.. module:: Base.LinAlg
.. currentmodule:: Base
Linear algebra functions in Julia are largely implemented by calling functions from LAPACK. Sparse factorizations call functions from SuiteSparse.
.. function:: *(A, B) :noindex: Matrix multiplication
.. function:: \\(A, B) :noindex: 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``. A direct solver is used for upper- or lower triangular ``A``. For Hermitian ``A`` (equivalent to symmetric ``A`` for non-complex ``A``) the BunchKaufman factorization is used. Otherwise an LU factorization is used. For rectangular ``A`` the result is the minimum-norm least squares solution computed by reducing ``A`` to bidiagonal form and solving the bidiagonal least squares problem. For sparse, square ``A`` the LU factorization (from UMFPACK) is used.
.. function:: dot(x, y) Compute the dot product
.. function:: cross(x, y) Compute the cross product of two 3-vectors
.. function:: norm(a) Compute the norm of a ``Vector`` or a ``Matrix``
.. function:: rref(A) Compute the reduced row echelon form of the matrix A.
.. function:: factorize(A) Compute a convenient factorization (including LU, Cholesky, Bunch Kaufman, Triangular) of A, based upon the type of the input matrix. The return value can then be reused for efficient solving of multiple systems. For example: ``A=factorize(A); x=A\\b; y=A\\C``.
.. function:: factorize!(A) ``factorize!`` is the same as :func:`factorize`, but saves space by overwriting the input A, instead of creating a copy.
.. function:: lu(A) -> L, U, p Compute the LU factorization of ``A``, such that ``A[p,:] = L*U``.
.. function:: lufact(A) -> LU Compute the LU factorization of ``A``, returning an ``LU`` object for dense ``A`` or an ``UmfpackLU`` object for sparse ``A``. The individual components of the factorization ``F`` can be accesed by indexing: ``F[:L]``, ``F[:U]``, and ``F[:P]`` (permutation matrix) or ``F[:p]`` (permutation vector). An ``UmfpackLU`` object has additional components ``F[:q]`` (the left permutation vector) and ``Rs`` the vector of scaling factors. The following functions are available for both ``LU`` and ``UmfpackLU`` objects: ``size``, ``\`` and ``det``. For ``LU`` there is also an ``inv`` method. The sparse LU factorization is such that ``L*U`` is equal to``scale(Rs,A)[p,q]``.
.. function:: lufact!(A) -> LU ``lufact!`` is the same as :func:`lufact`, but saves space by overwriting the input A, instead of creating a copy. For sparse ``A`` the ``nzval`` field is not overwritten but the index fields, ``colptr`` and ``rowval`` are decremented in place, converting from 1-based indices to 0-based indices.
.. function:: chol(A, [LU]) -> F Compute Cholesky factorization of a symmetric positive-definite matrix ``A`` and return the matrix ``F``. If ``LU`` is ``:L`` (Lower), ``A = L*L'``. If ``LU`` is ``:U`` (Upper), ``A = R'*R``.
.. function:: cholfact(A, [LU]) -> Cholesky Compute the Cholesky factorization of a dense symmetric positive-definite matrix ``A`` and return a ``Cholesky`` object. ``LU`` may be ``:L`` for using the lower part or ``:U`` for the upper part. The default is to use ``:U``. The triangular matrix can be obtained from the factorization ``F`` with: ``F[:L]`` and ``F[:U]``. The following functions are available for ``Cholesky`` objects: ``size``, ``\``, ``inv``, ``det``. A ``LAPACK.PosDefException`` error is thrown in case the matrix is not positive definite.
.. function:: cholfact(A, [ll]) -> CholmodFactor Compute the sparse Cholesky factorization of a sparse matrix ``A``. If ``A`` is Hermitian its Cholesky factor is determined. If ``A`` is not Hermitian the Cholesky factor of ``A*A'`` is determined. A fill-reducing permutation is used. Methods for ``size``, ``solve``, ``\``, ``findn_nzs``, ``diag``, ``det`` and ``logdet``. One of the solve methods includes an integer argument that can be used to solve systems involving parts of the factorization only. The optional boolean argument, ``ll`` determines whether the factorization returned is of the ``A[p,p] = L*L'`` form, where ``L`` is lower triangular or ``A[p,p] = scale(L,D)*L'`` form where ``L`` is unit lower triangular and ``D`` is a non-negative vector. The default is LDL.
.. function:: cholfact!(A, [LU]) -> Cholesky ``cholfact!`` is the same as :func:`cholfact`, but saves space by overwriting the input A, instead of creating a copy.
.. function:: cholpfact(A, [LU]) -> CholeskyPivoted Compute the pivoted Cholesky factorization of a symmetric positive semi-definite matrix ``A`` and return a ``CholeskyPivoted`` object. ``LU`` may be ``:L`` for using the lower part or ``:U`` for the upper part. The default is to use ``:U``. The triangular factors contained in the factorization ``F`` can be obtained with ``F[:L]`` and ``F[:U]``, whereas the permutation can be obtained with ``F[:P]`` or ``F[:p]``. The following functions are available for ``CholeskyPivoted`` objects: ``size``, ``\``, ``inv``, ``det``. A ``LAPACK.RankDeficientException`` error is thrown in case the matrix is rank deficient.
.. function:: cholpfact!(A, [LU]) -> CholeskyPivoted ``cholpfact!`` is the same as ``cholpfact``, but saves space by overwriting the input A, instead of creating a copy.
.. function:: qr(A, [thin]) -> Q, R Compute the QR factorization of ``A`` such that ``A = Q*R``. Also see ``qrfact``. The default is to compute a thin factorization. Note that `R` is not extended with zeros when the full `Q` is requested.
.. function:: qrfact(A) Computes the QR factorization of ``A`` and returns a ``QR`` type, which is a ``Factorization`` ``F`` consisting of an orthogonal matrix ``F[:Q]`` and a triangular matrix ``F[:R]``. The following functions are available for ``QR`` objects: ``size``, ``\``. The orthogonal matrix ``Q=F[:Q]`` is a ``QRPackedQ`` type which has the ``*`` operator overloaded to support efficient multiplication by ``Q`` and ``Q'``. Multiplication with respect to either thin or full ``Q`` is allowed, i.e. both ``F[:Q]*F[:R]`` and ``F[:Q]*A`` are supported. A ``QRPackedQ`` matrix can be converted into a regular matrix with ``full``.
.. function:: qrfact!(A) ``qrfact!`` is the same as :func:`qrfact`, but saves space by overwriting the input A, instead of creating a copy.
.. function:: qrp(A, [thin]) -> Q, R, p Computes the QR factorization of ``A`` with pivoting, such that ``A[:,p] = Q*R``, Also see ``qrpfact``. The default is to compute a thin factorization.
.. function:: qrpfact(A) -> QRPivoted Computes the QR factorization of ``A`` with pivoting and returns a ``QRPivoted`` object, which is a ``Factorization`` ``F`` consisting of an orthogonal matrix ``F[:Q]``, a triangular matrix ``F[:R]``, and a permutation ``F[:p]`` (or its matrix representation ``F[:P]``). The following functions are available for ``QRPivoted`` objects: ``size``, ``\``. The orthogonal matrix ``Q=F[:Q]`` is a ``QRPivotedQ`` type which has the ``*`` operator overloaded to support efficient multiplication by ``Q`` and ``Q'``. Multiplication with respect to either the thin or full ``Q`` is allowed, i.e. both ``F[:Q]*F[:R]`` and ``F[:Q]*A`` are supperted. A ``QRPivotedQ`` matrix can be converted into a regular matrix with ``full``.
.. function:: qrpfact!(A) -> QRPivoted ``qrpfact!`` is the same as :func:`qrpfact`, but saves space by overwriting the input A, instead of creating a copy.
.. function:: bkfact(A) -> BunchKaufman Compute the Bunch Kaufman factorization of a real symmetric or complex Hermitian matrix ``A`` and return a ``BunchKaufman`` object. The following functions are available for ``BunchKaufman`` objects: ``size``, ``\``, ``inv``, ``issym``, ``ishermitian``.
.. function:: bkfact!(A) -> BunchKaufman ``bkfact!`` is the same as :func:`bkfact`, but saves space by overwriting the input A, instead of creating a copy.
.. function:: sqrtm(A) Compute the matrix square root of ``A``. If ``B = sqrtm(A)``, then ``B*B == A`` within roundoff error.
.. function:: eig(A) -> D, V Compute eigenvalues and eigenvectors of A
.. function:: eig(A, B) -> D, V Compute generalized eigenvalues and vectors of A and B
.. function:: eigvals(A) Returns the eigenvalues of ``A``.
.. function:: eigmax(A) Returns the largest eigenvalue of ``A``.
.. function:: eigmin(A) Returns the smallest eigenvalue of ``A``.
.. function:: eigvecs(A, [eigvals]) Returns the eigenvectors of ``A``. For SymTridiagonal matrices, if the optional vector of eigenvalues ``eigvals`` is specified, returns the specific corresponding eigenvectors.
.. function:: eigfact(A) Compute the eigenvalue decomposition of ``A`` and return an ``Eigen`` object. If ``F`` is the factorization object, the eigenvalues can be accessed with ``F[:values]`` and the eigenvectors with ``F[:vectors]``. The following functions are available for ``Eigen`` objects: ``inv``, ``det``.
.. function:: eigfact(A, B) Compute the generalized eigenvalue decomposition of ``A`` and ``B`` and return an ``GeneralizedEigen`` object. If ``F`` is the factorization object, the eigenvalues can be accessed with ``F[:values]`` and the eigenvectors with ``F[:vectors]``.
.. function:: eigfact!(A, [B]) ``eigfact!`` is the same as :func:`eigfact`, but saves space by overwriting the input A (and B), instead of creating a copy.
.. function:: hessfact(A) 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]`` 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 ``full``.
.. function:: hessfact!(A) ``hessfact!`` is the same as :func:`hessfact`, but saves space by overwriting the input A, instead of creating a copy.
.. function:: schurfact(A) -> 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 unitary/orthogonal 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]``.
.. function:: schurfact!(A) Computer the Schur factorization of A, overwriting A in the process. See :func:`schurfact`
.. function:: schur(A) -> Schur[:T], Schur[:Z], Schur[:values] See schurfact
.. function:: schurfact(A, B) -> 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[:alpha]./F[:beta]``.
.. function:: schur(A,B) -> GeneralizedSchur[:S], GeneralizedSchur[:T], GeneralizedSchur[:Q], GeneralizedSchur[:Z] See schurfact
.. function:: svdfact(A, [thin]) -> 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*diagm(S)*Vt``. If ``thin`` is ``true``, an economy mode decomposition is returned. The algorithm produces ``Vt`` and hence ``Vt`` is more efficient to extract than ``V``. The default is to produce a thin decomposition.
.. function:: svdfact!(A, [thin]) -> SVD ``svdfact!`` is the same as :func:`svdfact`, but saves space by overwriting the input A, instead of creating a copy. If ``thin`` is ``true``, an economy mode decomposition is returned. The default is to produce a thin decomposition.
.. function:: svd(A, [thin]) -> U, S, V Compute the SVD of A, returning ``U``, vector ``S``, and ``V`` such that ``A == U*diagm(S)*V'``. If ``thin`` is ``true``, an economy mode decomposition is returned.
.. function:: svdvals(A) Returns the singular values of ``A``.
.. function:: svdvals!(A) Returns the singular values of ``A``, while saving space by overwriting the input.
.. function:: svdfact(A, B) -> GeneralizedSVD Compute the generalized SVD of ``A`` and ``B``, returning a ``GeneralizedSVD`` Factorization object, such that ``A = U*D1*R0*Q'`` and ``B = V*D2*R0*Q'``.
.. function:: svd(A, B) -> U, V, Q, D1, D2, R0 Compute the generalized SVD of ``A`` and ``B``, returning ``U``, ``V``, ``Q``, ``D1``, ``D2``, and ``R0`` such that ``A = U*D1*R0*Q'`` and ``B = V*D2*R0*Q'``.
.. function:: svdvals(A, B) Return only the singular values from the generalized singular value decomposition of ``A`` and ``B``.
.. function:: triu(M) Upper triangle of a matrix.
.. function:: triu!(M) Upper triangle of a matrix, overwriting M in the process.
.. function:: tril(M) Lower triangle of a matrix.
.. function:: tril!(M) Lower triangle of a matrix, overwriting M in the process.
.. function:: diagind(M[, k]) A ``Range`` giving the indices of the ``k``-th diagonal of the matrix ``M``.
.. function:: diag(M[, k]) The ``k``-th diagonal of a matrix, as a vector.
.. function:: diagm(v[, k]) Construct a diagonal matrix and place ``v`` on the ``k``-th diagonal.
.. function:: scale(A, b), scale(b, A) Scale an array ``A`` by a scalar ``b``, returning a new array. If ``A`` is a matrix and ``b`` is a vector, then ``scale!(A,b)`` scales each column ``i`` of ``A`` by ``b[i]`` (similar to ``A*diagm(b)``), while ``scale!(b,A)`` scales each row ``i`` of ``A`` by ``b[i]`` (similar to ``diagm(b)*A``), returning a new array. Note: for large ``A``, ``scale`` can be much faster than ``A .* b`` or ``b .* A``, due to the use of BLAS.
.. function:: scale!(A, b), scale!(b, A) Scale an array ``A`` by a scalar ``b``, similar to ``scale`` but overwriting ``A`` in-place. If ``A`` is a matrix and ``b`` is a vector, then ``scale!(A,b)`` scales each column ``i`` of ``A`` by ``b[i]`` (similar to ``A*diagm(b)``), while ``scale!(b,A)`` scales each row ``i`` of ``A`` by ``b[i]`` (similar to ``diagm(b)*A``), again operating in-place on ``A``.
.. function:: symmetrize!(A[, UL::Char])
``symmetrize!(A)`` converts from the BLAS/LAPACK symmetric storage
format, in which only the ``UL`` ('U'pper or 'L'ower, default 'U')
triangle is used, to a full symmetric matrix.
.. function:: Tridiagonal(dl, d, du) Construct a tridiagonal matrix from the lower diagonal, diagonal, and upper diagonal, respectively. The result is of type ``Tridiagonal`` and provides efficient specialized linear solvers, but may be converted into a regular matrix with ``full``.
.. function:: Bidiagonal(dv, ev, isupper) Constructs an upper (isupper=true) or lower (isupper=false) bidiagonal matrix using the given diagonal (dv) and off-diagonal (ev) vectors. The result is of type ``Bidiagonal`` and provides efficient specialized linear solvers, but may be converted into a regular matrix with ``full``.
.. function:: SymTridiagonal(d, du) Construct a real-symmetric tridiagonal matrix from the diagonal and upper diagonal, respectively. The result is of type ``SymTridiagonal`` and provides efficient specialized eigensolvers, but may be converted into a regular matrix with ``full``.
.. function:: Woodbury(A, U, C, V) Construct a matrix in a form suitable for applying the Woodbury matrix identity
.. function:: rank(M) Compute the rank of a matrix
.. function:: norm(A, [p]) Compute the ``p``-norm of a vector or a matrix. ``p`` is ``2`` by default, if not provided. If ``A`` is a vector, ``norm(A, p)`` computes the ``p``-norm. ``norm(A, Inf)`` returns the largest value in ``abs(A)``, whereas ``norm(A, -Inf)`` returns the smallest. If ``A`` is a matrix, valid values for ``p`` are ``1``, ``2``, or ``Inf``. In order to compute the Frobenius norm, use ``normfro``.
.. function:: normfro(A) Compute the Frobenius norm of a matrix ``A``.
.. function:: cond(M, [p]) Matrix condition number, computed using the p-norm. ``p`` is 2 by default, if not provided. Valid values for ``p`` are ``1``, ``2``, or ``Inf``.
.. function:: trace(M) Matrix trace
.. function:: det(M) Matrix determinant
.. function:: logdet(M) Log of Matrix determinant. Equivalent to ``log(det(M))``, but may provide increased accuracy and/or speed.
.. function:: inv(M) Matrix inverse
.. function:: pinv(M) Moore-Penrose inverse
.. function:: null(M) Basis for null space of M.
.. function:: repmat(A, n, m) Construct a matrix by repeating the given matrix ``n`` times in dimension 1 and ``m`` times in dimension 2.
.. function:: repeat(A, inner = Int[], outer = Int[]) Construct an array by repeating the entries of ``A``. The i-th element of ``inner`` specifies the number of times that the individual entries of the i-th dimension of ``A`` should be repeated. The i-th element of ``outer`` specifies the number of times that a slice along the i-th dimension of ``A`` should be repeated.
.. function:: kron(A, B) Kronecker tensor product of two vectors or two matrices.
.. function:: linreg(x, y) Determine parameters ``[a, b]`` that minimize the squared error between ``y`` and ``a+b*x``.
.. function:: linreg(x, y, w) Weighted least-squares linear regression.
.. function:: expm(A) Matrix exponential.
.. function:: issym(A) Test whether a matrix is symmetric.
.. function:: isposdef(A) Test whether a matrix is positive-definite.
.. function:: isposdef!(A) Test whether a matrix is positive-definite, overwriting A in the processes.
.. function:: istril(A) Test whether a matrix is lower-triangular.
.. function:: istriu(A) Test whether a matrix is upper-triangular.
.. function:: ishermitian(A) Test whether a matrix is hermitian.
.. function:: transpose(A) The transpose operator (``.'``).
.. function:: ctranspose(A) The conjugate transpose operator (``'``).
.. function:: eigs(A; nev=6, which="LM", tol=0.0, maxiter=1000, sigma=0, ritzvec=true, op_part=:real,v0=zeros((0,))) -> (d,[v,],nconv,niter,nmult,resid)
``eigs`` computes eigenvalues ``d`` of A using Arnoldi factorization. The following keyword arguments are supported:
* ``nev``: Number of eigenvalues
* ``which``: type of eigenvalues ("LM", "SM")
* ``tol``: tolerance (:math:`tol \le 0.0` defaults to ``DLAMCH('EPS')``)
* ``maxiter``: Maximum number of iterations
* ``sigma``: find eigenvalues close to ``sigma`` using shift and invert
* ``ritzvec``: Returns the Ritz vectors ``v`` (eigenvectors) if ``true``
* ``op_part``: which part of linear operator to use for real A (:real, :imag)
* ``v0``: starting vector from which to start the Arnoldi iteration
``eigs`` returns the ``nev`` requested eigenvalues in ``d``, the corresponding Ritz vectors ``v`` (only if ``ritzvec=true``), the number of converged eigenvalues ``nconv``, the number of iterations ``niter`` and the number of matrix vector multiplications ``nmult``, as well as the final residual vector ``resid``.
.. function:: svds(A; nev=6, which="LA", tol=0.0, maxiter=1000, ritzvec=true)
``svds`` computes the singular values of A using Arnoldi factorization. The following keyword arguments are supported:
* ``nsv``: Number of singular values
* ``which``: type of singular values ("LA")
* ``tol``: tolerance (:math:`tol \le 0.0` defaults to ``DLAMCH('EPS')``)
* ``maxiter``: Maximum number of iterations
* ``ritzvec``: Returns the singular vectors if ``true``
.. function:: peakflops(n; parallel=false) ``peakflops`` computes the peak flop rate of the computer by using BLAS dgemm. 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.
.. module:: Base.LinAlg.BLAS
This module provides wrappers for some of the BLAS functions for
linear algebra. Those BLAS functions that overwrite one of the input
arrays have names ending in '!'.
Usually a function has 4 methods defined, one each for Float64,
Float32, Complex128 and Complex64 arrays.
.. currentmodule:: Base
.. 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``. There are no ``dot`` methods for ``Complex`` arrays.
The following functions are defined within the Base.LinAlg.BLAS module.
.. currentmodule:: Base.LinAlg.BLAS
.. function:: blascopy!(n, X, incx, Y, incy) Copy ``n`` elements of array ``X`` with stride ``incx`` to array ``Y`` with stride ``incy``. Returns ``Y``.
.. function:: nrm2(n, X, incx) 2-norm of a vector consisting of ``n`` elements of array ``X`` with stride ``incx``.
.. function:: asum(n, X, incx) sum of the absolute values of the first ``n`` elements of array ``X`` with stride ``incx``.
.. function:: axpy!(n, a, X, incx, Y, incy) Overwrite ``Y`` with ``a*X + Y``. Returns ``Y``.
.. function:: scal!(n, a, X, incx) Overwrite ``X`` with ``a*X``. Returns ``X``.
.. function:: scal(n, a, X, incx) Returns ``a*X``.
.. function:: syrk!(uplo, trans, alpha, A, beta, C)
Rank-k update of the symmetric matrix ``C`` as ``alpha*A*A.' +
beta*C`` or ``alpha*A.'*A + beta*C`` according to whether ``trans``
is 'N' or 'T'. When ``uplo`` is 'U' the upper triangle of ``C`` is
updated ('L' for lower triangle). Returns ``C``.
.. function:: syrk(uplo, trans, alpha, A)
Returns either the upper triangle or the lower triangle, according
to ``uplo`` ('U' or 'L'), of ``alpha*A*A.'`` or ``alpha*A.'*A``,
according to ``trans`` ('N' or 'T').
.. function:: herk!(uplo, trans, alpha, A, beta, C)
Methods for complex arrays only. Rank-k update of the Hermitian
matrix ``C`` as ``alpha*A*A' + beta*C`` or ``alpha*A'*A + beta*C``
according to whether ``trans`` is 'N' or 'T'. When ``uplo`` is 'U'
the upper triangle of ``C`` is updated ('L' for lower triangle).
Returns ``C``.
.. function:: herk(uplo, trans, alpha, A)
Methods for complex arrays only. Returns either the upper triangle
or the lower triangle, according to ``uplo`` ('U' or 'L'), of
``alpha*A*A'`` or ``alpha*A'*A``, according to ``trans`` ('N' or 'T').
.. 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`` ('N' or 'T'). The matrix ``A`` is
a general band matrix of dimension ``m`` by ``size(A,2)`` with
``kl`` sub-diagonals and ``ku`` super-diagonals. Returns the
updated ``y``.
.. function:: gbmv(trans, m, kl, ku, alpha, A, x, beta, y)
Returns ``alpha*A*x`` or ``alpha*A'*x`` according to ``trans`` ('N'
or 'T'). The matrix ``A`` is a general band matrix of dimension
``m`` by ``size(A,2)`` with ``kl`` sub-diagonals and
``ku`` super-diagonals.
.. 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`` super-diagonals stored in the argument ``A``. The storage layout for ``A`` is described the reference BLAS module, level-2 BLAS at http://www.netlib.org/lapack/explore-html/. Returns the updated ``y``.
.. function:: sbmv(uplo, k, alpha, A, x) Returns ``alpha*A*x`` where ``A`` is a symmetric band matrix of order ``size(A,2)`` with ``k`` super-diagonals stored in the argument ``A``.
.. function:: sbmv(uplo, k, A, x) Returns ``A*x`` where ``A`` is a symmetric band matrix of order ``size(A,2)`` with ``k`` super-diagonals stored in the argument ``A``.
.. 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`` (transpose ``A``) and ``tB``. Returns the updated ``C``.
.. function:: gemm(tA, tB, alpha, A, B) Returns ``alpha*A*B`` or the other three variants according to ``tA`` (transpose ``A``) and ``tB``.
.. function:: gemm(tA, tB, alpha, A, B) Returns ``alpha*A*B`` or the other three variants according to ``tA`` (transpose ``A``) and ``tB``.
.. function:: gemv!(tA, alpha, A, x, beta, y) Update the vector ``y`` as ``alpha*A*x + beta*x`` or ``alpha*A'x + beta*x`` according to ``tA`` (transpose ``A``). Returns the updated ``y``.
.. function:: gemv(tA, alpha, A, x) Returns ``alpha*A*x`` or ``alpha*A'x`` according to ``tA`` (transpose ``A``).
.. function:: gemv(tA, alpha, A, x) Returns ``A*x`` or ``A'x`` according to ``tA`` (transpose ``A``).
.. 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. Returns the updated ``C``.
.. function:: symm(side, ul, alpha, A, B) Returns ``alpha*A*B`` or ``alpha*B*A`` according to ``side``. ``A`` is assumed to be symmetric. Only the ``ul`` triangle of ``A`` is used.
.. function:: symm(side, ul, A, B) Returns ``A*B`` or ``B*A`` according to ``side``. ``A`` is assumed to be symmetric. Only the ``ul`` triangle of ``A`` is used.
.. function:: symm(tA, tB, alpha, A, B) Returns ``alpha*A*B`` or the other three variants according to ``tA`` (transpose ``A``) and ``tB``.
.. function:: symv!(ul, alpha, A, x, beta, y) Update the vector ``y`` as ``alpha*A*y + beta*y``. ``A`` is assumed to be symmetric. Only the ``ul`` triangle of ``A`` is used. Returns the updated ``y``.
.. function:: symv(ul, alpha, A, x) Returns ``alpha*A*x``. ``A`` is assumed to be symmetric. Only the ``ul`` triangle of ``A`` is used.
.. function:: symv(ul, A, x) Returns ``A*x``. ``A`` is assumed to be symmetric. Only the ``ul`` triangle of ``A`` is used.
.. 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`` (A on left or right) and ``tA`` (transpose A). Only the ``ul`` triangle of ``A`` is used. ``dA`` indicates if ``A`` is unit-triangular (the diagonal is assumed to be all ones). Returns the updated ``B``.
.. function:: trmm(side, ul, tA, dA, alpha, A, B) Returns ``alpha*A*B`` or one of the other three variants determined by ``side`` (A on left or right) and ``tA`` (transpose A). Only the ``ul`` triangle of ``A`` is used. ``dA`` indicates if ``A`` is unit-triangular (the diagonal is assumed to be all ones).
.. 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`` (A on left or right of ``X``) and ``tA`` (transpose A). Only the ``ul`` triangle of ``A`` is used. ``dA`` indicates if ``A`` is unit-triangular (the diagonal is assumed to be all ones). Returns the updated ``B``.
.. function:: trsm(side, ul, tA, dA, alpha, A, B) Returns the solution to ``A*X = alpha*B`` or one of the other three variants determined by ``side`` (A on left or right of ``X``) and ``tA`` (transpose A). Only the ``ul`` triangle of ``A`` is used. ``dA`` indicates if ``A`` is unit-triangular (the diagonal is assumed to be all ones).
.. function:: trmv!(side, ul, tA, dA, alpha, A, b) Update ``b`` as ``alpha*A*b`` or one of the other three variants determined by ``side`` (A on left or right) and ``tA`` (transpose A). Only the ``ul`` triangle of ``A`` is used. ``dA`` indicates if ``A`` is unit-triangular (the diagonal is assumed to be all ones). Returns the updated ``b``.
.. function:: trmv(side, ul, tA, dA, alpha, A, b) Returns ``alpha*A*b`` or one of the other three variants determined by ``side`` (A on left or right) and ``tA`` (transpose A). Only the ``ul`` triangle of ``A`` is used. ``dA`` indicates if ``A`` is unit-triangular (the diagonal is assumed to be all ones).
.. function:: trsv!(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`` (A on left or right of ``X``) and ``tA`` (transpose A). Only the ``ul`` triangle of ``A`` is used. ``dA`` indicates if ``A`` is unit-triangular (the diagonal is assumed to be all ones). Returns the updated ``b``.
.. function:: trsv(side, ul, tA, dA, alpha, A, b) Returns the solution to ``A*X = alpha*b`` or one of the other three variants determined by ``side`` (A on left or right of ``X``) and ``tA`` (transpose A). Only the ``ul`` triangle of ``A`` is used. ``dA`` indicates if ``A`` is unit-triangular (the diagonal is assumed to be all ones).
.. function:: blas_set_num_threads(n) Set the number of threads the BLAS library should use.