of the matrix A
.
Examples
julia> A = [1 0 2+2im 0 33im; 0 4 0 5 0; 66im 0 7 0 8+8im; 0 9 0 1 0; 2+2im 0 33im 0 4]; julia> Hupper = Hermitian(A) 5×5 Hermitian{Complex{Int64},Array{Complex{Int64},2}}: 1+0im 0+0im 2+2im 0+0im 33im 0+0im 4+0im 0+0im 5+0im 0+0im 22im 0+0im 7+0im 0+0im 8+8im 0+0im 5+0im 0+0im 1+0im 0+0im 3+3im 0+0im 88im 0+0im 4+0im julia> Hlower = Hermitian(A, :L) 5×5 Hermitian{Complex{Int64},Array{Complex{Int64},2}}: 1+0im 0+0im 6+6im 0+0im 22im 0+0im 4+0im 0+0im 9+0im 0+0im 66im 0+0im 7+0im 0+0im 3+3im 0+0im 9+0im 0+0im 1+0im 0+0im 2+2im 0+0im 33im 0+0im 4+0im
Note that Hupper
will not be equal to Hlower
unless A
is itself Hermitian (e.g. if A == adjoint(A)
).
All nonreal parts of the diagonal will be ignored.
Hermitian(fill(complex(1,1), 1, 1)) == fill(1, 1, 1)source
LinearAlgebra.LowerTriangular
Type
LowerTriangular(A::AbstractMatrix)
Construct a LowerTriangular
view of the matrix A
.
Examples
julia> A = [1.0 2.0 3.0; 4.0 5.0 6.0; 7.0 8.0 9.0] 3×3 Array{Float64,2}: 1.0 2.0 3.0 4.0 5.0 6.0 7.0 8.0 9.0 julia> LowerTriangular(A) 3×3 LowerTriangular{Float64,Array{Float64,2}}: 1.0 ⋅ ⋅ 4.0 5.0 ⋅ 7.0 8.0 9.0source
LinearAlgebra.UpperTriangular
Type
UpperTriangular(A::AbstractMatrix)
Construct an UpperTriangular
view of the matrix A
.
Examples
julia> A = [1.0 2.0 3.0; 4.0 5.0 6.0; 7.0 8.0 9.0] 3×3 Array{Float64,2}: 1.0 2.0 3.0 4.0 5.0 6.0 7.0 8.0 9.0 julia> UpperTriangular(A) 3×3 UpperTriangular{Float64,Array{Float64,2}}: 1.0 2.0 3.0 ⋅ 5.0 6.0 ⋅ ⋅ 9.0source
LinearAlgebra.UnitLowerTriangular
Type
UnitLowerTriangular(A::AbstractMatrix)
Construct a UnitLowerTriangular
view of the matrix A
. Such a view has the oneunit
of the eltype
of A
on its diagonal.
Examples
julia> A = [1.0 2.0 3.0; 4.0 5.0 6.0; 7.0 8.0 9.0] 3×3 Array{Float64,2}: 1.0 2.0 3.0 4.0 5.0 6.0 7.0 8.0 9.0 julia> UnitLowerTriangular(A) 3×3 UnitLowerTriangular{Float64,Array{Float64,2}}: 1.0 ⋅ ⋅ 4.0 1.0 ⋅ 7.0 8.0 1.0source
LinearAlgebra.UnitUpperTriangular
Type
UnitUpperTriangular(A::AbstractMatrix)
Construct an UnitUpperTriangular
view of the matrix A
. Such a view has the oneunit
of the eltype
of A
on its diagonal.
Examples
julia> A = [1.0 2.0 3.0; 4.0 5.0 6.0; 7.0 8.0 9.0] 3×3 Array{Float64,2}: 1.0 2.0 3.0 4.0 5.0 6.0 7.0 8.0 9.0 julia> UnitUpperTriangular(A) 3×3 UnitUpperTriangular{Float64,Array{Float64,2}}: 1.0 2.0 3.0 ⋅ 1.0 6.0 ⋅ ⋅ 1.0source
LinearAlgebra.UpperHessenberg
Type
UpperHessenberg(A::AbstractMatrix)
Construct an UpperHessenberg
view of the matrix A
. Entries of A
below the first subdiagonal are ignored.
Efficient algorithms are implemented for H \ b
, det(H)
, and similar.
See also the hessenberg
function to factor any matrix into a similar upperHessenberg matrix.
If F::Hessenberg
is the factorization object, the unitary matrix can be accessed with F.Q
and the Hessenberg matrix with F.H
. When Q
is extracted, the resulting type is the HessenbergQ
object, and may be converted to a regular matrix with convert(Array, _)
(or Array(_)
for short).
Iterating the decomposition produces the factors F.Q
and F.H
.
Examples
julia> A = [1 2 3 4; 5 6 7 8; 9 10 11 12; 13 14 15 16] 4×4 Array{Int64,2}: 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 julia> UpperHessenberg(A) 4×4 UpperHessenberg{Int64,Array{Int64,2}}: 1 2 3 4 5 6 7 8 ⋅ 10 11 12 ⋅ ⋅ 15 16source
LinearAlgebra.UniformScaling
Type
UniformScaling{T<:Number}
Generically sized uniform scaling operator defined as a scalar times the identity operator, λ*I
. See also I
.
Examples
julia> J = UniformScaling(2.) UniformScaling{Float64} 2.0*I julia> A = [1. 2.; 3. 4.] 2×2 Array{Float64,2}: 1.0 2.0 3.0 4.0 julia> J*A 2×2 Array{Float64,2}: 2.0 4.0 6.0 8.0source
LinearAlgebra.Factorization
Type
LinearAlgebra.Factorization
Abstract type for matrix factorizations a.k.a. matrix decompositions. See online documentation for a list of available matrix factorizations.
source
LinearAlgebra.LU
Type
LU <: Factorization
Matrix factorization type of the LU
factorization of a square matrix A
. This is the return type of lu
, the corresponding matrix factorization function.
The individual components of the factorization F::LU
can be accessed via getproperty
:
Component  Description 

F.L 
L (unit lower triangular) part of LU

F.U 
U (upper triangular) part of LU

F.p 
(right) permutation Vector

F.P 
(right) permutation Matrix

Iterating the factorization produces the components F.L
, F.U
, and F.p
.
Examples
julia> A = [4 3; 6 3] 2×2 Array{Int64,2}: 4 3 6 3 julia> F = lu(A) LU{Float64,Array{Float64,2}} L factor: 2×2 Array{Float64,2}: 1.0 0.0 0.666667 1.0 U factor: 2×2 Array{Float64,2}: 6.0 3.0 0.0 1.0 julia> F.L * F.U == A[F.p, :] true julia> l, u, p = lu(A); # destructuring via iteration julia> l == F.L && u == F.U && p == F.p truesource
LinearAlgebra.lu
Function
lu(A, pivot=Val(true); check = true) > F::LU
Compute the LU factorization of A
.
When check = true
, an error is thrown if the decomposition fails. When check = false
, responsibility for checking the decomposition's validity (via issuccess
) lies with the user.
In most cases, if A
is a subtype S
of AbstractMatrix{T}
with an element type T
supporting +
, 
, *
and /
, the return type is LU{T,S{T}}
. If pivoting is chosen (default) the element type should also support abs
and <
.
The individual components of the factorization F
can be accessed via getproperty
:
Component  Description 

F.L 
L (lower triangular) part of LU

F.U 
U (upper triangular) part of LU

F.p 
(right) permutation Vector

F.P 
(right) permutation Matrix

Iterating the factorization produces the components F.L
, F.U
, and F.p
.
The relationship between F
and A
is
F.L*F.U == A[F.p, :]
F
further supports the following functions:
Supported function  LU 
LU{T,Tridiagonal{T}} 

/ 
✓  
\ 
✓  ✓ 
inv 
✓  ✓ 
det 
✓  ✓ 
logdet 
✓  ✓ 
logabsdet 
✓  ✓ 
size 
✓  ✓ 
Examples
julia> A = [4 3; 6 3] 2×2 Array{Int64,2}: 4 3 6 3 julia> F = lu(A) LU{Float64,Array{Float64,2}} L factor: 2×2 Array{Float64,2}: 1.0 0.0 0.666667 1.0 U factor: 2×2 Array{Float64,2}: 6.0 3.0 0.0 1.0 julia> F.L * F.U == A[F.p, :] true julia> l, u, p = lu(A); # destructuring via iteration julia> l == F.L && u == F.U && p == F.p truesource
LinearAlgebra.lu!
Function
lu!(A, pivot=Val(true); check = true) > LU
lu!
is the same as lu
, but saves space by overwriting the input A
, instead of creating a copy. An InexactError
exception is thrown if the factorization produces a number not representable by the element type of A
, e.g. for integer types.
Examples
julia> A = [4. 3.; 6. 3.] 2×2 Array{Float64,2}: 4.0 3.0 6.0 3.0 julia> F = lu!(A) LU{Float64,Array{Float64,2}} L factor: 2×2 Array{Float64,2}: 1.0 0.0 0.666667 1.0 U factor: 2×2 Array{Float64,2}: 6.0 3.0 0.0 1.0 julia> iA = [4 3; 6 3] 2×2 Array{Int64,2}: 4 3 6 3 julia> lu!(iA) ERROR: InexactError: Int64(0.6666666666666666) Stacktrace: [...]source
LinearAlgebra.Cholesky
Type
Cholesky <: Factorization
Matrix factorization type of the Cholesky factorization of a dense symmetric/Hermitian positive definite matrix A
. This is the return type of cholesky
, the corresponding matrix factorization function.
The triangular Cholesky factor can be obtained from the factorization F::Cholesky
via F.L
and F.U
.
Examples
julia> A = [4. 12. 16.; 12. 37. 43.; 16. 43. 98.] 3×3 Array{Float64,2}: 4.0 12.0 16.0 12.0 37.0 43.0 16.0 43.0 98.0 julia> C = cholesky(A) Cholesky{Float64,Array{Float64,2}} U factor: 3×3 UpperTriangular{Float64,Array{Float64,2}}: 2.0 6.0 8.0 ⋅ 1.0 5.0 ⋅ ⋅ 3.0 julia> C.U 3×3 UpperTriangular{Float64,Array{Float64,2}}: 2.0 6.0 8.0 ⋅ 1.0 5.0 ⋅ ⋅ 3.0 julia> C.L 3×3 LowerTriangular{Float64,Array{Float64,2}}: 2.0 ⋅ ⋅ 6.0 1.0 ⋅ 8.0 5.0 3.0 julia> C.L * C.U == A truesource
LinearAlgebra.CholeskyPivoted
Type
CholeskyPivoted
Matrix factorization type of the pivoted Cholesky factorization of a dense symmetric/Hermitian positive semidefinite matrix A
. This is the return type of cholesky(_, Val(true))
, the corresponding matrix factorization function.
The triangular Cholesky factor can be obtained from the factorization F::CholeskyPivoted
via F.L
and F.U
.
Examples
julia> A = [4. 12. 16.; 12. 37. 43.; 16. 43. 98.] 3×3 Array{Float64,2}: 4.0 12.0 16.0 12.0 37.0 43.0 16.0 43.0 98.0 julia> C = cholesky(A, Val(true)) CholeskyPivoted{Float64,Array{Float64,2}} U factor with rank 3: 3×3 UpperTriangular{Float64,Array{Float64,2}}: 9.89949 4.34366 1.61624 ⋅ 4.25825 1.1694 ⋅ ⋅ 0.142334 permutation: 3element Array{Int64,1}: 3 2 1source
LinearAlgebra.cholesky
Function
cholesky(A, Val(false); check = true) > Cholesky
Compute the Cholesky factorization of a dense symmetric positive definite matrix A
and return a Cholesky
factorization. The matrix A
can either be a Symmetric
or Hermitian
StridedMatrix
or a perfectly symmetric or Hermitian StridedMatrix
. The triangular Cholesky factor can be obtained from the factorization F
with: F.L
and F.U
. The following functions are available for Cholesky
objects: size
, \
, inv
, det
, logdet
and isposdef
.
When check = true
, an error is thrown if the decomposition fails. When check = false
, responsibility for checking the decomposition's validity (via issuccess
) lies with the user.
Examples
julia> A = [4. 12. 16.; 12. 37. 43.; 16. 43. 98.] 3×3 Array{Float64,2}: 4.0 12.0 16.0 12.0 37.0 43.0 16.0 43.0 98.0 julia> C = cholesky(A) Cholesky{Float64,Array{Float64,2}} U factor: 3×3 UpperTriangular{Float64,Array{Float64,2}}: 2.0 6.0 8.0 ⋅ 1.0 5.0 ⋅ ⋅ 3.0 julia> C.U 3×3 UpperTriangular{Float64,Array{Float64,2}}: 2.0 6.0 8.0 ⋅ 1.0 5.0 ⋅ ⋅ 3.0 julia> C.L 3×3 LowerTriangular{Float64,Array{Float64,2}}: 2.0 ⋅ ⋅ 6.0 1.0 ⋅ 8.0 5.0 3.0 julia> C.L * C.U == A truesource
cholesky(A, Val(true); tol = 0.0, check = true) > CholeskyPivoted
Compute the pivoted Cholesky factorization of a dense symmetric positive semidefinite matrix A
and return a CholeskyPivoted
factorization. The matrix A
can either be a Symmetric
or Hermitian
StridedMatrix
or a perfectly symmetric or Hermitian StridedMatrix
. The triangular Cholesky factor can be obtained from the factorization F
with: F.L
and F.U
. The following functions are available for CholeskyPivoted
objects: size
, \
, inv
, det
, and rank
. The argument tol
determines the tolerance for determining the rank. For negative values, the tolerance is the machine precision.
When check = true
, an error is thrown if the decomposition fails. When check = false
, responsibility for checking the decomposition's validity (via issuccess
) lies with the user.
LinearAlgebra.cholesky!
Function
cholesky!(A, Val(false); check = true) > Cholesky
The same as cholesky
, but saves space by overwriting the input A
, instead of creating a copy. An InexactError
exception is thrown if the factorization produces a number not representable by the element type of A
, e.g. for integer types.
Examples
julia> A = [1 2; 2 50] 2×2 Array{Int64,2}: 1 2 2 50 julia> cholesky!(A) ERROR: InexactError: Int64(6.782329983125268) Stacktrace: [...]source
cholesky!(A, Val(true); tol = 0.0, check = true) > CholeskyPivoted
The same as cholesky
, but saves space by overwriting the input A
, instead of creating a copy. An InexactError
exception is thrown if the factorization produces a number not representable by the element type of A
, e.g. for integer types.
LinearAlgebra.lowrankupdate
Function
lowrankupdate(C::Cholesky, v::StridedVector) > CC::Cholesky
Update a Cholesky factorization C
with the vector v
. If A = C.U'C.U
then CC = cholesky(C.U'C.U + v*v')
but the computation of CC
only uses O(n^2)
operations.
LinearAlgebra.lowrankdowndate
Function
lowrankdowndate(C::Cholesky, v::StridedVector) > CC::Cholesky
Downdate a Cholesky factorization C
with the vector v
. If A = C.U'C.U
then CC = cholesky(C.U'C.U  v*v')
but the computation of CC
only uses O(n^2)
operations.
LinearAlgebra.lowrankupdate!
Function
lowrankupdate!(C::Cholesky, v::StridedVector) > CC::Cholesky
Update a Cholesky factorization C
with the vector v
. If A = C.U'C.U
then CC = cholesky(C.U'C.U + v*v')
but the computation of CC
only uses O(n^2)
operations. The input factorization C
is updated in place such that on exit C == CC
. The vector v
is destroyed during the computation.
LinearAlgebra.lowrankdowndate!
Function
lowrankdowndate!(C::Cholesky, v::StridedVector) > CC::Cholesky
Downdate a Cholesky factorization C
with the vector v
. If A = C.U'C.U
then CC = cholesky(C.U'C.U  v*v')
but the computation of CC
only uses O(n^2)
operations. The input factorization C
is updated in place such that on exit C == CC
. The vector v
is destroyed during the computation.
LinearAlgebra.LDLt
Type
LDLt <: Factorization
Matrix factorization type of the LDLt
factorization of a real SymTridiagonal
matrix S
such that S = L*Diagonal(d)*L'
, where L
is a UnitLowerTriangular
matrix and d
is a vector. The main use of an LDLt
factorization F = ldlt(S)
is to solve the linear system of equations Sx = b
with F\b
. This is the return type of ldlt
, the corresponding matrix factorization function.
Examples
julia> S = SymTridiagonal([3., 4., 5.], [1., 2.]) 3×3 SymTridiagonal{Float64,Array{Float64,1}}: 3.0 1.0 ⋅ 1.0 4.0 2.0 ⋅ 2.0 5.0 julia> F = ldlt(S) LDLt{Float64,SymTridiagonal{Float64,Array{Float64,1}}}([3.0 0.3333333333333333 0.0; 0.3333333333333333 3.6666666666666665 0.5454545454545455; 0.0 0.5454545454545455 3.909090909090909])source
LinearAlgebra.ldlt
Function
ldlt(S::SymTridiagonal) > LDLt
Compute an LDLt
factorization of the real symmetric tridiagonal matrix S
such that S = L*Diagonal(d)*L'
where L
is a unit lower triangular matrix and d
is a vector. The main use of an LDLt
factorization F = ldlt(S)
is to solve the linear system of equations Sx = b
with F\b
.
Examples
julia> S = SymTridiagonal([3., 4., 5.], [1., 2.]) 3×3 SymTridiagonal{Float64,Array{Float64,1}}: 3.0 1.0 ⋅ 1.0 4.0 2.0 ⋅ 2.0 5.0 julia> ldltS = ldlt(S); julia> b = [6., 7., 8.]; julia> ldltS \ b 3element Array{Float64,1}: 1.7906976744186047 0.627906976744186 1.3488372093023255 julia> S \ b 3element Array{Float64,1}: 1.7906976744186047 0.627906976744186 1.3488372093023255source
LinearAlgebra.ldlt!
Function
ldlt!(S::SymTridiagonal) > LDLt
Same as ldlt
, but saves space by overwriting the input S
, instead of creating a copy.
Examples
julia> S = SymTridiagonal([3., 4., 5.], [1., 2.]) 3×3 SymTridiagonal{Float64,Array{Float64,1}}: 3.0 1.0 ⋅ 1.0 4.0 2.0 ⋅ 2.0 5.0 julia> ldltS = ldlt!(S); julia> ldltS === S false julia> S 3×3 SymTridiagonal{Float64,Array{Float64,1}}: 3.0 0.333333 ⋅ 0.333333 3.66667 0.545455 ⋅ 0.545455 3.90909source
LinearAlgebra.QR
Type
QR <: Factorization
A QR matrix factorization stored in a packed format, typically obtained from qr
. If $A$ is an m
×n
matrix, then
where $Q$ is an orthogonal/unitary matrix and $R$ is upper triangular. The matrix $Q$ is stored as a sequence of Householder reflectors $v_i$ and coefficients $\tau_i$ where:
\[Q = \prod_{i=1}^{\min(m,n)} (I  \tau_i v_i v_i^T).\]Iterating the decomposition produces the components Q
and R
.
The object has two fields:

factors
is anm
×n
matrix.The upper triangular part contains the elements of $R$, that is
R = triu(F.factors)
for aQR
objectF
.The subdiagonal part contains the reflectors $v_i$ stored in a packed format where $v_i$ is the $i$th column of the matrix
V = I + tril(F.factors, 1)
.
τ
is a vector of lengthmin(m,n)
containing the coefficients $au_i$.
LinearAlgebra.QRCompactWY
Type
QRCompactWY <: Factorization
A QR matrix factorization stored in a compact blocked format, typically obtained from qr
. If $A$ is an m
×n
matrix, then
where $Q$ is an orthogonal/unitary matrix and $R$ is upper triangular. It is similar to the QR
format except that the orthogonal/unitary matrix $Q$ is stored in Compact WY format [Schreiber1989], as a lower trapezoidal matrix $V$ and an upper triangular matrix $T$ where
such that $v_i$ is the $i$th column of $V$, and $au_i$ is the $i$th diagonal element of $T$.
Iterating the decomposition produces the components Q
and R
.
The object has two fields:

factors
, as in theQR
type, is anm
×n
matrix.The upper triangular part contains the elements of $R$, that is
R = triu(F.factors)
for aQR
objectF
.The subdiagonal part contains the reflectors $v_i$ stored in a packed format such that
V = I + tril(F.factors, 1)
.
T
is a square matrix withmin(m,n)
columns, whose upper triangular part gives the matrix $T$ above (the subdiagonal elements are ignored).
This format should not to be confused with the older WY representation [Bischof1987].
C Bischof and C Van Loan, "The WY representation for products of Householder matrices", SIAM J Sci Stat Comput 8 (1987), s2s13. doi:10.1137/0908009
R Schreiber and C Van Loan, "A storageefficient WY representation for products of Householder transformations", SIAM J Sci Stat Comput 10 (1989), 5357. doi:10.1137/0910005
LinearAlgebra.QRPivoted
Type
QRPivoted <: Factorization
A QR matrix factorization with column pivoting in a packed format, typically obtained from qr
. If $A$ is an m
×n
matrix, then
where $P$ is a permutation matrix, $Q$ is an orthogonal/unitary matrix and $R$ is upper triangular. The matrix $Q$ is stored as a sequence of Householder reflectors:
\[Q = \prod_{i=1}^{\min(m,n)} (I  \tau_i v_i v_i^T).\]Iterating the decomposition produces the components Q
, R
, and p
.
The object has three fields:

factors
is anm
×n
matrix.The upper triangular part contains the elements of $R$, that is
R = triu(F.factors)
for aQR
objectF
.The subdiagonal part contains the reflectors $v_i$ stored in a packed format where $v_i$ is the $i$th column of the matrix
V = I + tril(F.factors, 1)
.
τ
is a vector of lengthmin(m,n)
containing the coefficients $au_i$.jpvt
is an integer vector of lengthn
corresponding to the permutation $P$.
LinearAlgebra.qr
Function
qr(A, pivot=Val(false)) > F
Compute the QR factorization of the matrix A
: an orthogonal (or unitary if A
is complexvalued) matrix Q
, and an upper triangular matrix R
such that
The returned object F
stores the factorization in a packed format:
if
pivot == Val(true)
thenF
is aQRPivoted
object,otherwise if the element type of
A
is a BLAS type (Float32
,Float64
,ComplexF32
orComplexF64
), thenF
is aQRCompactWY
object,otherwise
F
is aQR
object.
The individual components of the decomposition F
can be retrieved via property accessors:

F.Q
: the orthogonal/unitary matrixQ

F.R
: the upper triangular matrixR

F.p
: the permutation vector of the pivot (QRPivoted
only) 
F.P
: the permutation matrix of the pivot (QRPivoted
only)
Iterating the decomposition produces the components Q
, R
, and if extant p
.
The following functions are available for the QR
objects: inv
, size
, and \
. When A
is rectangular, \
will return a least squares solution and if the solution is not unique, the one with smallest norm is returned. When A
is not full rank, factorization with (column) pivoting is required to obtain a minimum norm solution.
Multiplication with respect to either full/square or nonfull/square Q
is allowed, i.e. both F.Q*F.R
and F.Q*A
are supported. A Q
matrix can be converted into a regular matrix with Matrix
. This operation returns the "thin" Q factor, i.e., if A
is m
×n
with m>=n
, then Matrix(F.Q)
yields an m
×n
matrix with orthonormal columns. To retrieve the "full" Q factor, an m
×m
orthogonal matrix, use F.Q*Matrix(I,m,m)
. If m<=n
, then Matrix(F.Q)
yields an m
×m
orthogonal matrix.
Examples
julia> A = [3.0 6.0; 4.0 8.0; 0.0 1.0] 3×2 Array{Float64,2}: 3.0 6.0 4.0 8.0 0.0 1.0 julia> F = qr(A) LinearAlgebra.QRCompactWY{Float64,Array{Float64,2}} Q factor: 3×3 LinearAlgebra.QRCompactWYQ{Float64,Array{Float64,2}}: 0.6 0.0 0.8 0.8 0.0 0.6 0.0 1.0 0.0 R factor: 2×2 Array{Float64,2}: 5.0 10.0 0.0 1.0 julia> F.Q * F.R == A true
qr
returns multiple types because LAPACK uses several representations that minimize the memory storage requirements of products of Householder elementary reflectors, so that the Q
and R
matrices can be stored compactly rather as two separate dense matrices.
LinearAlgebra.qr!
Function
qr!(A, pivot=Val(false))
qr!
is the same as qr
when A
is a subtype of StridedMatrix
, but saves space by overwriting the input A
, instead of creating a copy. An InexactError
exception is thrown if the factorization produces a number not representable by the element type of A
, e.g. for integer types.
Examples
julia> a = [1. 2.; 3. 4.] 2×2 Array{Float64,2}: 1.0 2.0 3.0 4.0 julia> qr!(a) LinearAlgebra.QRCompactWY{Float64,Array{Float64,2}} Q factor: 2×2 LinearAlgebra.QRCompactWYQ{Float64,Array{Float64,2}}: 0.316228 0.948683 0.948683 0.316228 R factor: 2×2 Array{Float64,2}: 3.16228 4.42719 0.0 0.632456 julia> a = [1 2; 3 4] 2×2 Array{Int64,2}: 1 2 3 4 julia> qr!(a) ERROR: InexactError: Int64(3.1622776601683795) Stacktrace: [...]source
LinearAlgebra.LQ
Type
LQ <: Factorization
Matrix factorization type of the LQ
factorization of a matrix A
. The LQ
decomposition is the QR
decomposition of transpose(A)
. This is the return type of lq
, the corresponding matrix factorization function.
If S::LQ
is the factorization object, the lower triangular component can be obtained via S.L
, and the orthogonal/unitary component via S.Q
, such that A ≈ S.L*S.Q
.
Iterating the decomposition produces the components S.L
and S.Q
.
Examples
```jldoctest julia> A = [5. 7.; 2. 4.] 2×2 Array{Float64,2}: 5.0 7.0 2.0 4.0
julia> S = lq(A) LQ{Float64,Array{Float64,2}} with factors L and Q: [8.60233 0.0; 4.41741 0.697486] [0.581238 0.813733; 0.813733 0.581238]
julia> S.L * S.Q 2×2 Array{Float64,2}: 5.0 7.0 2.0 4.0
julia> l, q = S; # destructuring via iteration
julia> l == S.L && q == S.Q true
source
LinearAlgebra.lq
Function
lq(A) > S::LQ
Compute the LQ decomposition of A
. The decomposition's lower triangular component can be obtained from the LQ
object S
via S.L
, and the orthogonal/unitary component via S.Q
, such that A ≈ S.L*S.Q
.
Iterating the decomposition produces the components S.L
and S.Q
.
The LQ decomposition is the QR decomposition of transpose(A)
.
Examples
julia> A = [5. 7.; 2. 4.] 2×2 Array{Float64,2}: 5.0 7.0 2.0 4.0 julia> S = lq(A) LQ{Float64,Array{Float64,2}} with factors L and Q: [8.60233 0.0; 4.41741 0.697486] [0.581238 0.813733; 0.813733 0.581238] julia> S.L * S.Q 2×2 Array{Float64,2}: 5.0 7.0 2.0 4.0 julia> l, q = S; # destructuring via iteration julia> l == S.L && q == S.Q truesource
LinearAlgebra.lq!
Function
lq!(A) > LQ
Compute the LQ factorization of A
, using the input matrix as a workspace. See also lq
.
LinearAlgebra.BunchKaufman
Type
BunchKaufman <: Factorization
Matrix factorization type of the BunchKaufman factorization of a symmetric or Hermitian matrix A
as P'UDU'P
or P'LDL'P
, depending on whether the upper (the default) or the lower triangle is stored in A
. If A
is complex symmetric then U'
and L'
denote the unconjugated transposes, i.e. transpose(U)
and transpose(L)
, respectively. This is the return type of bunchkaufman
, the corresponding matrix factorization function.
If S::BunchKaufman
is the factorization object, the components can be obtained via S.D
, S.U
or S.L
as appropriate given S.uplo
, and S.p
.
Iterating the decomposition produces the components S.D
, S.U
or S.L
as appropriate given S.uplo
, and S.p
.
Examples
julia> A = [1 2; 2 3] 2×2 Array{Int64,2}: 1 2 2 3 julia> S = bunchkaufman(A) # A gets wrapped internally by Symmetric(A) BunchKaufman{Float64,Array{Float64,2}} D factor: 2×2 Tridiagonal{Float64,Array{Float64,1}}: 0.333333 0.0 0.0 3.0 U factor: 2×2 UnitUpperTriangular{Float64,Array{Float64,2}}: 1.0 0.666667 ⋅ 1.0 permutation: 2element Array{Int64,1}: 1 2 julia> d, u, p = S; # destructuring via iteration julia> d == S.D && u == S.U && p == S.p true julia> S = bunchkaufman(Symmetric(A, :L)) BunchKaufman{Float64,Array{Float64,2}} D factor: 2×2 Tridiagonal{Float64,Array{Float64,1}}: 3.0 0.0 0.0 0.333333 L factor: 2×2 UnitLowerTriangular{Float64,Array{Float64,2}}: 1.0 ⋅ 0.666667 1.0 permutation: 2element Array{Int64,1}: 2 1source
LinearAlgebra.bunchkaufman
Function
bunchkaufman(A, rook::Bool=false; check = true) > S::BunchKaufman
Compute the BunchKaufman [Bunch1977] factorization of a symmetric or Hermitian matrix A
as P'*U*D*U'*P
or P'*L*D*L'*P
, depending on which triangle is stored in A
, and return a BunchKaufman
object. Note that if A
is complex symmetric then U'
and L'
denote the unconjugated transposes, i.e. transpose(U)
and transpose(L)
.
Iterating the decomposition produces the components S.D
, S.U
or S.L
as appropriate given S.uplo
, and S.p
.
If rook
is true
, rook pivoting is used. If rook
is false, rook pivoting is not used.
When check = true
, an error is thrown if the decomposition fails. When check = false
, responsibility for checking the decomposition's validity (via issuccess
) lies with the user.
The following functions are available for BunchKaufman
objects: size
, \
, inv
, issymmetric
, ishermitian
, getindex
.
J R Bunch and L Kaufman, Some stable methods for calculating inertia and solving symmetric linear systems, Mathematics of Computation 31:137 (1977), 163179. url.
Examples
julia> A = [1 2; 2 3] 2×2 Array{Int64,2}: 1 2 2 3 julia> S = bunchkaufman(A) # A gets wrapped internally by Symmetric(A) BunchKaufman{Float64,Array{Float64,2}} D factor: 2×2 Tridiagonal{Float64,Array{Float64,1}}: 0.333333 0.0 0.0 3.0 U factor: 2×2 UnitUpperTriangular{Float64,Array{Float64,2}}: 1.0 0.666667 ⋅ 1.0 permutation: 2element Array{Int64,1}: 1 2 julia> d, u, p = S; # destructuring via iteration julia> d == S.D && u == S.U && p == S.p true julia> S = bunchkaufman(Symmetric(A, :L)) BunchKaufman{Float64,Array{Float64,2}} D factor: 2×2 Tridiagonal{Float64,Array{Float64,1}}: 3.0 0.0 0.0 0.333333 L factor: 2×2 UnitLowerTriangular{Float64,Array{Float64,2}}: 1.0 ⋅ 0.666667 1.0 permutation: 2element Array{Int64,1}: 2 1source
LinearAlgebra.bunchkaufman!
Function
bunchkaufman!(A, rook::Bool=false; check = true) > BunchKaufman
bunchkaufman!
is the same as bunchkaufman
, but saves space by overwriting the input A
, instead of creating a copy.
LinearAlgebra.Eigen
Type
Eigen <: Factorization
Matrix factorization type of the eigenvalue/spectral decomposition of a square matrix A
. This is the return type of eigen
, the corresponding matrix factorization function.
If F::Eigen
is the factorization object, the eigenvalues can be obtained via F.values
and the eigenvectors as the columns of the matrix F.vectors
. (The k
th eigenvector can be obtained from the slice F.vectors[:, k]
.)
Iterating the decomposition produces the components F.values
and F.vectors
.
Examples
julia> F = eigen([1.0 0.0 0.0; 0.0 3.0 0.0; 0.0 0.0 18.0]) Eigen{Float64,Float64,Array{Float64,2},Array{Float64,1}} eigenvalues: 3element Array{Float64,1}: 1.0 3.0 18.0 eigenvectors: 3×3 Array{Float64,2}: 1.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 1.0 julia> F.values 3element Array{Float64,1}: 1.0 3.0 18.0 julia> F.vectors 3×3 Array{Float64,2}: 1.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 1.0 julia> vals, vecs = F; # destructuring via iteration julia> vals == F.values && vecs == F.vectors truesource
LinearAlgebra.GeneralizedEigen
Type
GeneralizedEigen <: Factorization
Matrix factorization type of the generalized eigenvalue/spectral decomposition of A
and B
. This is the return type of eigen
, the corresponding matrix factorization function, when called with two matrix arguments.
If F::GeneralizedEigen
is the factorization object, the eigenvalues can be obtained via F.values
and the eigenvectors as the columns of the matrix F.vectors
. (The k
th eigenvector can be obtained from the slice F.vectors[:, k]
.)
Iterating the decomposition produces the components F.values
and F.vectors
.
Examples
julia> A = [1 0; 0 1] 2×2 Array{Int64,2}: 1 0 0 1 julia> B = [0 1; 1 0] 2×2 Array{Int64,2}: 0 1 1 0 julia> F = eigen(A, B) GeneralizedEigen{Complex{Float64},Complex{Float64},Array{Complex{Float64},2},Array{Complex{Float64},1}} eigenvalues: 2element Array{Complex{Float64},1}: 0.0  1.0im 0.0 + 1.0im eigenvectors: 2×2 Array{Complex{Float64},2}: 0.0+1.0im 0.01.0im 1.0+0.0im 1.00.0im julia> F.values 2element Array{Complex{Float64},1}: 0.0  1.0im 0.0 + 1.0im julia> F.vectors 2×2 Array{Complex{Float64},2}: 0.0+1.0im 0.01.0im 1.0+0.0im 1.00.0im julia> vals, vecs = F; # destructuring via iteration julia> vals == F.values && vecs == F.vectors truesource
LinearAlgebra.eigvals
Function
eigvals(A; permute::Bool=true, scale::Bool=true, sortby) > values
Return the eigenvalues of A
.
For general nonsymmetric matrices it is possible to specify how the matrix is balanced before the eigenvalue calculation. The permute
, scale
, and sortby
keywords are the same as for eigen!
.
Examples
julia> diag_matrix = [1 0; 0 4] 2×2 Array{Int64,2}: 1 0 0 4 julia> eigvals(diag_matrix) 2element Array{Float64,1}: 1.0 4.0source
For a scalar input, eigvals
will return a scalar.
Example
julia> eigvals(2) 2source
eigvals(A, B) > values
Computes the generalized eigenvalues of A
and B
.
Examples
julia> A = [1 0; 0 1] 2×2 Array{Int64,2}: 1 0 0 1 julia> B = [0 1; 1 0] 2×2 Array{Int64,2}: 0 1 1 0 julia> eigvals(A,B) 2element Array{Complex{Float64},1}: 0.0  1.0im 0.0 + 1.0imsource
eigvals(A::Union{SymTridiagonal, Hermitian, Symmetric}, irange::UnitRange) > values
Returns the eigenvalues of A
. It is possible to calculate only a subset of the eigenvalues by specifying a UnitRange
irange
covering indices of the sorted eigenvalues, e.g. the 2nd to 8th eigenvalues.
julia> A = SymTridiagonal([1.; 2.; 1.], [2.; 3.]) 3×3 SymTridiagonal{Float64,Array{Float64,1}}: 1.0 2.0 ⋅ 2.0 2.0 3.0 ⋅ 3.0 1.0 julia> eigvals(A, 2:2) 1element Array{Float64,1}: 0.9999999999999996 julia> eigvals(A) 3element Array{Float64,1}: 2.1400549446402604 1.0000000000000002 5.140054944640259source
eigvals(A::Union{SymTridiagonal, Hermitian, Symmetric}, vl::Real, vu::Real) > values
Returns the eigenvalues of A
. It is possible to calculate only a subset of the eigenvalues by specifying a pair vl
and vu
for the lower and upper boundaries of the eigenvalues.
julia> A = SymTridiagonal([1.; 2.; 1.], [2.; 3.]) 3×3 SymTridiagonal{Float64,Array{Float64,1}}: 1.0 2.0 ⋅ 2.0 2.0 3.0 ⋅ 3.0 1.0 julia> eigvals(A, 1, 2) 1element Array{Float64,1}: 1.0000000000000009 julia> eigvals(A) 3element Array{Float64,1}: 2.1400549446402604 1.0000000000000002 5.140054944640259source
LinearAlgebra.eigvals!
Function
eigvals!(A; permute::Bool=true, scale::Bool=true, sortby) > values
Same as eigvals
, but saves space by overwriting the input A
, instead of creating a copy. The permute
, scale
, and sortby
keywords are the same as for eigen
.
The input matrix A
will not contain its eigenvalues after eigvals!
is called on it  A
is used as a workspace.
Examples
julia> A = [1. 2.; 3. 4.] 2×2 Array{Float64,2}: 1.0 2.0 3.0 4.0 julia> eigvals!(A) 2element Array{Float64,1}: 0.3722813232690143 5.372281323269014 julia> A 2×2 Array{Float64,2}: 0.372281 1.0 0.0 5.37228source
eigvals!(A, B; sortby) > values
Same as eigvals
, but saves space by overwriting the input A
(and B
), instead of creating copies.
The input matrices A
and B
will not contain their eigenvalues after eigvals!
is called. They are used as workspaces.
Examples
julia> A = [1. 0.; 0. 1.] 2×2 Array{Float64,2}: 1.0 0.0 0.0 1.0 julia> B = [0. 1.; 1. 0.] 2×2 Array{Float64,2}: 0.0 1.0 1.0 0.0 julia> eigvals!(A, B) 2element Array{Complex{Float64},1}: 0.0  1.0im 0.0 + 1.0im julia> A 2×2 Array{Float64,2}: 0.0 1.0 1.0 0.0 julia> B 2×2 Array{Float64,2}: 1.0 0.0 0.0 1.0source
eigvals!(A::Union{SymTridiagonal, Hermitian, Symmetric}, irange::UnitRange) > values
Same as eigvals
, but saves space by overwriting the input A
, instead of creating a copy. irange
is a range of eigenvalue indices to search for  for instance, the 2nd to 8th eigenvalues.
eigvals!(A::Union{SymTridiagonal, Hermitian, Symmetric}, vl::Real, vu::Real) > values
Same as eigvals
, but saves space by overwriting the input A
, instead of creating a copy. vl
is the lower bound of the interval to search for eigenvalues, and vu
is the upper bound.
LinearAlgebra.eigmax
Function
eigmax(A; permute::Bool=true, scale::Bool=true)
Return the largest eigenvalue of A
. The option permute=true
permutes the matrix to become closer to upper triangular, and scale=true
scales the matrix by its diagonal elements to make rows and columns more equal in norm. Note that if the eigenvalues of A
are complex, this method will fail, since complex numbers cannot be sorted.
Examples
julia> A = [0 im; im 0] 2×2 Array{Complex{Int64},2}: 0+0im 0+1im 01im 0+0im julia> eigmax(A) 1.0 julia> A = [0 im; 1 0] 2×2 Array{Complex{Int64},2}: 0+0im 0+1im 1+0im 0+0im julia> eigmax(A) ERROR: DomainError with Complex{Int64}[0+0im 0+1im; 1+0im 0+0im]: `A` cannot have complex eigenvalues. Stacktrace: [...]source
LinearAlgebra.eigmin
Function
eigmin(A; permute::Bool=true, scale::Bool=true)
Return the smallest eigenvalue of A
. The option permute=true
permutes the matrix to become closer to upper triangular, and scale=true
scales the matrix by its diagonal elements to make rows and columns more equal in norm. Note that if the eigenvalues of A
are complex, this method will fail, since complex numbers cannot be sorted.
Examples
julia> A = [0 im; im 0] 2×2 Array{Complex{Int64},2}: 0+0im 0+1im 01im 0+0im julia> eigmin(A) 1.0 julia> A = [0 im; 1 0] 2×2 Array{Complex{Int64},2}: 0+0im 0+1im 1+0im 0+0im julia> eigmin(A) ERROR: DomainError with Complex{Int64}[0+0im 0+1im; 1+0im 0+0im]: `A` cannot have complex eigenvalues. Stacktrace: [...]source
LinearAlgebra.eigvecs
Function
eigvecs(A::SymTridiagonal[, eigvals]) > Matrix
Return a matrix M
whose columns are the eigenvectors of A
. (The k
th eigenvector can be obtained from the slice M[:, k]
.)
If the optional vector of eigenvalues eigvals
is specified, eigvecs
returns the specific corresponding eigenvectors.
Examples
julia> A = SymTridiagonal([1.; 2.; 1.], [2.; 3.]) 3×3 SymTridiagonal{Float64,Array{Float64,1}}: 1.0 2.0 ⋅ 2.0 2.0 3.0 ⋅ 3.0 1.0 julia> eigvals(A) 3element Array{Float64,1}: 2.1400549446402604 1.0000000000000002 5.140054944640259 julia> eigvecs(A) 3×3 Array{Float64,2}: 0.418304 0.83205 0.364299 0.656749 7.39009e16 0.754109 0.627457 0.5547 0.546448 julia> eigvecs(A, [1.]) 3×1 Array{Float64,2}: 0.8320502943378438 4.263514128092366e17 0.5547001962252291source
eigvecs(A; permute::Bool=true, scale::Bool=true, `sortby`) > Matrix
Return a matrix M
whose columns are the eigenvectors of A
. (The k
th eigenvector can be obtained from the slice M[:, k]
.) The permute
, scale
, and sortby
keywords are the same as for eigen
.
Examples
julia> eigvecs([1.0 0.0 0.0; 0.0 3.0 0.0; 0.0 0.0 18.0]) 3×3 Array{Float64,2}: 1.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 1.0source
eigvecs(A, B) > Matrix
Return a matrix M
whose columns are the generalized eigenvectors of A
and B
. (The k
th eigenvector can be obtained from the slice M[:, k]
.)
Examples
julia> A = [1 0; 0 1] 2×2 Array{Int64,2}: 1 0 0 1 julia> B = [0 1; 1 0] 2×2 Array{Int64,2}: 0 1 1 0 julia> eigvecs(A, B) 2×2 Array{Complex{Float64},2}: 0.0+1.0im 0.01.0im 1.0+0.0im 1.00.0imsource
LinearAlgebra.eigen
Function
eigen(A; permute::Bool=true, scale::Bool=true, sortby) > Eigen
Computes the eigenvalue decomposition of A
, returning an Eigen
factorization object F
which contains the eigenvalues in F.values
and the eigenvectors in the columns of the matrix F.vectors
. (The k
th eigenvector can be obtained from the slice F.vectors[:, k]
.)
Iterating the decomposition produces the components F.values
and F.vectors
.
The following functions are available for Eigen
objects: inv
, det
, and isposdef
.
For general nonsymmetric matrices it is possible to specify how the matrix is balanced before the eigenvector calculation. The option permute=true
permutes the matrix to become closer to upper triangular, and scale=true
scales the matrix by its diagonal elements to make rows and columns more equal in norm. The default is true
for both options.
By default, the eigenvalues and vectors are sorted lexicographically by (real(λ),imag(λ))
. A different comparison function by(λ)
can be passed to sortby
, or you can pass sortby=nothing
to leave the eigenvalues in an arbitrary order. Some special matrix types (e.g. Diagonal
or SymTridiagonal
) may implement their own sorting convention and not accept a sortby
keyword.
Examples
julia> F = eigen([1.0 0.0 0.0; 0.0 3.0 0.0; 0.0 0.0 18.0]) Eigen{Float64,Float64,Array{Float64,2},Array{Float64,1}} eigenvalues: 3element Array{Float64,1}: 1.0 3.0 18.0 eigenvectors: 3×3 Array{Float64,2}: 1.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 1.0 julia> F.values 3element Array{Float64,1}: 1.0 3.0 18.0 julia> F.vectors 3×3 Array{Float64,2}: 1.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 1.0 julia> vals, vecs = F; # destructuring via iteration julia> vals == F.values && vecs == F.vectors truesource
eigen(A, B) > GeneralizedEigen
Computes the generalized eigenvalue decomposition of A
and B
, returning a GeneralizedEigen
factorization object F
which contains the generalized eigenvalues in F.values
and the generalized eigenvectors in the columns of the matrix F.vectors
. (The k
th generalized eigenvector can be obtained from the slice F.vectors[:, k]
.)
Iterating the decomposition produces the components F.values
and F.vectors
.
Any keyword arguments passed to eigen
are passed through to the lowerlevel eigen!
function.
Examples
julia> A = [1 0; 0 1] 2×2 Array{Int64,2}: 1 0 0 1 julia> B = [0 1; 1 0] 2×2 Array{Int64,2}: 0 1 1 0 julia> F = eigen(A, B); julia> F.values 2element Array{Complex{Float64},1}: 0.0  1.0im 0.0 + 1.0im julia> F.vectors 2×2 Array{Complex{Float64},2}: 0.0+1.0im 0.01.0im 1.0+0.0im 1.00.0im julia> vals, vecs = F; # destructuring via iteration julia> vals == F.values && vecs == F.vectors truesource
eigen(A::Union{SymTridiagonal, Hermitian, Symmetric}, irange::UnitRange) > Eigen
Computes the eigenvalue decomposition of A
, returning an Eigen
factorization object F
which contains the eigenvalues in F.values
and the eigenvectors in the columns of the matrix F.vectors
. (The k
th eigenvector can be obtained from the slice F.vectors[:, k]
.)
Iterating the decomposition produces the components F.values
and F.vectors
.
The following functions are available for Eigen
objects: inv
, det
, and isposdef
.
The UnitRange
irange
specifies indices of the sorted eigenvalues to search for.
If irange
is not 1:n
, where n
is the dimension of A
, then the returned factorization will be a truncated factorization.
eigen(A::Union{SymTridiagonal, Hermitian, Symmetric}, vl::Real, vu::Real) > Eigen
Computes the eigenvalue decomposition of A
, returning an Eigen
factorization object F
which contains the eigenvalues in F.values
and the eigenvectors in the columns of the matrix F.vectors
. (The k
th eigenvector can be obtained from the slice F.vectors[:, k]
.)
Iterating the decomposition produces the components F.values
and F.vectors
.
The following functions are available for Eigen
objects: inv
, det
, and isposdef
.
vl
is the lower bound of the window of eigenvalues to search for, and vu
is the upper bound.
If [vl
, vu
] does not contain all eigenvalues of A
, then the returned factorization will be a truncated factorization.
LinearAlgebra.eigen!
Function
eigen!(A, [B]; permute, scale, sortby)
Same as eigen
, but saves space by overwriting the input A
(and B
), instead of creating a copy.
LinearAlgebra.Hessenberg
Type
Hessenberg <: Factorization
A Hessenberg
object represents the Hessenberg factorization QHQ'
of a square matrix, or a shift Q(H+μI)Q'
thereof, which is produced by the hessenberg
function.
LinearAlgebra.hessenberg
Function
hessenberg(A) > Hessenberg
Compute the Hessenberg decomposition of A
and return a Hessenberg
object. If F
is the factorization object, the unitary matrix can be accessed with F.Q
(of type LinearAlgebra.HessenbergQ
) and the Hessenberg matrix with F.H
(of type UpperHessenberg
), either of which may be converted to a regular matrix with Matrix(F.H)
or Matrix(F.Q)
.
If A
is Hermitian
or realSymmetric
, then the Hessenberg decomposition produces a realsymmetric tridiagonal matrix and F.H
is of type SymTridiagonal
.
Note that the shifted factorization A+μI = Q (H+μI) Q'
can be constructed efficiently by F + μ*I
using the UniformScaling
object I
, which creates a new Hessenberg
object with shared storage and a modified shift. The shift of a given F
is obtained by F.μ
. This is useful because multiple shifted solves (F + μ*I) \ b
(for different μ
and/or b
) can be performed efficiently once F
is created.
Iterating the decomposition produces the factors F.Q, F.H, F.μ
.
Examples
julia> A = [4. 9. 7.; 4. 4. 1.; 4. 3. 2.] 3×3 Array{Float64,2}: 4.0 9.0 7.0 4.0 4.0 1.0 4.0 3.0 2.0 julia> F = hessenberg(A); julia> F.Q * F.H * F.Q' 3×3 Array{Float64,2}: 4.0 9.0 7.0 4.0 4.0 1.0 4.0 3.0 2.0 julia> q, h = F; # destructuring via iteration julia> q == F.Q && h == F.H truesource
LinearAlgebra.hessenberg!
Function
hessenberg!(A) > Hessenberg
hessenberg!
is the same as hessenberg
, but saves space by overwriting the input A
, instead of creating a copy.
LinearAlgebra.Schur
Type
Schur <: Factorization
Matrix factorization type of the Schur factorization of a matrix A
. This is the return type of schur(_)
, the corresponding matrix factorization function.
If F::Schur
is the factorization object, the (quasi) triangular Schur factor can be obtained via either F.Schur
or F.T
and the orthogonal/unitary Schur vectors via F.vectors
or F.Z
such that A = F.vectors * F.Schur * F.vectors'
. The eigenvalues of A
can be obtained with F.values
.
Iterating the decomposition produces the components F.T
, F.Z
, and F.values
.
Examples
julia> A = [5. 7.; 2. 4.] 2×2 Array{Float64,2}: 5.0 7.0 2.0 4.0 julia> F = schur(A) Schur{Float64,Array{Float64,2}} T factor: 2×2 Array{Float64,2}: 3.0 9.0 0.0 2.0 Z factor: 2×2 Array{Float64,2}: 0.961524 0.274721 0.274721 0.961524 eigenvalues: 2element Array{Float64,1}: 3.0 2.0 julia> F.vectors * F.Schur * F.vectors' 2×2 Array{Float64,2}: 5.0 7.0 2.0 4.0 julia> t, z, vals = F; # destructuring via iteration julia> t == F.T && z == F.Z && vals == F.values truesource
LinearAlgebra.GeneralizedSchur
Type
GeneralizedSchur <: Factorization
Matrix factorization type of the generalized Schur factorization of two matrices A
and B
. This is the return type of schur(_, _)
, the corresponding matrix factorization function.
If F::GeneralizedSchur
is the factorization object, the (quasi) triangular Schur factors can be obtained via F.S
and F.T
, the left unitary/orthogonal Schur vectors via F.left
or F.Q
, and the right unitary/orthogonal Schur vectors can be obtained with F.right
or F.Z
such that A=F.left*F.S*F.right'
and B=F.left*F.T*F.right'
. The generalized eigenvalues of A
and B
can be obtained with F.α./F.β
.
Iterating the decomposition produces the components F.S
, F.T
, F.Q
, F.Z
, F.α
, and F.β
.
LinearAlgebra.schur
Function
schur(A::StridedMatrix) > F::Schur
Computes the Schur factorization of the matrix A
. The (quasi) triangular Schur factor can be obtained from the Schur
object F
with either F.Schur
or F.T
and the orthogonal/unitary Schur vectors can be obtained with F.vectors
or F.Z
such that A = F.vectors * F.Schur * F.vectors'
. The eigenvalues of A
can be obtained with F.values
.
Iterating the decomposition produces the components F.T
, F.Z
, and F.values
.
Examples
julia> A = [5. 7.; 2. 4.] 2×2 Array{Float64,2}: 5.0 7.0 2.0 4.0 julia> F = schur(A) Schur{Float64,Array{Float64,2}} T factor: 2×2 Array{Float64,2}: 3.0 9.0 0.0 2.0 Z factor: 2×2 Array{Float64,2}: 0.961524 0.274721 0.274721 0.961524 eigenvalues: 2element Array{Float64,1}: 3.0 2.0 julia> F.vectors * F.Schur * F.vectors' 2×2 Array{Float64,2}: 5.0 7.0 2.0 4.0 julia> t, z, vals = F; # destructuring via iteration julia> t == F.T && z == F.Z && vals == F.values truesource
schur(A::StridedMatrix, B::StridedMatrix) > F::GeneralizedSchur
Computes the Generalized Schur (or QZ) factorization of the matrices A
and B
. The (quasi) triangular Schur factors can be obtained from the Schur
object F
with F.S
and F.T
, the left unitary/orthogonal Schur vectors can be obtained with F.left
or F.Q
and the right unitary/orthogonal Schur vectors can be obtained with F.right
or F.Z
such that A=F.left*F.S*F.right'
and B=F.left*F.T*F.right'
. The generalized eigenvalues of A
and B
can be obtained with F.α./F.β
.
Iterating the decomposition produces the components F.S
, F.T
, F.Q
, F.Z
, F.α
, and F.β
.
LinearAlgebra.schur!
Function
schur!(A::StridedMatrix) > F::Schur
Same as schur
but uses the input argument A
as workspace.
Examples
julia> A = [5. 7.; 2. 4.] 2×2 Array{Float64,2}: 5.0 7.0 2.0 4.0 julia> F = schur!(A) Schur{Float64,Array{Float64,2}} T factor: 2×2 Array{Float64,2}: 3.0 9.0 0.0 2.0 Z factor: 2×2 Array{Float64,2}: 0.961524 0.274721 0.274721 0.961524 eigenvalues: 2element Array{Float64,1}: 3.0 2.0 julia> A 2×2 Array{Float64,2}: 3.0 9.0 0.0 2.0source
schur!(A::StridedMatrix, B::StridedMatrix) > F::GeneralizedSchur
Same as schur
but uses the input matrices A
and B
as workspace.
LinearAlgebra.ordschur
Function
ordschur(F::Schur, select::Union{Vector{Bool},BitVector}) > F::Schur
Reorders the Schur factorization F
of a matrix A = Z*T*Z'
according to the logical array select
returning the reordered factorization F
object. The selected eigenvalues appear in the leading diagonal of F.Schur
and the corresponding leading columns of F.vectors
form an orthogonal/unitary basis of the corresponding right invariant subspace. In the real case, a complex conjugate pair of eigenvalues must be either both included or both excluded via select
.
ordschur(F::GeneralizedSchur, select::Union{Vector{Bool},BitVector}) > F::GeneralizedSchur
Reorders the Generalized Schur factorization F
of a matrix pair (A, B) = (Q*S*Z', Q*T*Z')
according to the logical array select
and returns a GeneralizedSchur object F
. The selected eigenvalues appear in the leading diagonal of both F.S
and F.T
, and the left and right orthogonal/unitary Schur vectors are also reordered such that (A, B) = F.Q*(F.S, F.T)*F.Z'
still holds and the generalized eigenvalues of A
and B
can still be obtained with F.α./F.β
.
LinearAlgebra.ordschur!
Function
ordschur!(F::Schur, select::Union{Vector{Bool},BitVector}) > F::Schur
Same as ordschur
but overwrites the factorization F
.
ordschur!(F::GeneralizedSchur, select::Union{Vector{Bool},BitVector}) > F::GeneralizedSchur
Same as ordschur
but overwrites the factorization F
.
LinearAlgebra.SVD
Type
SVD <: Factorization
Matrix factorization type of the singular value decomposition (SVD) of a matrix A
. This is the return type of svd(_)
, the corresponding matrix factorization function.
If F::SVD
is the factorization object, U
, S
, V
and Vt
can be obtained via F.U
, F.S
, F.V
and F.Vt
, such that A = U * Diagonal(S) * Vt
. The singular values in S
are sorted in descending order.
Iterating the decomposition produces the components U
, S
, and V
.
Examples
julia> A = [1. 0. 0. 0. 2.; 0. 0. 3. 0. 0.; 0. 0. 0. 0. 0.; 0. 2. 0. 0. 0.] 4×5 Array{Float64,2}: 1.0 0.0 0.0 0.0 2.0 0.0 0.0 3.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 2.0 0.0 0.0 0.0 julia> F = svd(A) SVD{Float64,Float64,Array{Float64,2}} U factor: 4×4 Array{Float64,2}: 0.0 1.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 1.0 0.0 singular values: 4element Array{Float64,1}: 3.0 2.23606797749979 2.0 0.0 Vt factor: 4×5 Array{Float64,2}: 0.0 0.0 1.0 0.0 0.0 0.447214 0.0 0.0 0.0 0.894427 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 julia> F.U * Diagonal(F.S) * F.Vt 4×5 Array{Float64,2}: 1.0 0.0 0.0 0.0 2.0 0.0 0.0 3.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 2.0 0.0 0.0 0.0 julia> u, s, v = F; # destructuring via iteration julia> u == F.U && s == F.S && v == F.V truesource
LinearAlgebra.GeneralizedSVD
Type
GeneralizedSVD <: Factorization
Matrix factorization type of the generalized singular value decomposition (SVD) of two matrices A
and B
, such that A = F.U*F.D1*F.R0*F.Q'
and B = F.V*F.D2*F.R0*F.Q'
. This is the return type of svd(_, _)
, the corresponding matrix factorization function.
For an MbyN matrix A
and PbyN matrix B
,

U
is a MbyM orthogonal matrix, 
V
is a PbyP orthogonal matrix, 
Q
is a NbyN orthogonal matrix, 
D1
is a Mby(K+L) diagonal matrix with 1s in the first K entries, 
D2
is a Pby(K+L) matrix whose top right LbyL block is diagonal, 
R0
is a (K+L)byN matrix whose rightmost (K+L)by(K+L) block is nonsingular upper block triangular,
K+L
is the effective numerical rank of the matrix [A; B]
.
Iterating the decomposition produces the components U
, V
, Q
, D1
, D2
, and R0
.
The entries of F.D1
and F.D2
are related, as explained in the LAPACK documentation for the generalized SVD and the xGGSVD3 routine which is called underneath (in LAPACK 3.6.0 and newer).
Examples
julia> A = [1. 0.; 0. 1.] 2×2 Array{Float64,2}: 1.0 0.0 0.0 1.0 julia> B = [0. 1.; 1. 0.] 2×2 Array{Float64,2}: 0.0 1.0 1.0 0.0 julia> F = svd(A, B); julia> F.U*F.D1*F.R0*F.Q' 2×2 Array{Float64,2}: 1.0 0.0 0.0 1.0 julia> F.V*F.D2*F.R0*F.Q' 2×2 Array{Float64,2}: 0.0 1.0 1.0 0.0source
LinearAlgebra.svd
Function
svd(A; full::Bool = false, alg::Algorithm = default_svd_alg(A)) > SVD
Compute the singular value decomposition (SVD) of A
and return an SVD
object.
U
, S
, V
and Vt
can be obtained from the factorization F
with F.U
, F.S
, F.V
and F.Vt
, such that A = U * Diagonal(S) * Vt
. The algorithm produces Vt
and hence Vt
is more efficient to extract than V
. The singular values in S
are sorted in descending order.
Iterating the decomposition produces the components U
, S
, and V
.
If full = false
(default), a "thin" SVD is returned. For a $M \times N$ matrix A
, in the full factorization U
is M \times M
and V
is N \times N
, while in the thin factorization U
is M \times K
and V
is N \times K
, where K = \min(M,N)
is the number of singular values.
If alg = DivideAndConquer()
a divideandconquer algorithm is used to calculate the SVD. Another (typically slower but more accurate) option is alg = QRIteration()
.
The alg
keyword argument requires Julia 1.3 or later.
Examples
julia> A = [1. 0. 0. 0. 2.; 0. 0. 3. 0. 0.; 0. 0. 0. 0. 0.; 0. 2. 0. 0. 0.] 4×5 Array{Float64,2}: 1.0 0.0 0.0 0.0 2.0 0.0 0.0 3.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 2.0 0.0 0.0 0.0 julia> F = svd(A); julia> F.U * Diagonal(F.S) * F.Vt 4×5 Array{Float64,2}: 1.0 0.0 0.0 0.0 2.0 0.0 0.0 3.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 2.0 0.0 0.0 0.0 julia> u, s, v = F; # destructuring via iteration julia> u == F.U && s == F.S && v == F.V truesource
svd(A, B) > GeneralizedSVD
Compute the generalized SVD of A
and B
, returning a GeneralizedSVD
factorization object F
, such that A = F.U*F.D1*F.R0*F.Q'
and B = F.V*F.D2*F.R0*F.Q'
.
For an MbyN matrix A
and PbyN matrix B
,

U
is a MbyM orthogonal matrix, 
V
is a PbyP orthogonal matrix, 
Q
is a NbyN orthogonal matrix, 
D1
is a Mby(K+L) diagonal matrix with 1s in the first K entries, 
D2
is a Pby(K+L) matrix whose top right LbyL block is diagonal, 
R0
is a (K+L)byN matrix whose rightmost (K+L)by(K+L) block is nonsingular upper block triangular,
K+L
is the effective numerical rank of the matrix [A; B]
.
Iterating the decomposition produces the components U
, V
, Q
, D1
, D2
, and R0
.
The entries of F.D1
and F.D2
are related, as explained in the LAPACK documentation for the generalized SVD and the xGGSVD3 routine which is called underneath (in LAPACK 3.6.0 and newer).
Examples
julia> A = [1. 0.; 0. 1.] 2×2 Array{Float64,2}: 1.0 0.0 0.0 1.0 julia> B = [0. 1.; 1. 0.] 2×2 Array{Float64,2}: 0.0 1.0 1.0 0.0 julia> F = svd(A, B); julia> F.U*F.D1*F.R0*F.Q' 2×2 Array{Float64,2}: 1.0 0.0 0.0 1.0 julia> F.V*F.D2*F.R0*F.Q' 2×2 Array{Float64,2}: 0.0 1.0 1.0 0.0source
LinearAlgebra.svd!
Function
svd!(A; full::Bool = false, alg::Algorithm = default_svd_alg(A)) > SVD
svd!
is the same as svd
, but saves space by overwriting the input A
, instead of creating a copy.
Examples
julia> A = [1. 0. 0. 0. 2.; 0. 0. 3. 0. 0.; 0. 0. 0. 0. 0.; 0. 2. 0. 0. 0.] 4×5 Array{Float64,2}: 1.0 0.0 0.0 0.0 2.0 0.0 0.0 3.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 2.0 0.0 0.0 0.0 julia> F = svd!(A); julia> F.U * Diagonal(F.S) * F.Vt 4×5 Array{Float64,2}: 1.0 0.0 0.0 0.0 2.0 0.0 0.0 3.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 2.0 0.0 0.0 0.0 julia> A 4×5 Array{Float64,2}: 2.23607 0.0 0.0 0.0 0.618034 0.0 3.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 2.0 0.0 0.0source
svd!(A, B) > GeneralizedSVD
svd!
is the same as svd
, but modifies the arguments A
and B
inplace, instead of making copies.
Examples
julia> A = [1. 0.; 0. 1.] 2×2 Array{Float64,2}: 1.0 0.0 0.0 1.0 julia> B = [0. 1.; 1. 0.] 2×2 Array{Float64,2}: 0.0 1.0 1.0 0.0 julia> F = svd!(A, B); julia> F.U*F.D1*F.R0*F.Q' 2×2 Array{Float64,2}: 1.0 0.0 0.0 1.0 julia> F.V*F.D2*F.R0*F.Q' 2×2 Array{Float64,2}: 0.0 1.0 1.0 0.0 julia> A 2×2 Array{Float64,2}: 1.41421 0.0 0.0 1.41421 julia> B 2×2 Array{Float64,2}: 1.0 0.0 0.0 1.0source
LinearAlgebra.svdvals
Function
svdvals(A)
Return the singular values of A
in descending order.
Examples
julia> A = [1. 0. 0. 0. 2.; 0. 0. 3. 0. 0.; 0. 0. 0. 0. 0.; 0. 2. 0. 0. 0.] 4×5 Array{Float64,2}: 1.0 0.0 0.0 0.0 2.0 0.0 0.0 3.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 2.0 0.0 0.0 0.0 julia> svdvals(A) 4element Array{Float64,1}: 3.0 2.23606797749979 2.0 0.0source
svdvals(A, B)
Return the generalized singular values from the generalized singular value decomposition of A
and B
. See also svd
.
Examples
julia> A = [1. 0.; 0. 1.] 2×2 Array{Float64,2}: 1.0 0.0 0.0 1.0 julia> B = [0. 1.; 1. 0.] 2×2 Array{Float64,2}: 0.0 1.0 1.0 0.0 julia> svdvals(A, B) 2element Array{Float64,1}: 1.0 1.0source
LinearAlgebra.svdvals!
Function
svdvals!(A)
Return the singular values of A
, saving space by overwriting the input. See also svdvals
and svd
.
Examples
julia> A = [1. 0. 0. 0. 2.; 0. 0. 3. 0. 0.; 0. 0. 0. 0. 0.; 0. 2. 0. 0. 0.] 4×5 Array{Float64,2}: 1.0 0.0 0.0 0.0 2.0 0.0 0.0 3.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 2.0 0.0 0.0 0.0 julia> svdvals!(A) 4element Array{Float64,1}: 3.0 2.23606797749979 2.0 0.0 julia> A 4×5 Array{Float64,2}: 2.23607 0.0 0.0 0.0 0.618034 0.0 3.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 2.0 0.0 0.0source
svdvals!(A, B)
Return the generalized singular values from the generalized singular value decomposition of A
and B
, saving space by overwriting A
and B
. See also svd
and svdvals
.
Examples
julia> A = [1. 0.; 0. 1.] 2×2 Array{Float64,2}: 1.0 0.0 0.0 1.0 julia> B = [0. 1.; 1. 0.] 2×2 Array{Float64,2}: 0.0 1.0 1.0 0.0 julia> svdvals!(A, B) 2element Array{Float64,1}: 1.0 1.0 julia> A 2×2 Array{Float64,2}: 1.41421 0.0 0.0 1.41421 julia> B 2×2 Array{Float64,2}: 1.0 0.0 0.0 1.0source
LinearAlgebra.Givens
Type
LinearAlgebra.Givens(i1,i2,c,s) > G
A Givens rotation linear operator. The fields c
and s
represent the cosine and sine of the rotation angle, respectively. The Givens
type supports left multiplication G*A
and conjugated transpose right multiplication A*G'
. The type doesn't have a size
and can therefore be multiplied with matrices of arbitrary size as long as i2<=size(A,2)
for G*A
or i2<=size(A,1)
for A*G'
.
See also: givens
LinearAlgebra.givens
Function
givens(f::T, g::T, i1::Integer, i2::Integer) where {T} > (G::Givens, r::T)
Computes the Givens rotation G
and scalar r
such that for any vector x
where
x[i1] = f x[i2] = g
the result of the multiplication
y = G*x
has the property that
y[i1] = r y[i2] = 0
See also: LinearAlgebra.Givens
givens(A::AbstractArray, i1::Integer, i2::Integer, j::Integer) > (G::Givens, r)
Computes the Givens rotation G
and scalar r
such that the result of the multiplication
B = G*A
has the property that
B[i1,j] = r B[i2,j] = 0
See also: LinearAlgebra.Givens
givens(x::AbstractVector, i1::Integer, i2::Integer) > (G::Givens, r)
Computes the Givens rotation G
and scalar r
such that the result of the multiplication
B = G*x
has the property that
B[i1] = r B[i2] = 0
See also: LinearAlgebra.Givens
LinearAlgebra.triu
Function
triu(M)
Upper triangle of a matrix.
Examples
julia> a = fill(1.0, (4,4)) 4×4 Array{Float64,2}: 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 julia> triu(a) 4×4 Array{Float64,2}: 1.0 1.0 1.0 1.0 0.0 1.0 1.0 1.0 0.0 0.0 1.0 1.0 0.0 0.0 0.0 1.0source
triu(M, k::Integer)
Returns the upper triangle of M
starting from the k
th superdiagonal.
Examples
julia> a = fill(1.0, (4,4)) 4×4 Array{Float64,2}: 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 julia> triu(a,3) 4×4 Array{Float64,2}: 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 julia> triu(a,3) 4×4 Array{Float64,2}: 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0source
LinearAlgebra.triu!
Function
triu!(M)
Upper triangle of a matrix, overwriting M
in the process. See also triu
.
triu!(M, k::Integer)
Return the upper triangle of M
starting from the k
th superdiagonal, overwriting M
in the process.
Examples
julia> M = [1 2 3 4 5; 1 2 3 4 5; 1 2 3 4 5; 1 2 3 4 5; 1 2 3 4 5] 5×5 Array{Int64,2}: 1 2 3 4 5 1 2 3 4 5 1 2 3 4 5 1 2 3 4 5 1 2 3 4 5 julia> triu!(M, 1) 5×5 Array{Int64,2}: 0 2 3 4 5 0 0 3 4 5 0 0 0 4 5 0 0 0 0 5 0 0 0 0 0source
LinearAlgebra.tril
Function
tril(M)
Lower triangle of a matrix.
Examples
julia> a = fill(1.0, (4,4)) 4×4 Array{Float64,2}: 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 julia> tril(a) 4×4 Array{Float64,2}: 1.0 0.0 0.0 0.0 1.0 1.0 0.0 0.0 1.0 1.0 1.0 0.0 1.0 1.0 1.0 1.0source
tril(M, k::Integer)
Returns the lower triangle of M
starting from the k
th superdiagonal.
Examples
julia> a = fill(1.0, (4,4)) 4×4 Array{Float64,2}: 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 julia> tril(a,3) 4×4 Array{Float64,2}: 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 julia> tril(a,3) 4×4 Array{Float64,2}: 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0source
LinearAlgebra.tril!
Function
tril!(M)
Lower triangle of a matrix, overwriting M
in the process. See also tril
.
tril!(M, k::Integer)
Return the lower triangle of M
starting from the k
th superdiagonal, overwriting M
in the process.
Examples
julia> M = [1 2 3 4 5; 1 2 3 4 5; 1 2 3 4 5; 1 2 3 4 5; 1 2 3 4 5] 5×5 Array{Int64,2}: 1 2 3 4 5 1 2 3 4 5 1 2 3 4 5 1 2 3 4 5 1 2 3 4 5 julia> tril!(M, 2) 5×5 Array{Int64,2}: 1 2 3 0 0 1 2 3 4 0 1 2 3 4 5 1 2 3 4 5 1 2 3 4 5source
LinearAlgebra.diagind
Function
diagind(M, k::Integer=0)
An AbstractRange
giving the indices of the k
th diagonal of the matrix M
.
Examples
julia> A = [1 2 3; 4 5 6; 7 8 9] 3×3 Array{Int64,2}: 1 2 3 4 5 6 7 8 9 julia> diagind(A,1) 2:4:6source
LinearAlgebra.diag
Function
diag(M, k::Integer=0)
The k
th diagonal of a matrix, as a vector.
See also: diagm
Examples
julia> A = [1 2 3; 4 5 6; 7 8 9] 3×3 Array{Int64,2}: 1 2 3 4 5 6 7 8 9 julia> diag(A,1) 2element Array{Int64,1}: 2 6source
LinearAlgebra.diagm
Function
diagm(kv::Pair{<:Integer,<:AbstractVector}...) diagm(m::Integer, n::Integer, kv::Pair{<:Integer,<:AbstractVector}...)
Construct a matrix from Pair
s of diagonals and vectors. Vector kv.second
will be placed on the kv.first
diagonal. By default the matrix is square and its size is inferred from kv
, but a nonsquare size m
×n
(padded with zeros as needed) can be specified by passing m,n
as the first arguments.
diagm
constructs a full matrix; if you want storageefficient versions with fast arithmetic, see Diagonal
, Bidiagonal
Tridiagonal
and SymTridiagonal
.
Examples
julia> diagm(1 => [1,2,3]) 4×4 Array{Int64,2}: 0 1 0 0 0 0 2 0 0 0 0 3 0 0 0 0 julia> diagm(1 => [1,2,3], 1 => [4,5]) 4×4 Array{Int64,2}: 0 1 0 0 4 0 2 0 0 5 0 3 0 0 0 0source
diagm(v::AbstractVector) diagm(m::Integer, n::Integer, v::AbstractVector)
Construct a matrix with elements of the vector as diagonal elements. By default (if size=nothing
), the matrix is square and its size is given by length(v)
, but a nonsquare size m
×n
can be specified by passing m,n
as the first arguments.
Examples
julia> diagm([1,2,3]) 3×3 Array{Int64,2}: 1 0 0 0 2 0 0 0 3source
LinearAlgebra.rank
Function
rank(A::AbstractMatrix; atol::Real=0, rtol::Real=atol>0 ? 0 : n*ϵ) rank(A::AbstractMatrix, rtol::Real)
Compute the rank of a matrix by counting how many singular values of A
have magnitude greater than max(atol, rtol*σ₁)
where σ₁
is A
's largest singular value. atol
and rtol
are the absolute and relative tolerances, respectively. The default relative tolerance is n*ϵ
, where n
is the size of the smallest dimension of A
, and ϵ
is the eps
of the element type of A
.
The atol
and rtol
keyword arguments requires at least Julia 1.1. In Julia 1.0 rtol
is available as a positional argument, but this will be deprecated in Julia 2.0.
Examples
julia> rank(Matrix(I, 3, 3)) 3 julia> rank(diagm(0 => [1, 0, 2])) 2 julia> rank(diagm(0 => [1, 0.001, 2]), rtol=0.1) 2 julia> rank(diagm(0 => [1, 0.001, 2]), rtol=0.00001) 3 julia> rank(diagm(0 => [1, 0.001, 2]), atol=1.5) 1source
LinearAlgebra.norm
Function
norm(A, p::Real=2)
For any iterable container A
(including arrays of any dimension) of numbers (or any element type for which norm
is defined), compute the p
norm (defaulting to p=2
) as if A
were a vector of the corresponding length.
The p
norm is defined as
with $a_i$ the entries of $A$, $ a_i $ the norm
of $a_i$, and $n$ the length of $A$. Since the p
norm is computed using the norm
s of the entries of A
, the p
norm of a vector of vectors is not compatible with the interpretation of it as a block vector in general if p != 2
.
p
can assume any numeric value (even though not all values produce a mathematically valid vector norm). In particular, norm(A, Inf)
returns the largest value in abs.(A)
, whereas norm(A, Inf)
returns the smallest. If A
is a matrix and p=2
, then this is equivalent to the Frobenius norm.
The second argument p
is not necessarily a part of the interface for norm
, i.e. a custom type may only implement norm(A)
without second argument.
Use opnorm
to compute the operator norm of a matrix.
Examples
julia> v = [3, 2, 6] 3element Array{Int64,1}: 3 2 6 julia> norm(v) 7.0 julia> norm(v, 1) 11.0 julia> norm(v, Inf) 6.0 julia> norm([1 2 3; 4 5 6; 7 8 9]) 16.881943016134134 julia> norm([1 2 3 4 5 6 7 8 9]) 16.881943016134134 julia> norm(1:9) 16.881943016134134 julia> norm(hcat(v,v), 1) == norm(vcat(v,v), 1) != norm([v,v], 1) true julia> norm(hcat(v,v), 2) == norm(vcat(v,v), 2) == norm([v,v], 2) true julia> norm(hcat(v,v), Inf) == norm(vcat(v,v), Inf) != norm([v,v], Inf) truesource
norm(x::Number, p::Real=2)
For numbers, return $\left( x^p \right)^{1/p}$.
Examples
julia> norm(2, 1) 2.0 julia> norm(2, 1) 2.0 julia> norm(2, 2) 2.0 julia> norm(2, 2) 2.0 julia> norm(2, Inf) 2.0 julia> norm(2, Inf) 2.0source
LinearAlgebra.opnorm
Function
opnorm(A::AbstractMatrix, p::Real=2)
Compute the operator norm (or matrix norm) induced by the vector p
norm, where valid values of p
are 1
, 2
, or Inf
. (Note that for sparse matrices, p=2
is currently not implemented.) Use norm
to compute the Frobenius norm.
When p=1
, the operator norm is the maximum absolute column sum of A
:
with $a_{ij}$ the entries of $A$, and $m$ and $n$ its dimensions.
When p=2
, the operator norm is the spectral norm, equal to the largest singular value of A
.
When p=Inf
, the operator norm is the maximum absolute row sum of A
:
Examples
julia> A = [1 2 3; 2 3 1] 2×3 Array{Int64,2}: 1 2 3 2 3 1 julia> opnorm(A, Inf) 6.0 julia> opnorm(A, 1) 5.0source
opnorm(x::Number, p::Real=2)
For numbers, return $\left( x^p \right)^{1/p}$. This is equivalent to norm
.
opnorm(A::Adjoint{<:Any,<:AbstracVector}, q::Real=2) opnorm(A::Transpose{<:Any,<:AbstracVector}, q::Real=2)
For Adjoint/Transposewrapped vectors, return the operator $q$norm of A
, which is equivalent to the p
norm with value p = q/(q1)
. They coincide at p = q = 2
. Use norm
to compute the p
norm of A
as a vector.
The difference in norm between a vector space and its dual arises to preserve the relationship between duality and the dot product, and the result is consistent with the operator p
norm of a 1 × n
matrix.
Examples
julia> v = [1; im]; julia> vc = v'; julia> opnorm(vc, 1) 1.0 julia> norm(vc, 1) 2.0 julia> norm(v, 1) 2.0 julia> opnorm(vc, 2) 1.4142135623730951 julia> norm(vc, 2) 1.4142135623730951 julia> norm(v, 2) 1.4142135623730951 julia> opnorm(vc, Inf) 2.0 julia> norm(vc, Inf) 1.0 julia> norm(v, Inf) 1.0source
LinearAlgebra.normalize!
Function
normalize!(v::AbstractVector, p::Real=2)
Normalize the vector v
inplace so that its p
norm equals unity, i.e. norm(v, p) == 1
. See also normalize
and norm
.
LinearAlgebra.normalize
Function
normalize(v::AbstractVector, p::Real=2)
Normalize the vector v
so that its p
norm equals unity, i.e. norm(v, p) == 1
. See also normalize!
and norm
.
Examples
julia> a = [1,2,4]; julia> b = normalize(a) 3element Array{Float64,1}: 0.2182178902359924 0.4364357804719848 0.8728715609439696 julia> norm(b) 1.0 julia> c = normalize(a, 1) 3element Array{Float64,1}: 0.14285714285714285 0.2857142857142857 0.5714285714285714 julia> norm(c, 1) 1.0source
LinearAlgebra.cond
Function
cond(M, p::Real=2)
Condition number of the matrix M
, computed using the operator p
norm. Valid values for p
are 1
, 2
(default), or Inf
.
LinearAlgebra.condskeel
Function
condskeel(M, [x, p::Real=Inf])\[\kappa_S(M, p) = \left\Vert \left\vert M \right\vert \left\vert M^{1} \right\vert \right\Vert_p \\ \kappa_S(M, x, p) = \left\Vert \left\vert M \right\vert \left\vert M^{1} \right\vert \left\vert x \right\vert \right\Vert_p\]
Skeel condition number $\kappa_S$ of the matrix M
, optionally with respect to the vector x
, as computed using the operator p
norm. $\left\vert M \right\vert$ denotes the matrix of (entry wise) absolute values of $M$; $\left\vert M \right\vert_{ij} = \left\vert M_{ij} \right\vert$. Valid values for p
are 1
, 2
and Inf
(default).
This quantity is also known in the literature as the Bauer condition number, relative condition number, or componentwise relative condition number.
source
LinearAlgebra.tr
Function
tr(M)
Matrix trace. Sums the diagonal elements of M
.
Examples
julia> A = [1 2; 3 4] 2×2 Array{Int64,2}: 1 2 3 4 julia> tr(A) 5source
LinearAlgebra.det
Function
det(M)
Matrix determinant.
Examples
julia> M = [1 0; 2 2] 2×2 Array{Int64,2}: 1 0 2 2 julia> det(M) 2.0source
LinearAlgebra.logdet
Function
logdet(M)
Log of matrix determinant. Equivalent to log(det(M))
, but may provide increased accuracy and/or speed.
Examples
julia> M = [1 0; 2 2] 2×2 Array{Int64,2}: 1 0 2 2 julia> logdet(M) 0.6931471805599453 julia> logdet(Matrix(I, 3, 3)) 0.0source
LinearAlgebra.logabsdet
Function
logabsdet(M)
Log of absolute value of matrix determinant. Equivalent to (log(abs(det(M))), sign(det(M)))
, but may provide increased accuracy and/or speed.
Examples
julia> A = [1. 0.; 0. 1.] 2×2 Array{Float64,2}: 1.0 0.0 0.0 1.0 julia> det(A) 1.0 julia> logabsdet(A) (0.0, 1.0) julia> B = [2. 0.; 0. 1.] 2×2 Array{Float64,2}: 2.0 0.0 0.0 1.0 julia> det(B) 2.0 julia> logabsdet(B) (0.6931471805599453, 1.0)source
Base.inv
Method
inv(M)
Matrix inverse. Computes matrix N
such that M * N = I
, where I
is the identity matrix. Computed by solving the leftdivision N = M \ I
.
Examples
julia> M = [2 5; 1 3] 2×2 Array{Int64,2}: 2 5 1 3 julia> N = inv(M) 2×2 Array{Float64,2}: 3.0 5.0 1.0 2.0 julia> M*N == N*M == Matrix(I, 2, 2) truesource
LinearAlgebra.pinv
Function
pinv(M; atol::Real=0, rtol::Real=atol>0 ? 0 : n*ϵ) pinv(M, rtol::Real) = pinv(M; rtol=rtol) # to be deprecated in Julia 2.0
Computes the MoorePenrose pseudoinverse.
For matrices M
with floating point elements, it is convenient to compute the pseudoinverse by inverting only singular values greater than max(atol, rtol*σ₁)
where σ₁
is the largest singular value of M
.
The optimal choice of absolute (atol
) and relative tolerance (rtol
) varies both with the value of M
and the intended application of the pseudoinverse. The default relative tolerance is n*ϵ
, where n
is the size of the smallest dimension of M
, and ϵ
is the eps
of the element type of M
.
For inverting dense illconditioned matrices in a leastsquares sense, rtol = sqrt(eps(real(float(one(eltype(M))))))
is recommended.
For more information, see [issue8859], [B96], [S84], [KY88].
Examples
julia> M = [1.5 1.3; 1.2 1.9] 2×2 Array{Float64,2}: 1.5 1.3 1.2 1.9 julia> N = pinv(M) 2×2 Array{Float64,2}: 1.47287 1.00775 0.930233 1.16279 julia> M * N 2×2 Array{Float64,2}: 1.0 2.22045e16 4.44089e16 1.0
Issue 8859, "Fix least squares", https://github.com/JuliaLang/julia/pull/8859
Åke Björck, "Numerical Methods for Least Squares Problems", SIAM Press, Philadelphia, 1996, "Other Titles in Applied Mathematics", Vol. 51. doi:10.1137/1.9781611971484
G. W. Stewart, "Rank Degeneracy", SIAM Journal on Scientific and Statistical Computing, 5(2), 1984, 403413. doi:10.1137/0905030
Konstantinos Konstantinides and Kung Yao, "Statistical analysis of effective singular values in matrix rank determination", IEEE Transactions on Acoustics, Speech and Signal Processing, 36(5), 1988, 757763. doi:10.1109/29.1585
LinearAlgebra.nullspace
Function
nullspace(M; atol::Real=0, rtol::Rea=atol>0 ? 0 : n*ϵ) nullspace(M, rtol::Real) = nullspace(M; rtol=rtol) # to be deprecated in Julia 2.0
Computes a basis for the nullspace of M
by including the singular vectors of A whose singular have magnitude are greater than max(atol, rtol*σ₁)
, where σ₁
is M
's largest singularvalue.
By default, the relative tolerance rtol
is n*ϵ
, where n
is the size of the smallest dimension of M
, and ϵ
is the eps
of the element type of M
.
Examples
julia> M = [1 0 0; 0 1 0; 0 0 0] 3×3 Array{Int64,2}: 1 0 0 0 1 0 0 0 0 julia> nullspace(M) 3×1 Array{Float64,2}: 0.0 0.0 1.0 julia> nullspace(M, rtol=3) 3×3 Array{Float64,2}: 0.0 1.0 0.0 1.0 0.0 0.0 0.0 0.0 1.0 julia> nullspace(M, atol=0.95) 3×1 Array{Float64,2}: 0.0 0.0 1.0source
Base.kron
Function
kron(A, B)
Kronecker tensor product of two vectors or two matrices.
For vectors v and w, the Kronecker product is related to the outer product by kron(v,w) == vec(w*transpose(v))
or w*transpose(v) == reshape(kron(v,w), (length(w), length(v)))
. Note how the ordering of v
and w
differs on the left and right of these expressions (due to columnmajor storage).
Examples
julia> A = [1 2; 3 4] 2×2 Array{Int64,2}: 1 2 3 4 julia> B = [im 1; 1 im] 2×2 Array{Complex{Int64},2}: 0+1im 1+0im 1+0im 01im julia> kron(A, B) 4×4 Array{Complex{Int64},2}: 0+1im 1+0im 0+2im 2+0im 1+0im 01im 2+0im 02im 0+3im 3+0im 0+4im 4+0im 3+0im 03im 4+0im 04im julia> v = [1, 2]; w = [3, 4, 5]; julia> w*transpose(v) 3×2 Array{Int64,2}: 3 6 4 8 5 10 julia> reshape(kron(v,w), (length(w), length(v))) 3×2 Array{Int64,2}: 3 6 4 8 5 10source
Base.exp
Method
exp(A::AbstractMatrix)
Compute the matrix exponential of A
, defined by
For symmetric or Hermitian A
, an eigendecomposition (eigen
) is used, otherwise the scaling and squaring algorithm (see [H05]) is chosen.
Nicholas J. Higham, "The squaring and scaling method for the matrix exponential revisited", SIAM Journal on Matrix Analysis and Applications, 26(4), 2005, 11791193. doi:10.1137/090768539
Examples
julia> A = Matrix(1.0I, 2, 2) 2×2 Array{Float64,2}: 1.0 0.0 0.0 1.0 julia> exp(A) 2×2 Array{Float64,2}: 2.71828 0.0 0.0 2.71828source
Base.:^
Method
^(A::AbstractMatrix, p::Number)
Matrix power, equivalent to $\exp(p\log(A))$
Examples
julia> [1 2; 0 3]^3 2×2 Array{Int64,2}: 1 26 0 27source
Base.:^
Method
^(b::Number, A::AbstractMatrix)
Matrix exponential, equivalent to $\exp(\log(b)A)$.
Support for raising Irrational
numbers (like ℯ
) to a matrix was added in Julia 1.1.
Examples
julia> 2^[1 2; 0 3] 2×2 Array{Float64,2}: 2.0 6.0 0.0 8.0 julia> ℯ^[1 2; 0 3] 2×2 Array{Float64,2}: 2.71828 17.3673 0.0 20.0855source
Base.log
Method
log(A{T}::StridedMatrix{T})
If A
has no negative real eigenvalue, compute the principal matrix logarithm of A
, i.e. the unique matrix $X$ such that $e^X = A$ and $\pi < Im(\lambda) < \pi$ for all the eigenvalues $\lambda$ of $X$. If A
has nonpositive eigenvalues, a nonprincipal matrix function is returned whenever possible.
If A
is symmetric or Hermitian, its eigendecomposition (eigen
) is used, if A
is triangular an improved version of the inverse scaling and squaring method is employed (see [AH12] and [AHR13]). For general matrices, the complex Schur form (schur
) is computed and the triangular algorithm is used on the triangular factor.
Awad H. AlMohy and Nicholas J. Higham, "Improved inverse scaling and squaring algorithms for the matrix logarithm", SIAM Journal on Scientific Computing, 34(4), 2012, C153C169. doi:10.1137/110852553
Awad H. AlMohy, Nicholas J. Higham and Samuel D. Relton, "Computing the Fréchet derivative of the matrix logarithm and estimating the condition number", SIAM Journal on Scientific Computing, 35(4), 2013, C394C410. doi:10.1137/120885991
Examples
julia> A = Matrix(2.7182818*I, 2, 2) 2×2 Array{Float64,2}: 2.71828 0.0 0.0 2.71828 julia> log(A) 2×2 Array{Float64,2}: 1.0 0.0 0.0 1.0source
Base.sqrt
Method
sqrt(A::AbstractMatrix)
If A
has no negative real eigenvalues, compute the principal matrix square root of A
, that is the unique matrix $X$ with eigenvalues having positive real part such that $X^2 = A$. Otherwise, a nonprincipal square root is returned.
If A
is symmetric or Hermitian, its eigendecomposition (eigen
) is used to compute the square root. Otherwise, the square root is determined by means of the BjörckHammarling method [BH83], which computes the complex Schur form (schur
) and then the complex square root of the triangular factor.
Åke Björck and Sven Hammarling, "A Schur method for the square root of a matrix", Linear Algebra and its Applications, 5253, 1983, 127140. doi:10.1016/00243795(83)80010X
Examples
julia> A = [4 0; 0 4] 2×2 Array{Int64,2}: 4 0 0 4 julia> sqrt(A) 2×2 Array{Float64,2}: 2.0 0.0 0.0 2.0source
Base.cos
Method
cos(A::AbstractMatrix)
Compute the matrix cosine of a square matrix A
.
If A
is symmetric or Hermitian, its eigendecomposition (eigen
) is used to compute the cosine. Otherwise, the cosine is determined by calling exp
.
Examples
julia> cos(fill(1.0, (2,2))) 2×2 Array{Float64,2}: 0.291927 0.708073 0.708073 0.291927source
Base.sin
Method
sin(A::AbstractMatrix)
Compute the matrix sine of a square matrix A
.
If A
is symmetric or Hermitian, its eigendecomposition (eigen
) is used to compute the sine. Otherwise, the sine is determined by calling exp
.
Examples
julia> sin(fill(1.0, (2,2))) 2×2 Array{Float64,2}: 0.454649 0.454649 0.454649 0.454649source
Base.Math.sincos
Method
sincos(A::AbstractMatrix)
Compute the matrix sine and cosine of a square matrix A
.
Examples
julia> S, C = sincos(fill(1.0, (2,2))); julia> S 2×2 Array{Float64,2}: 0.454649 0.454649 0.454649 0.454649 julia> C 2×2 Array{Float64,2}: 0.291927 0.708073 0.708073 0.291927source
Base.tan
Method
tan(A::AbstractMatrix)
Compute the matrix tangent of a square matrix A
.
If A
is symmetric or Hermitian, its eigendecomposition (eigen
) is used to compute the tangent. Otherwise, the tangent is determined by calling exp
.
Examples
julia> tan(fill(1.0, (2,2))) 2×2 Array{Float64,2}: 1.09252 1.09252 1.09252 1.09252source
Base.Math.sec
Method
sec(A::AbstractMatrix)
Compute the matrix secant of a square matrix A
.
Base.Math.csc
Method
csc(A::AbstractMatrix)
Compute the matrix cosecant of a square matrix A
.
Base.Math.cot
Method
cot(A::AbstractMatrix)
Compute the matrix cotangent of a square matrix A
.
Base.cosh
Method
cosh(A::AbstractMatrix)
Compute the matrix hyperbolic cosine of a square matrix A
.
Base.sinh
Method
sinh(A::AbstractMatrix)
Compute the matrix hyperbolic sine of a square matrix A
.
Base.tanh
Method
tanh(A::AbstractMatrix)
Compute the matrix hyperbolic tangent of a square matrix A
.
Base.Math.sech
Method
sech(A::AbstractMatrix)
Compute the matrix hyperbolic secant of square matrix A
.
Base.Math.csch
Method
csch(A::AbstractMatrix)
Compute the matrix hyperbolic cosecant of square matrix A
.
Base.Math.coth
Method
coth(A::AbstractMatrix)
Compute the matrix hyperbolic cotangent of square matrix A
.
Base.acos
Method
acos(A::AbstractMatrix)
Compute the inverse matrix cosine of a square matrix A
.
If A
is symmetric or Hermitian, its eigendecomposition (eigen
) is used to compute the inverse cosine. Otherwise, the inverse cosine is determined by using log
and sqrt
. For the theory and logarithmic formulas used to compute this function, see [AH16_1].
Mary Aprahamian and Nicholas J. Higham, "Matrix Inverse Trigonometric and Inverse Hyperbolic Functions: Theory and Algorithms", MIMS EPrint: 2016.4. https://doi.org/10.1137/16M1057577
Examples
julia> acos(cos([0.5 0.1; 0.2 0.3])) 2×2 Array{Complex{Float64},2}: 0.58.32667e17im 0.1+0.0im 0.2+2.63678e16im 0.33.46945e16imsource
Base.asin
Method
asin(A::AbstractMatrix)
Compute the inverse matrix sine of a square matrix A
.
If A
is symmetric or Hermitian, its eigendecomposition (eigen
) is used to compute the inverse sine. Otherwise, the inverse sine is determined by using log
and sqrt
. For the theory and logarithmic formulas used to compute this function, see [AH16_2].
Mary Aprahamian and Nicholas J. Higham, "Matrix Inverse Trigonometric and Inverse Hyperbolic Functions: Theory and Algorithms", MIMS EPrint: 2016.4. https://doi.org/10.1137/16M1057577
Examples
julia> asin(sin([0.5 0.1; 0.2 0.3])) 2×2 Array{Complex{Float64},2}: 0.54.16334e17im 0.15.55112e17im 0.2+9.71445e17im 0.31.249e16imsource
Base.atan
Method
atan(A::AbstractMatrix)
Compute the inverse matrix tangent of a square matrix A
.
If A
is symmetric or Hermitian, its eigendecomposition (eigen
) is used to compute the inverse tangent. Otherwise, the inverse tangent is determined by using log
. For the theory and logarithmic formulas used to compute this function, see [AH16_3].
Mary Aprahamian and Nicholas J. Higham, "Matrix Inverse Trigonometric and Inverse Hyperbolic Functions: Theory and Algorithms", MIMS EPrint: 2016.4. https://doi.org/10.1137/16M1057577
Examples
julia> atan(tan([0.5 0.1; 0.2 0.3])) 2×2 Array{Complex{Float64},2}: 0.5+1.38778e17im 0.12.77556e17im 0.2+6.93889e17im 0.34.16334e17imsource
Base.Math.asec
Method
asec(A::AbstractMatrix)
Compute the inverse matrix secant of A
.
Base.Math.acsc
Method
acsc(A::AbstractMatrix)
Compute the inverse matrix cosecant of A
.
Base.Math.acot
Method
acot(A::AbstractMatrix)
Compute the inverse matrix cotangent of A
.
Base.acosh
Method
acosh(A::AbstractMatrix)
Compute the inverse hyperbolic matrix cosine of a square matrix A
. For the theory and logarithmic formulas used to compute this function, see [AH16_4].
Mary Aprahamian and Nicholas J. Higham, "Matrix Inverse Trigonometric and Inverse Hyperbolic Functions: Theory and Algorithms", MIMS EPrint: 2016.4. https://doi.org/10.1137/16M1057577
Base.asinh
Method
asinh(A::AbstractMatrix)
Compute the inverse hyperbolic matrix sine of a square matrix A
. For the theory and logarithmic formulas used to compute this function, see [AH16_5].
Mary Aprahamian and Nicholas J. Higham, "Matrix Inverse Trigonometric and Inverse Hyperbolic Functions: Theory and Algorithms", MIMS EPrint: 2016.4. https://doi.org/10.1137/16M1057577
Base.atanh
Method
atanh(A::AbstractMatrix)
Compute the inverse hyperbolic matrix tangent of a square matrix A
. For the theory and logarithmic formulas used to compute this function, see [AH16_6].
Mary Aprahamian and Nicholas J. Higham, "Matrix Inverse Trigonometric and Inverse Hyperbolic Functions: Theory and Algorithms", MIMS EPrint: 2016.4. https://doi.org/10.1137/16M1057577
Base.Math.asech
Method
asech(A::AbstractMatrix)
Compute the inverse matrix hyperbolic secant of A
.
Base.Math.acsch
Method
acsch(A::AbstractMatrix)
Compute the inverse matrix hyperbolic cosecant of A
.
Base.Math.acoth
Method
acoth(A::AbstractMatrix)
Compute the inverse matrix hyperbolic cotangent of A
.
LinearAlgebra.lyap
Function
lyap(A, C)
Computes the solution X
to the continuous Lyapunov equation AX + XA' + C = 0
, where no eigenvalue of A
has a zero real part and no two eigenvalues are negative complex conjugates of each other.
Examples
julia> A = [3. 4.; 5. 6] 2×2 Array{Float64,2}: 3.0 4.0 5.0 6.0 julia> B = [1. 1.; 1. 2.] 2×2 Array{Float64,2}: 1.0 1.0 1.0 2.0 julia> X = lyap(A, B) 2×2 Array{Float64,2}: 0.5 0.5 0.5 0.25 julia> A*X + X*A' + B 2×2 Array{Float64,2}: 0.0 6.66134e16 6.66134e16 8.88178e16source
LinearAlgebra.sylvester
Function
sylvester(A, B, C)
Computes the solution X
to the Sylvester equation AX + XB + C = 0
, where A
, B
and C
have compatible dimensions and A
and B
have no eigenvalues with equal real part.
Examples
julia> A = [3. 4.; 5. 6] 2×2 Array{Float64,2}: 3.0 4.0 5.0 6.0 julia> B = [1. 1.; 1. 2.] 2×2 Array{Float64,2}: 1.0 1.0 1.0 2.0 julia> C = [1. 2.; 2. 1] 2×2 Array{Float64,2}: 1.0 2.0 2.0 1.0 julia> X = sylvester(A, B, C) 2×2 Array{Float64,2}: 4.46667 1.93333 3.73333 1.8 julia> A*X + X*B + C 2×2 Array{Float64,2}: 2.66454e15 1.77636e15 3.77476e15 4.44089e16source
LinearAlgebra.issuccess
Function
issuccess(F::Factorization)
Test that a factorization of a matrix succeeded.
julia> F = cholesky([1 0; 0 1]); julia> LinearAlgebra.issuccess(F) true julia> F = lu([1 0; 0 0]; check = false); julia> LinearAlgebra.issuccess(F) falsesource
LinearAlgebra.issymmetric
Function
issymmetric(A) > Bool
Test whether a matrix is symmetric.
Examples
julia> a = [1 2; 2 1] 2×2 Array{Int64,2}: 1 2 2 1 julia> issymmetric(a) true julia> b = [1 im; im 1] 2×2 Array{Complex{Int64},2}: 1+0im 0+1im 01im 1+0im julia> issymmetric(b) falsesource
LinearAlgebra.isposdef
Function
isposdef(A) > Bool
Test whether a matrix is positive definite (and Hermitian) by trying to perform a Cholesky factorization of A
. See also isposdef!
Examples
julia> A = [1 2; 2 50] 2×2 Array{Int64,2}: 1 2 2 50 julia> isposdef(A) truesource
LinearAlgebra.isposdef!
Function
isposdef!(A) > Bool
Test whether a matrix is positive definite (and Hermitian) by trying to perform a Cholesky factorization of A
, overwriting A
in the process. See also isposdef
.
Examples
julia> A = [1. 2.; 2. 50.]; julia> isposdef!(A) true julia> A 2×2 Array{Float64,2}: 1.0 2.0 2.0 6.78233source
LinearAlgebra.istril
Function
istril(A::AbstractMatrix, k::Integer = 0) > Bool
Test whether A
is lower triangular starting from the k
th superdiagonal.
Examples
julia> a = [1 2; 2 1] 2×2 Array{Int64,2}: 1 2 2 1 julia> istril(a) false julia> istril(a, 1) true julia> b = [1 0; im 1] 2×2 Array{Complex{Int64},2}: 1+0im 0+0im 01im 1+0im julia> istril(b) true julia> istril(b, 1) falsesource
LinearAlgebra.istriu
Function
istriu(A::AbstractMatrix, k::Integer = 0) > Bool
Test whether A
is upper triangular starting from the k
th superdiagonal.
Examples
julia> a = [1 2; 2 1] 2×2 Array{Int64,2}: 1 2 2 1 julia> istriu(a) false julia> istriu(a, 1) true julia> b = [1 im; 0 1] 2×2 Array{Complex{Int64},2}: 1+0im 0+1im 0+0im 1+0im julia> istriu(b) true julia> istriu(b, 1) falsesource
LinearAlgebra.isdiag
Function
isdiag(A) > Bool
Test whether a matrix is diagonal.
Examples
julia> a = [1 2; 2 1] 2×2 Array{Int64,2}: 1 2 2 1 julia> isdiag(a) false julia> b = [im 0; 0 im] 2×2 Array{Complex{Int64},2}: 0+1im 0+0im 0+0im 01im julia> isdiag(b) truesource
LinearAlgebra.ishermitian
Function
ishermitian(A) > Bool
Test whether a matrix is Hermitian.
Examples
julia> a = [1 2; 2 1] 2×2 Array{Int64,2}: 1 2 2 1 julia> ishermitian(a) true julia> b = [1 im; im 1] 2×2 Array{Complex{Int64},2}: 1+0im 0+1im 01im 1+0im julia> ishermitian(b) truesource
Base.transpose
Function
transpose(A)
Lazy transpose. Mutating the returned object should appropriately mutate A
. Often, but not always, yields Transpose(A)
, where Transpose
is a lazy transpose wrapper. Note that this operation is recursive.
This operation is intended for linear algebra usage  for general data manipulation see permutedims
, which is nonrecursive.
Examples
julia> A = [3+2im 9+2im; 8+7im 4+6im] 2×2 Array{Complex{Int64},2}: 3+2im 9+2im 8+7im 4+6im julia> transpose(A) 2×2 Transpose{Complex{Int64},Array{Complex{Int64},2}}: 3+2im 8+7im 9+2im 4+6imsource
LinearAlgebra.transpose!
Function
transpose!(dest,src)
Transpose array src
and store the result in the preallocated array dest
, which should have a size corresponding to (size(src,2),size(src,1))
. No inplace transposition is supported and unexpected results will happen if src
and dest
have overlapping memory regions.
Examples
julia> A = [3+2im 9+2im; 8+7im 4+6im] 2×2 Array{Complex{Int64},2}: 3+2im 9+2im 8+7im 4+6im julia> B = zeros(Complex{Int64}, 2, 2) 2×2 Array{Complex{Int64},2}: 0+0im 0+0im 0+0im 0+0im julia> transpose!(B, A); julia> B 2×2 Array{Complex{Int64},2}: 3+2im 8+7im 9+2im 4+6im julia> A 2×2 Array{Complex{Int64},2}: 3+2im 9+2im 8+7im 4+6imsource
LinearAlgebra.Transpose
Type
Transpose
Lazy wrapper type for a transpose view of the underlying linear algebra object, usually an AbstractVector
/AbstractMatrix
, but also some Factorization
, for instance. Usually, the Transpose
constructor should not be called directly, use transpose
instead. To materialize the view use copy
.
This type is intended for linear algebra usage  for general data manipulation see permutedims
.
Examples
julia> A = [3+2im 9+2im; 8+7im 4+6im] 2×2 Array{Complex{Int64},2}: 3+2im 9+2im 8+7im 4+6im julia> transpose(A) 2×2 Transpose{Complex{Int64},Array{Complex{Int64},2}}: 3+2im 8+7im 9+2im 4+6imsource
Base.adjoint
Function
adjoint(A)
Lazy adjoint (conjugate transposition) (also postfix '
). Note that adjoint
is applied recursively to elements.
This operation is intended for linear algebra usage  for general data manipulation see permutedims
.
Examples
julia> A = [3+2im 9+2im; 8+7im 4+6im] 2×2 Array{Complex{Int64},2}: 3+2im 9+2im 8+7im 4+6im julia> adjoint(A) 2×2 Adjoint{Complex{Int64},Array{Complex{Int64},2}}: 32im 87im 92im 46imsource
LinearAlgebra.adjoint!
Function
adjoint!(dest,src)
Conjugate transpose array src
and store the result in the preallocated array dest
, which should have a size corresponding to (size(src,2),size(src,1))
. No inplace transposition is supported and unexpected results will happen if src
and dest
have overlapping memory regions.
Examples
julia> A = [3+2im 9+2im; 8+7im 4+6im] 2×2 Array{Complex{Int64},2}: 3+2im 9+2im 8+7im 4+6im julia> B = zeros(Complex{Int64}, 2, 2) 2×2 Array{Complex{Int64},2}: 0+0im 0+0im 0+0im 0+0im julia> adjoint!(B, A); julia> B 2×2 Array{Complex{Int64},2}: 32im 87im 92im 46im julia> A 2×2 Array{Complex{Int64},2}: 3+2im 9+2im 8+7im 4+6imsource
LinearAlgebra.Adjoint
Type
Adjoint
Lazy wrapper type for an adjoint view of the underlying linear algebra object, usually an AbstractVector
/AbstractMatrix
, but also some Factorization
, for instance. Usually, the Adjoint
constructor should not be called directly, use adjoint
instead. To materialize the view use copy
.
This type is intended for linear algebra usage  for general data manipulation see permutedims
.
Examples
julia> A = [3+2im 9+2im; 8+7im 4+6im] 2×2 Array{Complex{Int64},2}: 3+2im 9+2im 8+7im 4+6im julia> adjoint(A) 2×2 Adjoint{Complex{Int64},Array{Complex{Int64},2}}: 32im 87im 92im 46imsource
Base.copy
Method
copy(A::Transpose) copy(A::Adjoint)
Eagerly evaluate the lazy matrix transpose/adjoint. Note that the transposition is applied recursively to elements.
This operation is intended for linear algebra usage  for general data manipulation see permutedims
, which is nonrecursive.
Examples
julia> A = [1 2im; 3im 4] 2×2 Array{Complex{Int64},2}: 1+0im 0+2im 03im 4+0im julia> T = transpose(A) 2×2 Transpose{Complex{Int64},Array{Complex{Int64},2}}: 1+0im 03im 0+2im 4+0im julia> copy(T) 2×2 Array{Complex{Int64},2}: 1+0im 03im 0+2im 4+0imsource
LinearAlgebra.stride1
Function
stride1(A) > Int
Return the distance between successive array elements in dimension 1 in units of element size.
Examples
julia> A = [1,2,3,4] 4element Array{Int64,1}: 1 2 3 4 julia> LinearAlgebra.stride1(A) 1 julia> B = view(A, 2:2:4) 2element view(::Array{Int64,1}, 2:2:4) with eltype Int64: 2 4 julia> LinearAlgebra.stride1(B) 2source
LinearAlgebra.checksquare
Function
LinearAlgebra.checksquare(A)
Check that a matrix is square, then return its common dimension. For multiple arguments, return a vector.
Examples
julia> A = fill(1, (4,4)); B = fill(1, (5,5)); julia> LinearAlgebra.checksquare(A, B) 2element Array{Int64,1}: 4 5source
LinearAlgebra.peakflops
Function
LinearAlgebra.peakflops(n::Integer=2000; parallel::Bool=false)
peakflops
computes the peak flop rate of the computer by using double precision gemm!
. By default, if no arguments are specified, it multiplies a matrix of size n x n
, where n = 2000
. If the underlying BLAS is using multiple threads, higher flop rates are realized. The number of BLAS threads can be set with BLAS.set_num_threads(n)
.
If the keyword argument parallel
is set to true
, peakflops
is run in parallel on all the worker processors. The flop rate of the entire parallel computer is returned. When running in parallel, only 1 BLAS thread is used. The argument n
still refers to the size of the problem that is solved on each processor.
This function requires at least Julia 1.1. In Julia 1.0 it is available from the standard library InteractiveUtils
.
Lowlevel matrix operations
In many cases there are inplace versions of matrix operations that allow you to supply a preallocated output vector or matrix. This is useful when optimizing critical code in order to avoid the overhead of repeated allocations. These inplace operations are suffixed with !
below (e.g. mul!
) according to the usual Julia convention.
LinearAlgebra.mul!
Function
mul!(Y, A, B) > Y
Calculates the matrixmatrix or matrixvector product $AB$ and stores the result in Y
, overwriting the existing value of Y
. Note that Y
must not be aliased with either A
or B
.
Examples
julia> A=[1.0 2.0; 3.0 4.0]; B=[1.0 1.0; 1.0 1.0]; Y = similar(B); mul!(Y, A, B); julia> Y 2×2 Array{Float64,2}: 3.0 3.0 7.0 7.0
Implementation
For custom matrix and vector types, it is recommended to implement 5argument mul!
rather than implementing 3argument mul!
directly if possible.
mul!(C, A, B, α, β) > C
Combined inplace matrixmatrix or matrixvector multiplyadd $A B α + C β$. The result is stored in C
by overwriting it. Note that C
must not be aliased with either A
or B
.
Fiveargument mul!
requires at least Julia 1.3.
Examples
julia> A=[1.0 2.0; 3.0 4.0]; B=[1.0 1.0; 1.0 1.0]; C=[1.0 2.0; 3.0 4.0]; julia> mul!(C, A, B, 100.0, 10.0) === C true julia> C 2×2 Array{Float64,2}: 310.0 320.0 730.0 740.0source
LinearAlgebra.lmul!
Function
lmul!(a::Number, B::AbstractArray)
Scale an array B
by a scalar a
overwriting B
inplace. Use rmul!
to multiply scalar from right. The scaling operation respects the semantics of the multiplication *
between a
and an element of B
. In particular, this also applies to multiplication involving nonfinite numbers such as NaN
and ±Inf
.
Prior to Julia 1.1, NaN
and ±Inf
entries in B
were treated inconsistently.
Examples
julia> B = [1 2; 3 4] 2×2 Array{Int64,2}: 1 2 3 4 julia> lmul!(2, B) 2×2 Array{Int64,2}: 2 4 6 8 julia> lmul!(0.0, [Inf]) 1element Array{Float64,1}: NaNsource
lmul!(A, B)
Calculate the matrixmatrix product $AB$, overwriting B
, and return the result. Here, A
must be of special matrix type, like, e.g., Diagonal
, UpperTriangular
or LowerTriangular
, or of some orthogonal type, see QR
.
Examples
julia> B = [0 1; 1 0]; julia> A = LinearAlgebra.UpperTriangular([1 2; 0 3]); julia> LinearAlgebra.lmul!(A, B); julia> B 2×2 Array{Int64,2}: 2 1 3 0 julia> B = [1.0 2.0; 3.0 4.0]; julia> F = qr([0 1; 1 0]); julia> lmul!(F.Q, B) 2×2 Array{Float64,2}: 3.0 4.0 1.0 2.0source
LinearAlgebra.rmul!
Function
rmul!(A::AbstractArray, b::Number)
Scale an array A
by a scalar b
overwriting A
inplace. Use lmul!
to multiply scalar from left. The scaling operation respects the semantics of the multiplication *
between an element of A
and b
. In particular, this also applies to multiplication involving nonfinite numbers such as NaN
and ±Inf
.
Prior to Julia 1.1, NaN
and ±Inf
entries in A
were treated inconsistently.
Examples
julia> A = [1 2; 3 4] 2×2 Array{Int64,2}: 1 2 3 4 julia> rmul!(A, 2) 2×2 Array{Int64,2}: 2 4 6 8 julia> rmul!([NaN], 0.0) 1element Array{Float64,1}: NaNsource
rmul!(A, B)
Calculate the matrixmatrix product $AB$, overwriting A
, and return the result. Here, B
must be of special matrix type, like, e.g., Diagonal
, UpperTriangular
or LowerTriangular
, or of some orthogonal type, see QR
.
Examples
julia> A = [0 1; 1 0]; julia> B = LinearAlgebra.UpperTriangular([1 2; 0 3]); julia> LinearAlgebra.rmul!(A, B); julia> A 2×2 Array{Int64,2}: 0 3 1 2 julia> A = [1.0 2.0; 3.0 4.0]; julia> F = qr([0 1; 1 0]); julia> rmul!(A, F.Q) 2×2 Array{Float64,2}: 2.0 1.0 4.0 3.0source
LinearAlgebra.ldiv!
Function
ldiv!(Y, A, B) > Y
Compute A \ B
inplace and store the result in Y
, returning the result.
The argument A
should not be a matrix. Rather, instead of matrices it should be a factorization object (e.g. produced by factorize
or cholesky
). The reason for this is that factorization itself is both expensive and typically allocates memory (although it can also be done inplace via, e.g., lu!
), and performancecritical situations requiring ldiv!
usually also require finegrained control over the factorization of A
.
Examples
julia> A = [1 2.2 4; 3.1 0.2 3; 4 1 2]; julia> X = [1; 2.5; 3]; julia> Y = zero(X); julia> ldiv!(Y, qr(A), X); julia> Y 3element Array{Float64,1}: 0.7128099173553719 0.051652892561983674 0.10020661157024757 julia> A\X 3element Array{Float64,1}: 0.7128099173553719 0.05165289256198333 0.10020661157024785source
ldiv!(A, B)
Compute A \ B
inplace and overwriting B
to store the result.
The argument A
should not be a matrix. Rather, instead of matrices it should be a factorization object (e.g. produced by factorize
or cholesky
). The reason for this is that factorization itself is both expensive and typically allocates memory (although it can also be done inplace via, e.g., lu!
), and performancecritical situations requiring ldiv!
usually also require finegrained control over the factorization of A
.
Examples
julia> A = [1 2.2 4; 3.1 0.2 3; 4 1 2]; julia> X = [1; 2.5; 3]; julia> Y = copy(X); julia> ldiv!(qr(A), X); julia> X 3element Array{Float64,1}: 0.7128099173553719 0.051652892561983674 0.10020661157024757 julia> A\Y 3element Array{Float64,1}: 0.7128099173553719 0.05165289256198333 0.10020661157024785source
ldiv!(a::Number, B::AbstractArray)
Divide each entry in an array B
by a scalar a
overwriting B
inplace. Use rdiv!
to divide scalar from right.
Examples
julia> B = [1.0 2.0; 3.0 4.0] 2×2 Array{Float64,2}: 1.0 2.0 3.0 4.0 julia> ldiv!(2.0, B) 2×2 Array{Float64,2}: 0.5 1.0 1.5 2.0source
LinearAlgebra.rdiv!
Function
rdiv!(A, B)
Compute A / B
inplace and overwriting A
to store the result.
The argument B
should not be a matrix. Rather, instead of matrices it should be a factorization object (e.g. produced by factorize
or cholesky
). The reason for this is that factorization itself is both expensive and typically allocates memory (although it can also be done inplace via, e.g., lu!
), and performancecritical situations requiring rdiv!
usually also require finegrained control over the factorization of B
.
rdiv!(A::AbstractArray, b::Number)
Divide each entry in an array A
by a scalar b
overwriting A
inplace. Use ldiv!
to divide scalar from left.
Examples
julia> A = [1.0 2.0; 3.0 4.0] 2×2 Array{Float64,2}: 1.0 2.0 3.0 4.0 julia> rdiv!(A, 2.0) 2×2 Array{Float64,2}: 0.5 1.0 1.5 2.0source
BLAS Functions
In Julia (as in much of scientific computation), dense linearalgebra operations are based on the LAPACK library, which in turn is built on top of basic linearalgebra buildingblocks known as the BLAS. There are highly optimized implementations of BLAS available for every computer architecture, and sometimes in highperformance linear algebra routines it is useful to call the BLAS functions directly.
LinearAlgebra.BLAS
provides wrappers for some of the BLAS functions. Those BLAS functions that overwrite one of the input arrays have names ending in '!'
. Usually, a BLAS function has four methods defined, for Float64
, Float32
, ComplexF64
, and ComplexF32
arrays.
BLAS Character Arguments
Many BLAS functions accept arguments that determine whether to transpose an argument (trans
), which triangle of a matrix to reference (uplo
or ul
), whether the diagonal of a triangular matrix can be assumed to be all ones (dA
) or which side of a matrix multiplication the input argument belongs on (side
). The possibilities are:
Multplication Order
side 
Meaning 

'L' 
The argument goes on the left side of a matrixmatrix operation. 
'R' 
The argument goes on the right side of a matrixmatrix operation. 
Triangle Referencing
uplo /ul

Meaning 

'U' 
Only the upper triangle of the matrix will be used. 
'L' 
Only the lower triangle of the matrix will be used. 
Transposition Operation
trans /tX

Meaning 

'N' 
The input matrix X is not transposed or conjugated. 
'T' 
The input matrix X will be transposed. 
'C' 
The input matrix X will be conjugated and transposed. 
Unit Diagonal
diag /dX

Meaning 

'N' 
The diagonal values of the matrix X will be read. 
'U' 
The diagonal of the matrix X is assumed to be all ones. 
LinearAlgebra.BLAS
Module
Interface to BLAS subroutines.
source
LinearAlgebra.BLAS.dot
Function
dot(n, X, incx, Y, incy)
Dot product of two vectors consisting of n
elements of array X
with stride incx
and n
elements of array Y
with stride incy
.
Examples
julia> BLAS.dot(10, fill(1.0, 10), 1, fill(1.0, 20), 2) 10.0source
LinearAlgebra.BLAS.dotu
Function
dotu(n, X, incx, Y, incy)
Dot function for two complex vectors consisting of n
elements of array X
with stride incx
and n
elements of array Y
with stride incy
.
Examples
julia> BLAS.dotu(10, fill(1.0im, 10), 1, fill(1.0+im, 20), 2) 10.0 + 10.0imsource
LinearAlgebra.BLAS.dotc
Function
dotc(n, X, incx, U, incy)
Dot function for two complex vectors, consisting of n
elements of array X
with stride incx
and n
elements of array U
with stride incy
, conjugating the first vector.
Examples
julia> BLAS.dotc(10, fill(1.0im, 10), 1, fill(1.0+im, 20), 2) 10.0  10.0imsource
LinearAlgebra.BLAS.blascopy!
Function
blascopy!(n, X, incx, Y, incy)
Copy n
elements of array X
with stride incx
to array Y
with stride incy
. Returns Y
.
LinearAlgebra.BLAS.nrm2
Function
nrm2(n, X, incx)
2norm of a vector consisting of n
elements of array X
with stride incx
.
Examples
julia> BLAS.nrm2(4, fill(1.0, 8), 2) 2.0 julia> BLAS.nrm2(1, fill(1.0, 8), 2) 1.0source
LinearAlgebra.BLAS.asum
Function
asum(n, X, incx)
Sum of the absolute values of the first n
elements of array X
with stride incx
.
Examples
julia> BLAS.asum(5, fill(1.0im, 10), 2) 5.0 julia> BLAS.asum(2, fill(1.0im, 10), 5) 2.0source
LinearAlgebra.axpy!
Function
axpy!(a, X, Y)
Overwrite Y
with a*X + Y
, where a
is a scalar. Return Y
.
Examples
julia> x = [1; 2; 3]; julia> y = [4; 5; 6]; julia> BLAS.axpy!(2, x, y) 3element Array{Int64,1}: 6 9 12source
LinearAlgebra.axpby!
Function
axpby!(a, X, b, Y)
Overwrite Y
with X*a + Y*b
, where a
and b
are scalars. Return Y
.
Examples
julia> x = [1., 2, 3]; julia> y = [4., 5, 6]; julia> BLAS.axpby!(2., x, 3., y) 3element Array{Float64,1}: 14.0 19.0 24.0source
LinearAlgebra.BLAS.scal!
Function
scal!(n, a, X, incx)
Overwrite X
with a*X
for the first n
elements of array X
with stride incx
. Returns X
.
LinearAlgebra.BLAS.scal
Function
scal(n, a, X, incx)
Return X
scaled by a
for the first n
elements of array X
with stride incx
.
LinearAlgebra.BLAS.iamax
Function
iamax(n, dx, incx) iamax(dx)
Find the index of the element of dx
with the maximum absolute value. n
is the length of dx
, and incx
is the stride. If n
and incx
are not provided, they assume default values of n=length(dx)
and incx=stride1(dx)
.
LinearAlgebra.BLAS.ger!
Function
ger!(alpha, x, y, A)
Rank1 update of the matrix A
with vectors x
and y
as alpha*x*y' + A
.
LinearAlgebra.BLAS.syr!
Function
syr!(uplo, alpha, x, A)
Rank1 update of the symmetric matrix A
with vector x
as alpha*x*transpose(x) + A
. uplo
controls which triangle of A
is updated. Returns A
.
LinearAlgebra.BLAS.syrk!
Function
syrk!(uplo, trans, alpha, A, beta, C)
Rankk update of the symmetric matrix C
as alpha*A*transpose(A) + beta*C
or alpha*transpose(A)*A + beta*C
according to trans
. Only the uplo
triangle of C
is used. Returns C
.
LinearAlgebra.BLAS.syrk
Function
syrk(uplo, trans, alpha, A)
Returns either the upper triangle or the lower triangle of A
, according to uplo
, of alpha*A*transpose(A)
or alpha*transpose(A)*A
, according to trans
.
LinearAlgebra.BLAS.her!
Function
her!(uplo, alpha, x, A)
Methods for complex arrays only. Rank1 update of the Hermitian matrix A
with vector x
as alpha*x*x' + A
. uplo
controls which triangle of A
is updated. Returns A
.
LinearAlgebra.BLAS.herk!
Function
herk!(uplo, trans, alpha, A, beta, C)
Methods for complex arrays only. Rankk update of the Hermitian matrix C
as alpha*A*A' + beta*C
or alpha*A'*A + beta*C
according to trans
. Only the uplo
triangle of C
is updated. Returns C
.
LinearAlgebra.BLAS.herk
Function
herk(uplo, trans, alpha, A)
Methods for complex arrays only. Returns the uplo
triangle of alpha*A*A'
or alpha*A'*A
, according to trans
.
LinearAlgebra.BLAS.gbmv!
Function
gbmv!(trans, m, kl, ku, alpha, A, x, beta, y)
Update vector y
as alpha*A*x + beta*y
or alpha*A'*x + beta*y
according to trans
. The matrix A
is a general band matrix of dimension m
by size(A,2)
with kl
subdiagonals and ku
superdiagonals. alpha
and beta
are scalars. Return the updated y
.
LinearAlgebra.BLAS.gbmv
Function
gbmv(trans, m, kl, ku, alpha, A, x)
Return alpha*A*x
or alpha*A'*x
according to trans
. The matrix A
is a general band matrix of dimension m
by size(A,2)
with kl
subdiagonals and ku
superdiagonals, and alpha
is a scalar.
LinearAlgebra.BLAS.sbmv!
Function
sbmv!(uplo, k, alpha, A, x, beta, y)
Update vector y
as alpha*A*x + beta*y
where A
is a a symmetric band matrix of order size(A,2)
with k
superdiagonals stored in the argument A
. The storage layout for A
is described the reference BLAS module, level2 BLAS at http://www.netlib.org/lapack/explorehtml/. Only the uplo
triangle of A
is used.
Return the updated y
.
LinearAlgebra.BLAS.sbmv
Method
sbmv(uplo, k, alpha, A, x)
Return alpha*A*x
where A
is a symmetric band matrix of order size(A,2)
with k
superdiagonals stored in the argument A
. Only the uplo
triangle of A
is used.
LinearAlgebra.BLAS.sbmv
Method
sbmv(uplo, k, A, x)
Return A*x
where A
is a symmetric band matrix of order size(A,2)
with k
superdiagonals stored in the argument A
. Only the uplo
triangle of A
is used.
LinearAlgebra.BLAS.gemm!
Function
gemm!(tA, tB, alpha, A, B, beta, C)
Update C
as alpha*A*B + beta*C
or the other three variants according to tA
and tB
. Return the updated C
.
LinearAlgebra.BLAS.gemm
Method
gemm(tA, tB, alpha, A, B)
Return alpha*A*B
or the other three variants according to tA
and tB
.
LinearAlgebra.BLAS.gemm
Method
gemm(tA, tB, A, B)
Return A*B
or the other three variants according to tA
and tB
.
LinearAlgebra.BLAS.gemv!
Function
gemv!(tA, alpha, A, x, beta, y)
Update the vector y
as alpha*A*x + beta*y
or alpha*A'x + beta*y
according to tA
. alpha
and beta
are scalars. Return the updated y
.
LinearAlgebra.BLAS.gemv
Method
gemv(tA, alpha, A, x)
Return alpha*A*x
or alpha*A'x
according to tA
. alpha
is a scalar.
LinearAlgebra.BLAS.gemv
Method
gemv(tA, A, x)
Return A*x
or A'x
according to tA
.
LinearAlgebra.BLAS.symm!
Function
symm!(side, ul, alpha, A, B, beta, C)
Update C
as alpha*A*B + beta*C
or alpha*B*A + beta*C
according to side
. A
is assumed to be symmetric. Only the ul
triangle of A
is used. Return the updated C
.
LinearAlgebra.BLAS.symm
Method
symm(side, ul, alpha, A, B)
Return alpha*A*B
or alpha*B*A
according to side
. A
is assumed to be symmetric. Only the ul
triangle of A
is used.
LinearAlgebra.BLAS.symm
Method
symm(side, ul, A, B)
Return A*B
or B*A
according to side
. A
is assumed to be symmetric. Only the ul
triangle of A
is used.
LinearAlgebra.BLAS.symv!
Function
symv!(ul, alpha, A, x, beta, y)
Update the vector y
as alpha*A*x + beta*y
. A
is assumed to be symmetric. Only the ul
triangle of A
is used. alpha
and beta
are scalars. Return the updated y
.
LinearAlgebra.BLAS.symv
Method
symv(ul, alpha, A, x)
Return alpha*A*x
. A
is assumed to be symmetric. Only the ul
triangle of A
is used. alpha
is a scalar.
LinearAlgebra.BLAS.symv
Method
symv(ul, A, x)
Return A*x
. A
is assumed to be symmetric. Only the ul
triangle of A
is used.
LinearAlgebra.BLAS.trmm!
Function
trmm!(side, ul, tA, dA, alpha, A, B)
Update B
as alpha*A*B
or one of the other three variants determined by side
and tA
. Only the ul
triangle of A
is used. dA
determines if the diagonal values are read or are assumed to be all ones. Returns the updated B
.
LinearAlgebra.BLAS.trmm
Function
trmm(side, ul, tA, dA, alpha, A, B)
Returns alpha*A*B
or one of the other three variants determined by side
and tA
. Only the ul
triangle of A
is used. dA
determines if the diagonal values are read or are assumed to be all ones.
LinearAlgebra.BLAS.trsm!
Function
trsm!(side, ul, tA, dA, alpha, A, B)
Overwrite B
with the solution to A*X = alpha*B
or one of the other three variants determined by side
and tA
. Only the ul
triangle of A
is used. dA
determines if the diagonal values are read or are assumed to be all ones. Returns the updated B
.
LinearAlgebra.BLAS.trsm
Function
trsm(side, ul, tA, dA, alpha, A, B)
Return the solution to A*X = alpha*B
or one of the other three variants determined by determined by side
and tA
. Only the ul
triangle of A
is used. dA
determines if the diagonal values are read or are assumed to be all ones.
LinearAlgebra.BLAS.trmv!
Function
trmv!(ul, tA, dA, A, b)
Return op(A)*b
, where op
is determined by tA
. Only the ul
triangle of A
is used. dA
determines if the diagonal values are read or are assumed to be all ones. The multiplication occurs inplace on b
.
LinearAlgebra.BLAS.trmv
Function
trmv(ul, tA, dA, A, b)
Return op(A)*b
, where op
is determined by tA
. Only the ul
triangle of A
is used. dA
determines if the diagonal values are read or are assumed to be all ones.
LinearAlgebra.BLAS.trsv!
Function
trsv!(ul, tA, dA, A, b)
Overwrite b
with the solution to A*x = b
or one of the other two variants determined by tA
and ul
. dA
determines if the diagonal values are read or are assumed to be all ones. Return the updated b
.
LinearAlgebra.BLAS.trsv
Function
trsv(ul, tA, dA, A, b)
Return the solution to A*x = b
or one of the other two variants determined by tA
and ul
. dA
determines if the diagonal values are read or are assumed to be all ones.
LinearAlgebra.BLAS.set_num_threads
Function
set_num_threads(n)
Set the number of threads the BLAS library should use.
source
LinearAlgebra.I
Constant
I
An object of type UniformScaling
, representing an identity matrix of any size.
Examples
julia> fill(1, (5,6)) * I == fill(1, (5,6)) true julia> [1 2im 3; 1im 2 3] * I 2×3 Array{Complex{Int64},2}: 1+0im 0+2im 3+0im 0+1im 2+0im 3+0imsource
LAPACK Functions
LinearAlgebra.LAPACK
provides wrappers for some of the LAPACK functions for linear algebra. Those functions that overwrite one of the input arrays have names ending in '!'
.
Usually a function has 4 methods defined, one each for Float64
, Float32
, ComplexF64
and ComplexF32
arrays.
Note that the LAPACK API provided by Julia can and will change in the future. Since this API is not userfacing, there is no commitment to support/deprecate this specific set of functions in future releases.
LinearAlgebra.LAPACK
Module
Interfaces to LAPACK subroutines.
source
LinearAlgebra.LAPACK.gbtrf!
Function
gbtrf!(kl, ku, m, AB) > (AB, ipiv)
Compute the LU factorization of a banded matrix AB
. kl
is the first subdiagonal containing a nonzero band, ku
is the last superdiagonal containing one, and m
is the first dimension of the matrix AB
. Returns the LU factorization inplace and ipiv
, the vector of pivots used.
LinearAlgebra.LAPACK.gbtrs!
Function
gbtrs!(trans, kl, ku, m, AB, ipiv, B)
Solve the equation AB * X = B
. trans
determines the orientation of AB
. It may be N
(no transpose), T
(transpose), or C
(conjugate transpose). kl
is the first subdiagonal containing a nonzero band, ku
is the last superdiagonal containing one, and m
is the first dimension of the matrix AB
. ipiv
is the vector of pivots returned from gbtrf!
. Returns the vector or matrix X
, overwriting B
inplace.
LinearAlgebra.LAPACK.gebal!
Function
gebal!(job, A) > (ilo, ihi, scale)
Balance the matrix A
before computing its eigensystem or Schur factorization. job
can be one of N
(A
will not be permuted or scaled), P
(A
will only be permuted), S
(A
will only be scaled), or B
(A
will be both permuted and scaled). Modifies A
inplace and returns ilo
, ihi
, and scale
. If permuting was turned on, A[i,j] = 0
if j > i
and 1 < j < ilo
or j > ihi
. scale
contains information about the scaling/permutations performed.
LinearAlgebra.LAPACK.gebak!
Function
gebak!(job, side, ilo, ihi, scale, V)
Transform the eigenvectors V
of a matrix balanced using gebal!
to the unscaled/unpermuted eigenvectors of the original matrix. Modifies V
inplace. side
can be L
(left eigenvectors are transformed) or R
(right eigenvectors are transformed).
LinearAlgebra.LAPACK.gebrd!
Function
gebrd!(A) > (A, d, e, tauq, taup)
Reduce A
inplace to bidiagonal form A = QBP'
. Returns A
, containing the bidiagonal matrix B
; d
, containing the diagonal elements of B
; e
, containing the offdiagonal elements of B
; tauq
, containing the elementary reflectors representing Q
; and taup
, containing the elementary reflectors representing P
.
LinearAlgebra.LAPACK.gelqf!
Function
gelqf!(A, tau)
Compute the LQ
factorization of A
, A = LQ
. tau
contains scalars which parameterize the elementary reflectors of the factorization. tau
must have length greater than or equal to the smallest dimension of A
.
Returns A
and tau
modified inplace.
gelqf!(A) > (A, tau)
Compute the LQ
factorization of A
, A = LQ
.
Returns A
, modified inplace, and tau
, which contains scalars which parameterize the elementary reflectors of the factorization.
LinearAlgebra.LAPACK.geqlf!
Function
geqlf!(A, tau)
Compute the QL
factorization of A
, A = QL
. tau
contains scalars which parameterize the elementary reflectors of the factorization. tau
must have length greater than or equal to the smallest dimension of A
.
Returns A
and tau
modified inplace.
geqlf!(A) > (A, tau)
Compute the QL
factorization of A
, A = QL
.
Returns A
, modified inplace, and tau
, which contains scalars which parameterize the elementary reflectors of the factorization.
LinearAlgebra.LAPACK.geqrf!
Function
geqrf!(A, tau)
Compute the QR
factorization of A
, A = QR
. tau
contains scalars which parameterize the elementary reflectors of the factorization. tau
must have length greater than or equal to the smallest dimension of A
.
Returns A
and tau
modified inplace.
geqrf!(A) > (A, tau)
Compute the QR
factorization of A
, A = QR
.
Returns A
, modified inplace, and tau
, which contains scalars which parameterize the elementary reflectors of the factorization.
LinearAlgebra.LAPACK.geqp3!
Function
geqp3!(A, jpvt, tau)
Compute the pivoted QR
factorization of A
, AP = QR
using BLAS level 3. P
is a pivoting matrix, represented by jpvt
. tau
stores the elementary reflectors. jpvt
must have length length greater than or equal to n
if A
is an (m x n)
matrix. tau
must have length greater than or equal to the smallest dimension of A
.
A
, jpvt
, and tau
are modified inplace.
geqp3!(A, jpvt) > (A, jpvt, tau)
Compute the pivoted QR
factorization of A
, AP = QR
using BLAS level 3. P
is a pivoting matrix, represented by jpvt
. jpvt
must have length greater than or equal to n
if A
is an (m x n)
matrix.
Returns A
and jpvt
, modified inplace, and tau
, which stores the elementary reflectors.
geqp3!(A) > (A, jpvt, tau)
Compute the pivoted QR
factorization of A
, AP = QR
using BLAS level 3.
Returns A
, modified inplace, jpvt
, which represents the pivoting matrix P
, and tau
, which stores the elementary reflectors.
LinearAlgebra.LAPACK.gerqf!
Function
gerqf!(A, tau)
Compute the RQ
factorization of A
, A = RQ
. tau
contains scalars which parameterize the elementary reflectors of the factorization. tau
must have length greater than or equal to the smallest dimension of A
.
Returns A
and tau
modified inplace.
gerqf!(A) > (A, tau)
Compute the RQ
factorization of A
, A = RQ
.
Returns A
, modified inplace, and tau
, which contains scalars which parameterize the elementary reflectors of the factorization.
LinearAlgebra.LAPACK.geqrt!
Function
geqrt!(A, T)
Compute the blocked QR
factorization of A
, A = QR
. T
contains upper triangular block reflectors which parameterize the elementary reflectors of the factorization. The first dimension of T
sets the block size and it must be between 1 and n
. The second dimension of T
must equal the smallest dimension of A
.
Returns A
and T
modified inplace.
geqrt!(A, nb) > (A, T)
Compute the blocked QR
factorization of A
, A = QR
. nb
sets the block size and it must be between 1 and n
, the second dimension of A
.
Returns A
, modified inplace, and T
, which contains upper triangular block reflectors which parameterize the elementary reflectors of the factorization.
LinearAlgebra.LAPACK.geqrt3!
Function
geqrt3!(A, T)
Recursively computes the blocked QR
factorization of A
, A = QR
. T
contains upper triangular block reflectors which parameterize the elementary reflectors of the factorization. The first dimension of T
sets the block size and it must be between 1 and n
. The second dimension of T
must equal the smallest dimension of A
.
Returns A
and T
modified inplace.
geqrt3!(A) > (A, T)
Recursively computes the blocked QR
factorization of A
, A = QR
.
Returns A
, modified inplace, and T
, which contains upper triangular block reflectors which parameterize the elementary reflectors of the factorization.
LinearAlgebra.LAPACK.getrf!
Function
getrf!(A) > (A, ipiv, info)
Compute the pivoted LU
factorization of A
, A = LU
.
Returns A
, modified inplace, ipiv
, the pivoting information, and an info
code which indicates success (info = 0
), a singular value in U
(info = i
, in which case U[i,i]
is singular), or an error code (info < 0
).
LinearAlgebra.LAPACK.tzrzf!
Function
tzrzf!(A) > (A, tau)
Transforms the upper trapezoidal matrix A
to upper triangular form inplace. Returns A
and tau
, the scalar parameters for the elementary reflectors of the transformation.
LinearAlgebra.LAPACK.ormrz!
Function
ormrz!(side, trans, A, tau, C)
Multiplies the matrix C
by Q
from the transformation supplied by tzrzf!
. Depending on side
or trans
the multiplication can be leftsided (side = L, Q*C
) or rightsided (side = R, C*Q
) and Q
can be unmodified (trans = N
), transposed (trans = T
), or conjugate transposed (trans = C
). Returns matrix C
which is modified inplace with the result of the multiplication.
LinearAlgebra.LAPACK.gels!
Function
gels!(trans, A, B) > (F, B, ssr)
Solves the linear equation A * X = B
, transpose(A) * X = B
, or adjoint(A) * X = B
using a QR or LQ factorization. Modifies the matrix/vector B
in place with the solution. A
is overwritten with its QR
or LQ
factorization. trans
may be one of N
(no modification), T
(transpose), or C
(conjugate transpose). gels!
searches for the minimum norm/least squares solution. A
may be under or over determined. The solution is returned in B
.
LinearAlgebra.LAPACK.gesv!
Function
gesv!(A, B) > (B, A, ipiv)
Solves the linear equation A * X = B
where A
is a square matrix using the LU
factorization of A
. A
is overwritten with its LU
factorization and B
is overwritten with the solution X
. ipiv
contains the pivoting information for the LU
factorization of A
.
LinearAlgebra.LAPACK.getrs!
Function
getrs!(trans, A, ipiv, B)
Solves the linear equation A * X = B
, transpose(A) * X = B
, or adjoint(A) * X = B
for square A
. Modifies the matrix/vector B
in place with the solution. A
is the LU
factorization from getrf!
, with ipiv
the pivoting information. trans
may be one of N
(no modification), T
(transpose), or C
(conjugate transpose).
LinearAlgebra.LAPACK.getri!
Function
getri!(A, ipiv)
Computes the inverse of A
, using its LU
factorization found by getrf!
. ipiv
is the pivot information output and A
contains the LU
factorization of getrf!
. A
is overwritten with its inverse.
LinearAlgebra.LAPACK.gesvx!
Function
gesvx!(fact, trans, A, AF, ipiv, equed, R, C, B) > (X, equed, R, C, B, rcond, ferr, berr, work)
Solves the linear equation A * X = B
(trans = N
), transpose(A) * X = B
(trans = T
), or adjoint(A) * X = B
(trans = C
) using the LU
factorization of A
. fact
may be E
, in which case A
will be equilibrated and copied to AF
; F
, in which case AF
and ipiv
from a previous LU
factorization are inputs; or N
, in which case A
will be copied to AF
and then factored. If fact = F
, equed
may be N
, meaning A
has not been equilibrated; R
, meaning A
was multiplied by Diagonal(R)
from the left; C
, meaning A
was multiplied by Diagonal(C)
from the right; or B
, meaning A
was multiplied by Diagonal(R)
from the left and Diagonal(C)
from the right. If fact = F
and equed = R
or B
the elements of R
must all be positive. If fact = F
and equed = C
or B
the elements of C
must all be positive.
Returns the solution X
; equed
, which is an output if fact
is not N
, and describes the equilibration that was performed; R
, the row equilibration diagonal; C
, the column equilibration diagonal; B
, which may be overwritten with its equilibrated form Diagonal(R)*B
(if trans = N
and equed = R,B
) or Diagonal(C)*B
(if trans = T,C
and equed = C,B
); rcond
, the reciprocal condition number of A
after equilbrating; ferr
, the forward error bound for each solution vector in X
; berr
, the forward error bound for each solution vector in X
; and work
, the reciprocal pivot growth factor.
gesvx!(A, B)
The noequilibration, notranspose simplification of gesvx!
.
LinearAlgebra.LAPACK.gelsd!
Function
gelsd!(A, B, rcond) > (B, rnk)
Computes the least norm solution of A * X = B
by finding the SVD
factorization of A
, then dividingandconquering the problem. B
is overwritten with the solution X
. Singular values below rcond
will be treated as zero. Returns the solution in B
and the effective rank of A
in rnk
.
LinearAlgebra.LAPACK.gelsy!
Function
gelsy!(A, B, rcond) > (B, rnk)
Computes the least norm solution of A * X = B
by finding the full QR
factorization of A
, then dividingandconquering the problem. B
is overwritten with the solution X
. Singular values below rcond
will be treated as zero. Returns the solution in B
and the effective rank of A
in rnk
.
LinearAlgebra.LAPACK.gglse!
Function
gglse!(A, c, B, d) > (X,res)
Solves the equation A * x = c
where x
is subject to the equality constraint B * x = d
. Uses the formula c  A*x^2 = 0
to solve. Returns X
and the residual sumofsquares.
LinearAlgebra.LAPACK.geev!
Function
geev!(jobvl, jobvr, A) > (W, VL, VR)
Finds the eigensystem of A
. If jobvl = N
, the left eigenvectors of A
aren't computed. If jobvr = N
, the right eigenvectors of A
aren't computed. If jobvl = V
or jobvr = V
, the corresponding eigenvectors are computed. Returns the eigenvalues in W
, the right eigenvectors in VR
, and the left eigenvectors in VL
.
LinearAlgebra.LAPACK.gesdd!
Function
gesdd!(job, A) > (U, S, VT)
Finds the singular value decomposition of A
, A = U * S * V'
, using a divide and conquer approach. If job = A
, all the columns of U
and the rows of V'
are computed. If job = N
, no columns of U
or rows of V'
are computed. If job = O
, A
is overwritten with the columns of (thin) U
and the rows of (thin) V'
. If job = S
, the columns of (thin) U
and the rows of (thin) V'
are computed and returned separately.
LinearAlgebra.LAPACK.gesvd!
Function
gesvd!(jobu, jobvt, A) > (U, S, VT)
Finds the singular value decomposition of A
, A = U * S * V'
. If jobu = A
, all the columns of U
are computed. If jobvt = A
all the rows of V'
are computed. If jobu = N
, no columns of U
are computed. If jobvt = N
no rows of V'
are computed. If jobu = O
, A
is overwritten with the columns of (thin) U
. If jobvt = O
, A
is overwritten with the rows of (thin) V'
. If jobu = S
, the columns of (thin) U
are computed and returned separately. If jobvt = S
the rows of (thin) V'
are computed and returned separately. jobu
and jobvt
can't both be O
.
Returns U
, S
, and Vt
, where S
are the singular values of A
.
LinearAlgebra.LAPACK.ggsvd!
Function
ggsvd!(jobu, jobv, jobq, A, B) > (U, V, Q, alpha, beta, k, l, R)
Finds the generalized singular value decomposition of A
and B
, U'*A*Q = D1*R
and V'*B*Q = D2*R
. D1
has alpha
on its diagonal and D2
has beta
on its diagonal. If jobu = U
, the orthogonal/unitary matrix U
is computed. If jobv = V
the orthogonal/unitary matrix V
is computed. If jobq = Q
, the orthogonal/unitary matrix Q
is computed. If jobu
, jobv
or jobq
is N
, that matrix is not computed. This function is only available in LAPACK versions prior to 3.6.0.
LinearAlgebra.LAPACK.ggsvd3!
Function
ggsvd3!(jobu, jobv, jobq, A, B) > (U, V, Q, alpha, beta, k, l, R)
Finds the generalized singular value decomposition of A
and B
, U'*A*Q = D1*R
and V'*B*Q = D2*R
. D1
has alpha
on its diagonal and D2
has beta
on its diagonal. If jobu = U
, the orthogonal/unitary matrix U
is computed. If jobv = V
the orthogonal/unitary matrix V
is computed. If jobq = Q
, the orthogonal/unitary matrix Q
is computed. If jobu
, jobv
, or jobq
is N
, that matrix is not computed. This function requires LAPACK 3.6.0.
LinearAlgebra.LAPACK.geevx!
Function
geevx!(balanc, jobvl, jobvr, sense, A) > (A, w, VL, VR, ilo, ihi, scale, abnrm, rconde, rcondv)
Finds the eigensystem of A
with matrix balancing. If jobvl = N
, the left eigenvectors of A
aren't computed. If jobvr = N
, the right eigenvectors of A
aren't computed. If jobvl = V
or jobvr = V
, the corresponding eigenvectors are computed. If balanc = N
, no balancing is performed. If balanc = P
, A
is permuted but not scaled. If balanc = S
, A
is scaled but not permuted. If balanc = B
, A
is permuted and scaled. If sense = N
, no reciprocal condition numbers are computed. If sense = E
, reciprocal condition numbers are computed for the eigenvalues only. If sense = V
, reciprocal condition numbers are computed for the right eigenvectors only. If sense = B
, reciprocal condition numbers are computed for the right eigenvectors and the eigenvectors. If sense = E,B
, the right and left eigenvectors must be computed.
LinearAlgebra.LAPACK.ggev!
Function
ggev!(jobvl, jobvr, A, B) > (alpha, beta, vl, vr)
Finds the generalized eigendecomposition of A
and B
. If jobvl = N
, the left eigenvectors aren't computed. If jobvr = N
, the right eigenvectors aren't computed. If jobvl = V
or jobvr = V
, the corresponding eigenvectors are computed.
LinearAlgebra.LAPACK.gtsv!
Function
gtsv!(dl, d, du, B)
Solves the equation A * X = B
where A
is a tridiagonal matrix with dl
on the subdiagonal, d
on the diagonal, and du
on the superdiagonal.
Overwrites B
with the solution X
and returns it.
LinearAlgebra.LAPACK.gttrf!
Function
gttrf!(dl, d, du) > (dl, d, du, du2, ipiv)
Finds the LU
factorization of a tridiagonal matrix with dl
on the subdiagonal, d
on the diagonal, and du
on the superdiagonal.
Modifies dl
, d
, and du
inplace and returns them and the second superdiagonal du2
and the pivoting vector ipiv
.
LinearAlgebra.LAPACK.gttrs!
Function
gttrs!(trans, dl, d, du, du2, ipiv, B)
Solves the equation A * X = B
(trans = N
), transpose(A) * X = B
(trans = T
), or adjoint(A) * X = B
(trans = C
) using the LU
factorization computed by gttrf!
. B
is overwritten with the solution X
.
LinearAlgebra.LAPACK.orglq!
Function
orglq!(A, tau, k = length(tau))
Explicitly finds the matrix Q
of a LQ
factorization after calling gelqf!
on A
. Uses the output of gelqf!
. A
is overwritten by Q
.
LinearAlgebra.LAPACK.orgqr!
Function
orgqr!(A, tau, k = length(tau))
Explicitly finds the matrix Q
of a QR
factorization after calling geqrf!
on A
. Uses the output of geqrf!
. A
is overwritten by Q
.
LinearAlgebra.LAPACK.orgql!
Function
orgql!(A, tau, k = length(tau))
Explicitly finds the matrix Q
of a QL
factorization after calling geqlf!
on A
. Uses the output of geqlf!
. A
is overwritten by Q
.
LinearAlgebra.LAPACK.orgrq!
Function
orgrq!(A, tau, k = length(tau))
Explicitly finds the matrix Q
of a RQ
factorization after calling gerqf!
on A
. Uses the output of gerqf!
. A
is overwritten by Q
.
LinearAlgebra.LAPACK.ormlq!
Function
ormlq!(side, trans, A, tau, C)
Computes Q * C
(trans = N
), transpose(Q) * C
(trans = T
), adjoint(Q) * C
(trans = C
) for side = L
or the equivalent rightsided multiplication for side = R
using Q
from a LQ
factorization of A
computed using gelqf!
. C
is overwritten.
LinearAlgebra.LAPACK.ormqr!
Function
ormqr!(side, trans, A, tau, C)
Computes Q * C
(trans = N
), transpose(Q) * C
(trans = T
), adjoint(Q) * C
(trans = C
) for side = L
or the equivalent rightsided multiplication for side = R
using Q
from a QR
factorization of A
computed using geqrf!
. C
is overwritten.
LinearAlgebra.LAPACK.ormql!
Function
ormql!(side, trans, A, tau, C)
Computes Q * C
(trans = N
), transpose(Q) * C
(trans = T
), adjoint(Q) * C
(trans = C
) for side = L
or the equivalent rightsided multiplication for side = R
using Q
from a QL
factorization of A
computed using geqlf!
. C
is overwritten.
LinearAlgebra.LAPACK.ormrq!
Function
ormrq!(side, trans, A, tau, C)
Computes Q * C
(trans = N
), transpose(Q) * C
(trans = T
), adjoint(Q) * C
(trans = C
) for side = L
or the equivalent rightsided multiplication for side = R
using Q
from a RQ
factorization of A
computed using gerqf!
. C
is overwritten.
LinearAlgebra.LAPACK.gemqrt!
Function
gemqrt!(side, trans, V, T, C)
Computes Q * C
(trans = N
), transpose(Q) * C
(trans = T
), adjoint(Q) * C
(trans = C
) for side = L
or the equivalent rightsided multiplication for side = R
using Q
from a QR
factorization of A
computed using geqrt!
. C
is overwritten.
LinearAlgebra.LAPACK.posv!
Function
posv!(uplo, A, B) > (A, B)
Finds the solution to A * X = B
where A
is a symmetric or Hermitian positive definite matrix. If uplo = U
the upper Cholesky decomposition of A
is computed. If uplo = L
the lower Cholesky decomposition of A
is computed. A
is overwritten by its Cholesky decomposition. B
is overwritten with the solution X
.
LinearAlgebra.LAPACK.potrf!
Function
potrf!(uplo, A)
Computes the Cholesky (upper if uplo = U
, lower if uplo = L
) decomposition of positivedefinite matrix A
. A
is overwritten and returned with an info code.
LinearAlgebra.LAPACK.potri!
Function
potri!(uplo, A)
Computes the inverse of positivedefinite matrix A
after calling potrf!
to find its (upper if uplo = U
, lower if uplo = L
) Cholesky decomposition.
A
is overwritten by its inverse and returned.
LinearAlgebra.LAPACK.potrs!
Function
potrs!(uplo, A, B)
Finds the solution to A * X = B
where A
is a symmetric or Hermitian positive definite matrix whose Cholesky decomposition was computed by potrf!
. If uplo = U
the upper Cholesky decomposition of A
was computed. If uplo = L
the lower Cholesky decomposition of A
was computed. B
is overwritten with the solution X
.
LinearAlgebra.LAPACK.pstrf!
Function
pstrf!(uplo, A, tol) > (A, piv, rank, info)
Computes the (upper if uplo = U
, lower if uplo = L
) pivoted Cholesky decomposition of positivedefinite matrix A
with a userset tolerance tol
. A
is overwritten by its Cholesky decomposition.
Returns A
, the pivots piv
, the rank of A
, and an info
code. If info = 0
, the factorization succeeded. If info = i > 0
, then A
is indefinite or rankdeficient.
LinearAlgebra.LAPACK.ptsv!
Function
ptsv!(D, E, B)
Solves A * X = B
for positivedefinite tridiagonal A
. D
is the diagonal of A
and E
is the offdiagonal. B
is overwritten with the solution X
and returned.
LinearAlgebra.LAPACK.pttrf!
Function
pttrf!(D, E)
Computes the LDLt factorization of a positivedefinite tridiagonal matrix with D
as diagonal and E
as offdiagonal. D
and E
are overwritten and returned.
LinearAlgebra.LAPACK.pttrs!
Function
pttrs!(D, E, B)
Solves A * X = B
for positivedefinite tridiagonal A
with diagonal D
and offdiagonal E
after computing A
's LDLt factorization using pttrf!
. B
is overwritten with the solution X
.
LinearAlgebra.LAPACK.trtri!
Function
trtri!(uplo, diag, A)
Finds the inverse of (upper if uplo = U
, lower if uplo = L
) triangular matrix A
. If diag = N
, A
has nonunit diagonal elements. If diag = U
, all diagonal elements of A
are one. A
is overwritten with its inverse.
LinearAlgebra.LAPACK.trtrs!
Function
trtrs!(uplo, trans, diag, A, B)
Solves A * X = B
(trans = N
), transpose(A) * X = B
(trans = T
), or adjoint(A) * X = B
(trans = C
) for (upper if uplo = U
, lower if uplo = L
) triangular matrix A
. If diag = N
, A
has nonunit diagonal elements. If diag = U
, all diagonal elements of A
are one. B
is overwritten with the solution X
.
LinearAlgebra.LAPACK.trcon!
Function
trcon!(norm, uplo, diag, A)
Finds the reciprocal condition number of (upper if uplo = U
, lower if uplo = L
) triangular matrix A
. If diag = N
, A
has nonunit diagonal elements. If diag = U
, all diagonal elements of A
are one. If norm = I
, the condition number is found in the infinity norm. If norm = O
or 1
, the condition number is found in the one norm.
LinearAlgebra.LAPACK.trevc!
Function
trevc!(side, howmny, select, T, VL = similar(T), VR = similar(T))
Finds the eigensystem of an upper triangular matrix T
. If side = R
, the right eigenvectors are computed. If side = L
, the left eigenvectors are computed. If side = B
, both sets are computed. If howmny = A
, all eigenvectors are found. If howmny = B
, all eigenvectors are found and backtransformed using VL
and VR
. If howmny = S
, only the eigenvectors corresponding to the values in select
are computed.
LinearAlgebra.LAPACK.trrfs!
Function
trrfs!(uplo, trans, diag, A, B, X, Ferr, Berr) > (Ferr, Berr)
Estimates the error in the solution to A * X = B
(trans = N
), transpose(A) * X = B
(trans = T
), adjoint(A) * X = B
(trans = C
) for side = L
, or the equivalent equations a righthanded side = R
X * A
after computing X
using trtrs!
. If uplo = U
, A
is upper triangular. If uplo = L
, A
is lower triangular. If diag = N
, A
has nonunit diagonal elements. If diag = U
, all diagonal elements of A
are one. Ferr
and Berr
are optional inputs. Ferr
is the forward error and Berr
is the backward error, each componentwise.
LinearAlgebra.LAPACK.stev!
Function
stev!(job, dv, ev) > (dv, Zmat)
Computes the eigensystem for a symmetric tridiagonal matrix with dv
as diagonal and ev
as offdiagonal. If job = N
only the eigenvalues are found and returned in dv
. If job = V
then the eigenvectors are also found and returned in Zmat
.
LinearAlgebra.LAPACK.stebz!
Function
stebz!(range, order, vl, vu, il, iu, abstol, dv, ev) > (dv, iblock, isplit)
Computes the eigenvalues for a symmetric tridiagonal matrix with dv
as diagonal and ev
as offdiagonal. If range = A
, all the eigenvalues are found. If range = V
, the eigenvalues in the halfopen interval (vl, vu]
are found. If range = I
, the eigenvalues with indices between il
and iu
are found. If order = B
, eigvalues are ordered within a block. If order = E
, they are ordered across all the blocks. abstol
can be set as a tolerance for convergence.
LinearAlgebra.LAPACK.stegr!
Function
stegr!(jobz, range, dv, ev, vl, vu, il, iu) > (w, Z)
Computes the eigenvalues (jobz = N
) or eigenvalues and eigenvectors (jobz = V
) for a symmetric tridiagonal matrix with dv
as diagonal and ev
as offdiagonal. If range = A
, all the eigenvalues are found. If range = V
, the eigenvalues in the halfopen interval (vl, vu]
are found. If range = I
, the eigenvalues with indices between il
and iu
are found. The eigenvalues are returned in w
and the eigenvectors in Z
.
LinearAlgebra.LAPACK.stein!
Function
stein!(dv, ev_in, w_in, iblock_in, isplit_in)
Computes the eigenvectors for a symmetric tridiagonal matrix with dv
as diagonal and ev_in
as offdiagonal. w_in
specifies the input eigenvalues for which to find corresponding eigenvectors. iblock_in
specifies the submatrices corresponding to the eigenvalues in w_in
. isplit_in
specifies the splitting points between the submatrix blocks.
LinearAlgebra.LAPACK.syconv!
Function
syconv!(uplo, A, ipiv) > (A, work)
Converts a symmetric matrix A
(which has been factorized into a triangular matrix) into two matrices L
and D
. If uplo = U
, A
is upper triangular. If uplo = L
, it is lower triangular. ipiv
is the pivot vector from the triangular factorization. A
is overwritten by L
and D
.
LinearAlgebra.LAPACK.sysv!
Function
sysv!(uplo, A, B) > (B, A, ipiv)
Finds the solution to A * X = B
for symmetric matrix A
. If uplo = U
, the upper half of A
is stored. If uplo = L
, the lower half is stored. B
is overwritten by the solution X
. A
is overwritten by its BunchKaufman factorization. ipiv
contains pivoting information about the factorization.
LinearAlgebra.LAPACK.sytrf!
Function
sytrf!(uplo, A) > (A, ipiv, info)
Computes the BunchKaufman factorization of a symmetric matrix A
. If uplo = U
, the upper half of A
is stored. If uplo = L
, the lower half is stored.
Returns A
, overwritten by the factorization, a pivot vector ipiv
, and the error code info
which is a nonnegative integer. If info
is positive the matrix is singular and the diagonal part of the factorization is exactly zero at position info
.
LinearAlgebra.LAPACK.sytri!
Function
sytri!(uplo, A, ipiv)
Computes the inverse of a symmetric matrix A
using the results of sytrf!
. If uplo = U
, the upper half of A
is stored. If uplo = L
, the lower half is stored. A
is overwritten by its inverse.
LinearAlgebra.LAPACK.sytrs!
Function
sytrs!(uplo, A, ipiv, B)
Solves the equation A * X = B
for a symmetric matrix A
using the results of sytrf!
. If uplo = U
, the upper half of A
is stored. If uplo = L
, the lower half is stored. B
is overwritten by the solution X
.
LinearAlgebra.LAPACK.hesv!
Function
hesv!(uplo, A, B) > (B, A, ipiv)
Finds the solution to A * X = B
for Hermitian matrix A
. If uplo = U
, the upper half of A
is stored. If uplo = L
, the lower half is stored. B
is overwritten by the solution X
. A
is overwritten by its BunchKaufman factorization. ipiv
contains pivoting information about the factorization.
LinearAlgebra.LAPACK.hetrf!
Function
hetrf!(uplo, A) > (A, ipiv, info)
Computes the BunchKaufman factorization of a Hermitian matrix A
. If uplo = U
, the upper half of A
is stored. If uplo = L
, the lower half is stored.
Returns A
, overwritten by the factorization, a pivot vector ipiv
, and the error code info
which is a nonnegative integer. If info
is positive the matrix is singular and the diagonal part of the factorization is exactly zero at position info
.
LinearAlgebra.LAPACK.hetri!
Function
hetri!(uplo, A, ipiv)
Computes the inverse of a Hermitian matrix A
using the results of sytrf!
. If uplo = U
, the upper half of A
is stored. If uplo = L
, the lower half is stored. A
is overwritten by its inverse.
LinearAlgebra.LAPACK.hetrs!
Function
hetrs!(uplo, A, ipiv, B)
Solves the equation A * X = B
for a Hermitian matrix A
using the results of sytrf!
. If uplo = U
, the upper half of A
is stored. If uplo = L
, the lower half is stored. B
is overwritten by the solution X
.
LinearAlgebra.LAPACK.syev!
Function
syev!(jobz, uplo, A)
Finds the eigenvalues (jobz = N
) or eigenvalues and eigenvectors (jobz = V
) of a symmetric matrix A
. If uplo = U
, the upper triangle of A
is used. If uplo = L
, the lower triangle of A
is used.
LinearAlgebra.LAPACK.syevr!
Function
syevr!(jobz, range, uplo, A, vl, vu, il, iu, abstol) > (W, Z)
Finds the eigenvalues (jobz = N
) or eigenvalues and eigenvectors (jobz = V
) of a symmetric matrix A
. If uplo = U
, the upper triangle of A
is used. If uplo = L
, the lower triangle of A
is used. If range = A
, all the eigenvalues are found. If range = V
, the eigenvalues in the halfopen interval (vl, vu]
are found. If range = I
, the eigenvalues with indices between il
and iu
are found. abstol
can be set as a tolerance for convergence.
The eigenvalues are returned in W
and the eigenvectors in Z
.
LinearAlgebra.LAPACK.sygvd!
Function
sygvd!(itype, jobz, uplo, A, B) > (w, A, B)
Finds the generalized eigenvalues (jobz = N
) or eigenvalues and eigenvectors (jobz = V
) of a symmetric matrix A
and symmetric positivedefinite matrix B
. If uplo = U
, the upper triangles of A
and B
are used. If uplo = L
, the lower triangles of A
and B
are used. If itype = 1
, the problem to solve is A * x = lambda * B * x
. If itype = 2
, the problem to solve is A * B * x = lambda * x
. If itype = 3
, the problem to solve is B * A * x = lambda * x
.
LinearAlgebra.LAPACK.bdsqr!
Function
bdsqr!(uplo, d, e_, Vt, U, C) > (d, Vt, U, C)
Computes the singular value decomposition of a bidiagonal matrix with d
on the diagonal and e_
on the offdiagonal. If uplo = U
, e_
is the superdiagonal. If uplo = L
, e_
is the subdiagonal. Can optionally also compute the product Q' * C
.
Returns the singular values in d
, and the matrix C
overwritten with Q' * C
.
LinearAlgebra.LAPACK.bdsdc!
Function
bdsdc!(uplo, compq, d, e_) > (d, e, u, vt, q, iq)
Computes the singular value decomposition of a bidiagonal matrix with d
on the diagonal and e_
on the offdiagonal using a divide and conqueq method. If uplo = U
, e_
is the superdiagonal. If uplo = L
, e_
is the subdiagonal. If compq = N
, only the singular values are found. If compq = I
, the singular values and vectors are found. If compq = P
, the singular values and vectors are found in compact form. Only works for real types.
Returns the singular values in d
, and if compq = P
, the compact singular vectors in iq
.
LinearAlgebra.LAPACK.gecon!
Function
gecon!(normtype, A, anorm)
Finds the reciprocal condition number of matrix A
. If normtype = I
, the condition number is found in the infinity norm. If normtype = O
or 1
, the condition number is found in the one norm. A
must be the result of getrf!
and anorm
is the norm of A
in the relevant norm.
LinearAlgebra.LAPACK.gehrd!
Function
gehrd!(ilo, ihi, A) > (A, tau)
Converts a matrix A
to Hessenberg form. If A
is balanced with gebal!
then ilo
and ihi
are the outputs of gebal!
. Otherwise they should be ilo = 1
and ihi = size(A,2)
. tau
contains the elementary reflectors of the factorization.
LinearAlgebra.LAPACK.orghr!
Function
orghr!(ilo, ihi, A, tau)
Explicitly finds Q
, the orthogonal/unitary matrix from gehrd!
. ilo
, ihi
, A
, and tau
must correspond to the input/output to gehrd!
.
LinearAlgebra.LAPACK.gees!
Function
gees!(jobvs, A) > (A, vs, w)
Computes the eigenvalues (jobvs = N
) or the eigenvalues and Schur vectors (jobvs = V
) of matrix A
. A
is overwritten by its Schur form.
Returns A
, vs
containing the Schur vectors, and w
, containing the eigenvalues.
LinearAlgebra.LAPACK.gges!
Function
gges!(jobvsl, jobvsr, A, B) > (A, B, alpha, beta, vsl, vsr)
Computes the generalized eigenvalues, generalized Schur form, left Schur vectors (jobsvl = V
), or right Schur vectors (jobvsr = V
) of A
and B
.
The generalized eigenvalues are returned in alpha
and beta
. The left Schur vectors are returned in vsl
and the right Schur vectors are returned in vsr
.
LinearAlgebra.LAPACK.trexc!
Function
trexc!(compq, ifst, ilst, T, Q) > (T, Q)
Reorder the Schur factorization of a matrix. If compq = V
, the Schur vectors Q
are reordered. If compq = N
they are not modified. ifst
and ilst
specify the reordering of the vectors.
LinearAlgebra.LAPACK.trsen!
Function
trsen!(compq, job, select, T, Q) > (T, Q, w, s, sep)
Reorder the Schur factorization of a matrix and optionally finds reciprocal condition numbers. If job = N
, no condition numbers are found. If job = E
, only the condition number for this cluster of eigenvalues is found. If job = V
, only the condition number for the invariant subspace is found. If job = B
then the condition numbers for the cluster and subspace are found. If compq = V
the Schur vectors Q
are updated. If compq = N
the Schur vectors are not modified. select
determines which eigenvalues are in the cluster.
Returns T
, Q
, reordered eigenvalues in w
, the condition number of the cluster of eigenvalues s
, and the condition number of the invariant subspace sep
.
LinearAlgebra.LAPACK.tgsen!
Function
tgsen!(select, S, T, Q, Z) > (S, T, alpha, beta, Q, Z)
Reorders the vectors of a generalized Schur decomposition. select
specifies the eigenvalues in each cluster.
LinearAlgebra.LAPACK.trsyl!
Function
trsyl!(transa, transb, A, B, C, isgn=1) > (C, scale)
Solves the Sylvester matrix equation A * X +/ X * B = scale*C
where A
and B
are both quasiupper triangular. If transa = N
, A
is not modified. If transa = T
, A
is transposed. If transa = C
, A
is conjugate transposed. Similarly for transb
and B
. If isgn = 1
, the equation A * X + X * B = scale * C
is solved. If isgn = 1
, the equation A * X  X * B = scale * C
is solved.
Returns X
(overwriting C
) and scale
.
© 2009–2020 Jeff Bezanson, Stefan Karpinski, Viral B. Shah, and other contributors
Licensed under the MIT License.
https://docs.julialang.org/en/v1.3.1/stdlib/LinearAlgebra/