The BLAS Interface

The cvxopt.blas module provides an interface to the double-precision real and complex Basic Linear Algebra Subprograms (BLAS). The names and calling sequences of the Python functions in the interface closely match the corresponding Fortran BLAS routines (described in the references below) and their functionality is exactly the same. Many of the operations performed by the BLAS routines can be implemented in a more straightforward way by using the matrix arithmetic of the section Arithmetic Operations, combined with the slicing and indexing of the section Indexing and Slicing. As an example, C = A * B gives the same result as the BLAS call gemm(A, B, C). The BLAS interface offers two advantages. First, some of the functions it includes are not easily implemented using the basic matrix arithmetic. For example, BLAS includes functions that efficiently exploit symmetry or triangular matrix structure. Second, there is a performance difference that can be significant for large matrices. Although our implementation of the basic matrix arithmetic makes internal calls to BLAS, it also often requires creating temporary matrices to store intermediate results. The BLAS functions on the other hand always operate directly on their matrix arguments and never require any copying to temporary matrices. Thus they can be viewed as generalizations of the in-place matrix addition and scalar multiplication of the section Arithmetic Operations to more complicated operations.

See also

  • C. L. Lawson, R. J. Hanson, D. R. Kincaid, F. T. Krogh, Basic Linear Algebra Subprograms for Fortran Use, ACM Transactions on Mathematical Software, 5(3), 309-323, 1975.
  • J. J. Dongarra, J. Du Croz, S. Hammarling, R. J. Hanson, An Extended Set of Fortran Basic Linear Algebra Subprograms, ACM Transactions on Mathematical Software, 14(1), 1-17, 1988.
  • J. J. Dongarra, J. Du Croz, S. Hammarling, I. Duff, A Set of Level 3 Basic Linear Algebra Subprograms, ACM Transactions on Mathematical Software, 16(1), 1-17, 1990.

Matrix Classes

The BLAS exploit several types of matrix structure: symmetric, Hermitian, triangular, and banded. We represent all these matrix classes by dense real or complex matrix objects, with additional arguments that specify the structure.

Vector
A real or complex \(n\)-vector is represented by a matrix of type 'd' or 'z' and length \(n\), with the entries of the vector stored in column-major order.
General matrix
A general real or complex \(m\) by \(n\) matrix is represented by a real or complex matrix of size (\(m\), \(n\)).
Symmetric matrix

A real or complex symmetric matrix of order \(n\) is represented by a real or complex matrix of size (\(n\), \(n\)), and a character argument uplo with two possible values: 'L' and 'U'. If uplo is 'L', the lower triangular part of the symmetric matrix is stored; if uplo is 'U', the upper triangular part is stored. A square matrix X of size (\(n\), \(n\)) can therefore be used to represent the symmetric matrices

\[\begin{split}\left[\begin{array}{ccccc} X[0,0] & X[1,0] & X[2,0] & \cdots & X[n-1,0] \\ X[1,0] & X[1,1] & X[2,1] & \cdots & X[n-1,1] \\ X[2,0] & X[2,1] & X[2,2] & \cdots & X[n-1,2] \\ \vdots & \vdots & \vdots & \ddots & \vdots \\ X[n-1,0] & X[n-1,1] & X[n-1,2] & \cdots & X[n-1,n-1] \end{array}\right] \quad \mbox{(uplo = 'L')},\end{split}\]\[\begin{split}\left[\begin{array}{ccccc} X[0,0] & X[0,1] & X[0,2] & \cdots & X[0,n-1] \\ X[0,1] & X[1,1] & X[1,2] & \cdots & X[1,n-1] \\ X[0,2] & X[1,2] & X[2,2] & \cdots & X[2,n-1] \\ \vdots & \vdots & \vdots & \ddots & \vdots \\ X[0,n-1] & X[1,n-1] & X[2,n-1] & \cdots & X[n-1,n-1] \end{array}\right] \quad \mbox{(uplo = U')}.\end{split}\]
Complex Hermitian matrix

A complex Hermitian matrix of order \(n\) is represented by a matrix of type 'z' and size (\(n\), \(n\)), and a character argument uplo with the same meaning as for symmetric matrices. A complex matrix X of size (\(n\), \(n\)) can represent the Hermitian matrices

\[\begin{split}\left[\begin{array}{ccccc} \Re X[0,0] & \bar X[1,0] & \bar X[2,0] & \cdots & \bar X[n-1,0] \\ X[1,0] & \Re X[1,1] & \bar X[2,1] & \cdots & \bar X[n-1,1] \\ X[2,0] & X[2,1] & \Re X[2,2] & \cdots & \bar X[n-1,2] \\ \vdots & \vdots & \vdots & \ddots & \vdots \\ X[n-1,0] & X[n-1,1] & X[n-1,2] & \cdots & \Re X[n-1,n-1] \end{array}\right] \quad \mbox{(uplo = 'L')},\end{split}\]\[\begin{split}\left[\begin{array}{ccccc} \Re X[0,0] & X[0,1] & X[0,2] & \cdots & X[0,n-1] \\ \bar X[0,1] & \Re X[1,1] & X[1,2] & \cdots & X[1,n-1] \\ \bar X[0,2] & \bar X[1,2] & \Re X[2,2] & \cdots & X[2,n-1] \\ \vdots & \vdots & \vdots & \ddots & \vdots \\ \bar X[0,n-1] & \bar X[1,n-1] & \bar X[2,n-1] & \cdots & \Re X[n-1,n-1] \end{array}\right] \quad \mbox{(uplo = 'U')}.\end{split}\]
Triangular matrix

A real or complex triangular matrix of order \(n\) is represented by a real or complex matrix of size (\(n\), \(n\)), and two character arguments: an argument uplo with possible values 'L' and 'U' to distinguish between lower and upper triangular matrices, and an argument diag with possible values 'U' and 'N' to distinguish between unit and non-unit triangular matrices. A square matrix X of size (\(n\), \(n\)) can represent the triangular matrices

\[\begin{split}\left[\begin{array}{ccccc} X[0,0] & 0 & 0 & \cdots & 0 \\ X[1,0] & X[1,1] & 0 & \cdots & 0 \\ X[2,0] & X[2,1] & X[2,2] & \cdots & 0 \\ \vdots & \vdots & \vdots & \ddots & \vdots \\ X[n-1,0] & X[n-1,1] & X[n-1,2] & \cdots & X[n-1,n-1] \end{array}\right] \quad \mbox{(uplo = 'L', diag = 'N')},\end{split}\]\[\begin{split}\left[\begin{array}{ccccc} 1 & 0 & 0 & \cdots & 0 \\ X[1,0] & 1 & 0 & \cdots & 0 \\ X[2,0] & X[2,1] & 1 & \cdots & 0 \\ \vdots & \vdots & \vdots & \ddots & \vdots \\ X[n-1,0] & X[n-1,1] & X[n-1,2] & \cdots & 1 \end{array}\right] \quad \mbox{(uplo = 'L', diag = 'U')},\end{split}\]\[\begin{split}\left[\begin{array}{ccccc} X[0,0] & X[0,1] & X[0,2] & \cdots & X[0,n-1] \\ 0 & X[1,1] & X[1,2] & \cdots & X[1,n-1] \\ 0 & 0 & X[2,2] & \cdots & X[2,n-1] \\ \vdots & \vdots & \vdots & \ddots & \vdots \\ 0 & 0 & 0 & \cdots & X[n-1,n-1] \end{array}\right] \quad \mbox{(uplo = 'U', diag = 'N')},\end{split}\]\[\begin{split}\left[\begin{array}{ccccc} 1 & X[0,1] & X[0,2] & \cdots & X[0,n-1] \\ 0 & 1 & X[1,2] & \cdots & X[1,n-1] \\ 0 & 0 & 1 & \cdots & X[2,n-1] \\ \vdots & \vdots & \vdots & \ddots & \vdots \\ 0 & 0 & 0 & \cdots & 1 \end{array}\right] \quad \mbox{(uplo = 'U', diag = 'U')}.\end{split}\]
General band matrix

A general real or complex \(m\) by \(n\) band matrix with \(k_l\) subdiagonals and \(k_u\) superdiagonals is represented by a real or complex matrix X of size (\(k_l + k_u + 1\), \(n\)), and the two integers \(m\) and \(k_l\). The diagonals of the band matrix are stored in the rows of X, starting at the top diagonal, and shifted horizontally so that the entries of column \(k\) of the band matrix are stored in column \(k\) of X. A matrix X of size (\(k_l + k_u + 1\), \(n\)) therefore represents the \(m\) by \(n\) band matrix

\[\begin{split}\left[ \begin{array}{ccccccc} X[k_u,0] & X[k_u-1,1] & X[k_u-2,2] & \cdots & X[0,k_u] & 0 & \cdots \\ X[k_u+1,0] & X[k_u,1] & X[k_u-1,2] & \cdots & X[1,k_u] & X[0,k_u+1] & \cdots \\ X[k_u+2,0] & X[k_u+1,1] & X[k_u,2] & \cdots & X[2,k_u] & X[1,k_u+1] & \cdots \\ \vdots & \vdots & \vdots & \ddots & \vdots & \vdots & \ddots \\ X[k_u+k_l,0] & X[k_u+k_l-1,1] & X[k_u+k_l-2,2] & \cdots & & & \\ 0 & X[k_u+k_l,1] & X[k_u+k_l-1,2] & \cdots & & & \\ \vdots & \vdots & \vdots & \ddots & & & \end{array}\right].\end{split}\]
Symmetric band matrix

A real or complex symmetric band matrix of order \(n\) with \(k\) subdiagonals, is represented by a real or complex matrix X of size (\(k+1\), \(n\)), and an argument uplo to indicate whether the subdiagonals (uplo is 'L') or superdiagonals (uplo is 'U') are stored. The \(k+1\) diagonals are stored as rows of X, starting at the top diagonal (i.e., the main diagonal if uplo is 'L', or the \(k\)-th superdiagonal if uplo is 'U') and shifted horizontally so that the entries of the \(k\)-th column of the band matrix are stored in column \(k\) of X. A matrix X of size (\(k+1\), \(n\)) can therefore represent the band matrices

\[\begin{split}\left[ \begin{array}{ccccccc} X[0,0] & X[1,0] & X[2,0] & \cdots & X[k,0] & 0 & \cdots \\ X[1,0] & X[0,1] & X[1,1] & \cdots & X[k-1,1] & X[k,1] & \cdots \\ X[2,0] & X[1,1] & X[0,2] & \cdots & X[k-2,2] & X[k-1,2] & \cdots \\ \vdots & \vdots & \vdots & \ddots & \vdots & \vdots & \ddots \\ X[k,0] & X[k-1,1] & X[k-2,2] & \cdots & & & \\ 0 & X[k,1] & X[k-1,2] & \cdots & & & \\ \vdots & \vdots & \vdots & \ddots & & & \end{array}\right] \quad \mbox{(uplo = 'L')},\end{split}\]\[\begin{split}\left[ \begin{array}{ccccccc} X[k,0] & X[k-1,1] & X[k-2,2] & \cdots & X[0,k] & 0 & \cdots \\ X[k-1,1] & X[k,1] & X[k-1,2] & \cdots & X[1,k] & X[0,k+1] & \cdots \\ X[k-2,2] & X[k-1,2] & X[k,2] & \cdots & X[2,k] & X[1,k+1] & \cdots \\ \vdots & \vdots & \vdots & \ddots & \vdots & \vdots & \ddots \\ X[0,k] & X[1,k] & X[2,k] & \cdots & & & \\ 0 & X[0,k+1] & X[1,k+1] & \cdots & & & \\ \vdots & \vdots & \vdots & \ddots & & & \end{array}\right] \quad \mbox{(uplo='U')}.\end{split}\]
Hermitian band matrix

A complex Hermitian band matrix of order \(n\) with \(k\) subdiagonals is represented by a complex matrix of size (\(k+1\), \(n\)) and an argument uplo, with the same meaning as for symmetric band matrices. A matrix X of size (\(k+1\), \(n\)) can represent the band matrices

\[\begin{split}\left[ \begin{array}{ccccccc} \Re X[0,0] & \bar X[1,0] & \bar X[2,0] & \cdots & \bar X[k,0] & 0 & \cdots \\ X[1,0] & \Re X[0,1] & \bar X[1,1] & \cdots & \bar X[k-1,1] & \bar X[k,1] & \cdots \\ X[2,0] & X[1,1] & \Re X[0,2] & \cdots & \bar X[k-2,2] & \bar X[k-1,2] & \cdots \\ \vdots & \vdots & \vdots & \ddots & \vdots & \vdots & \ddots \\ X[k,0] & X[k-1,1] & X[k-2,2] & \cdots & & & \\ 0 & X[k,1] & X[k-1,2] & \cdots & & & \\ \vdots & \vdots & \vdots & \ddots & & & \end{array}\right] \quad \mbox{(uplo = 'L')},\end{split}\]\[\begin{split}\left[ \begin{array}{ccccccc} \Re X[k,0] & X[k-1,1] & X[k-2,2] & \cdots & X[0,k] & 0 & \cdots \\ \bar X[k-1,1] & \Re X[k,1] & X[k-1,2] & \cdots & X[1,k] & X[0,k+1] & \cdots \\ \bar X[k-2,2] & \bar X[k-1,2] & \Re X[k,2] & \cdots & X[2,k] & X[1,k+1] & \cdots \\ \vdots & \vdots & \vdots & \ddots & \vdots & \vdots & \ddots \\ \bar X[0,k] & \bar X[1,k] & \bar X[2,k] & \cdots & & & \\ 0 & \bar X[0,k+1] & \bar X[1,k+1] & \cdots & & & \\ \vdots & \vdots & \vdots & \ddots & & & \end{array}\right] \quad \mbox{(uplo='U')}.\end{split}\]
Triangular band matrix

A triangular band matrix of order \(n\) with \(k\) subdiagonals or superdiagonals is represented by a real complex matrix of size (\(k+1\), \(n\)) and two character arguments uplo and diag, with similar conventions as for symmetric band matrices. A matrix X of size (\(k+1\), \(n\)) can represent the band matrices

\[\begin{split}\left[ \begin{array}{cccc} X[0,0] & 0 & 0 & \cdots \\ X[1,0] & X[0,1] & 0 & \cdots \\ X[2,0] & X[1,1] & X[0,2] & \cdots \\ \vdots & \vdots & \vdots & \ddots \\ X[k,0] & X[k-1,1] & X[k-2,2] & \cdots \\ 0 & X[k,1] & X[k-1,1] & \cdots \\ \vdots & \vdots & \vdots & \ddots \end{array}\right] \quad \mbox{(uplo = 'L', diag = 'N')},\end{split}\]\[\begin{split}\left[ \begin{array}{cccc} 1 & 0 & 0 & \cdots \\ X[1,0] & 1 & 0 & \cdots \\ X[2,0] & X[1,1] & 1 & \cdots \\ \vdots & \vdots & \vdots & \ddots \\ X[k,0] & X[k-1,1] & X[k-2,2] & \cdots \\ 0 & X[k,1] & X[k-1,2] & \cdots \\ \vdots & \vdots & \vdots & \ddots \end{array}\right] \quad \mbox{(uplo = 'L', diag = 'U')},\end{split}\]\[\begin{split}\left[ \begin{array}{ccccccc} X[k,0] & X[k-1,1] & X[k-2,3] & \cdots & X[0,k] & 0 & \cdots\\ 0 & X[k,1] & X[k-1,2] & \cdots & X[1,k] & X[0,k+1] & \cdots \\ 0 & 0 & X[k,2] & \cdots & X[2,k] & X[1,k+1] & \cdots \\ \vdots & \vdots & \vdots & \ddots & \vdots & \vdots & \ddots \end{array}\right] \quad \mbox{(uplo = 'U', diag = 'N')},\end{split}\]\[\begin{split}\left[ \begin{array}{ccccccc} 1 & X[k-1,1] & X[k-2,3] & \cdots & X[0,k] & 0 & \cdots\\ 0 & 1 & X[k-1,2] & \cdots & X[1,k] & X[0,k+1] & \cdots \\ 0 & 0 & 1 & \cdots & X[2,k] & X[1,k+1] & \cdots \\ \vdots & \vdots & \vdots & \ddots & \vdots & \vdots & \ddots \end{array}\right] \quad \mbox{(uplo = 'U', diag = 'U')}.\end{split}\]

When discussing BLAS functions in the following sections we will omit several less important optional arguments that can be used to select submatrices for in-place operations. The complete specification is documented in the docstrings of the source code, and can be viewed with the pydoc help program.

Level 1 BLAS

The level 1 functions implement vector operations.

cvxopt.blas.scal(alpha, x)

Scales a vector by a constant:

\[x := \alpha x.\]

If x is a real matrix, the scalar argument alpha must be a Python integer or float. If x is complex, alpha can be an integer, float, or complex.

cvxopt.blas.nrm2(x)

Euclidean norm of a vector: returns

\[\|x\|_2.\]
cvxopt.blas.asum(x)

1-Norm of a vector: returns

\[\|x\|_1 \quad \mbox{($x$ real)}, \qquad \|\Re x\|_1 + \|\Im x\|_1 \quad \mbox{($x$ complex)}.\]
cvxopt.blas.iamax(x)

Returns

\[\mathop{\rm argmax}_{k=0,\ldots,n-1} |x_k| \quad \mbox{($x$ real)}, \qquad \mathop{\rm argmax}_{k=0,\ldots,n-1} |\Re x_k| + |\Im x_k| \quad \mbox{($x$ complex)}.\]

If more than one coefficient achieves the maximum, the index of the first \(k\) is returned.

cvxopt.blas.swap(x, y)

Interchanges two vectors:

\[x \leftrightarrow y.\]

x and y are matrices of the same type ('d' or 'z').

cvxopt.blas.copy(x, y)

Copies a vector to another vector:

\[y := x.\]

x and y are matrices of the same type ('d' or 'z').

cvxopt.blas.axpy(x, y[, alpha = 1.0])

Constant times a vector plus a vector:

\[y := \alpha x + y.\]

x and y are matrices of the same type ('d' or 'z'). If x is real, the scalar argument alpha must be a Python integer or float. If x is complex, alpha can be an integer, float, or complex.

cvxopt.blas.dot(x, y)

Returns

\[x^Hy.\]

x and y are matrices of the same type ('d' or 'z').

cvxopt.blas.dotu(x, y)

Returns

\[x^Ty.\]

x and y are matrices of the same type ('d' or 'z').

Level 2 BLAS

The level 2 functions implement matrix-vector products and rank-1 and rank-2 matrix updates. Different types of matrix structure can be exploited using the conventions of the section Matrix Classes.

cvxopt.blas.gemv(A, x, y[, trans = 'N', alpha = 1.0, beta = 0.0])

Matrix-vector product with a general matrix:

\[\begin{split}y & := \alpha Ax + \beta y \quad (\mathrm{trans} = \mathrm{'N'}), \\ y & := \alpha A^T x + \beta y \quad (\mathrm{trans} = \mathrm{'T'}), \\ y & := \alpha A^H x + \beta y \quad (\mathrm{trans} = \mathrm{'C'}).\end{split}\]

The arguments A, x, and y must have the same type ('d' or 'z'). Complex values of alpha and beta are only allowed if A is complex.

cvxopt.blas.symv(A, x, y[, uplo = 'L', alpha = 1.0, beta = 0.0])

Matrix-vector product with a real symmetric matrix:

\[y := \alpha A x + \beta y,\]

where \(A\) is a real symmetric matrix. The arguments A, x, and y must have type 'd', and alpha and beta must be real.

cvxopt.blas.hemv(A, x, y[, uplo = 'L', alpha = 1.0, beta = 0.0])

Matrix-vector product with a real symmetric or complex Hermitian matrix:

\[y := \alpha A x + \beta y,\]

where \(A\) is real symmetric or complex Hermitian. The arguments A, x, y must have the same type ('d' or 'z'). Complex values of alpha and beta are only allowed if A is complex.

cvxopt.blas.trmv(A, x[, uplo = 'L', trans = 'N', diag = 'N'])

Matrix-vector product with a triangular matrix:

\[\begin{split}x & := Ax \quad (\mathrm{trans} = \mathrm{'N'}), \\ x & := A^T x \quad (\mathrm{trans} = \mathrm{'T'}), \\ x & := A^H x \quad (\mathrm{trans} = \mathrm{'C'}),\end{split}\]

where \(A\) is square and triangular. The arguments A and x must have the same type ('d' or 'z').

cvxopt.blas.trsv(A, x[, uplo = 'L', trans = 'N', diag = 'N'])

Solution of a nonsingular triangular set of linear equations:

\[\begin{split}x & := A^{-1}x \quad (\mathrm{trans} = \mathrm{'N'}), \\ x & := A^{-T}x \quad (\mathrm{trans} = \mathrm{'T'}), \\ x & := A^{-H}x \quad (\mathrm{trans} = \mathrm{'C'}),\end{split}\]

where \(A\) is square and triangular with nonzero diagonal elements. The arguments A and x must have the same type ('d' or 'z').

cvxopt.blas.gbmv(A, m, kl, x, y[, trans = 'N', alpha = 1.0, beta = 0.0])

Matrix-vector product with a general band matrix:

\[\begin{split}y & := \alpha Ax + \beta y \quad (\mathrm{trans} = \mathrm{'N'}), \\ y & := \alpha A^T x + \beta y \quad (\mathrm{trans} = \mathrm{'T'}), \\ y & := \alpha A^H x + \beta y \quad (\mathrm{trans} = \mathrm{'C'}),\end{split}\]

where \(A\) is a rectangular band matrix with \(m\) rows and \(k_l\) subdiagonals. The arguments A, x, y must have the same type ('d' or 'z'). Complex values of alpha and beta are only allowed if A is complex.

cvxopt.blas.sbmv(A, x, y[, uplo = 'L', alpha = 1.0, beta = 0.0])

Matrix-vector product with a real symmetric band matrix:

\[y := \alpha Ax + \beta y,\]

where \(A\) is a real symmetric band matrix. The arguments A, x, y must have type 'd', and alpha and beta must be real.

cvxopt.blas.hbmv(A, x, y[, uplo = 'L', alpha = 1.0, beta = 0.0])

Matrix-vector product with a real symmetric or complex Hermitian band matrix:

\[y := \alpha Ax + \beta y,\]

where \(A\) is a real symmetric or complex Hermitian band matrix. The arguments A, x, y must have the same type ('d' or 'z'). Complex values of alpha and beta are only allowed if A is complex.

cvxopt.blas.tbmv(A, x[, uplo = 'L', trans = 'N', diag = 'N'])

Matrix-vector product with a triangular band matrix:

\[\begin{split}x & := Ax \quad (\mathrm{trans} = \mathrm{'N'}), \\ x & := A^T x \quad (\mathrm{trans} = \mathrm{'T'}), \\ x & := A^H x \quad (\mathrm{trans} = \mathrm{'C'}).\end{split}\]

The arguments A and x must have the same type ('d' or 'z').

cvxopt.blas.tbsv(A, x[, uplo = 'L', trans = 'N', diag = 'N'])

Solution of a triangular banded set of linear equations:

\[\begin{split}x & := A^{-1}x \quad (\mathrm{trans} = \mathrm{'N'}), \\ x & := A^{-T} x \quad (\mathrm{trans} = \mathrm{'T'}), \\ x & := A^{-H} x \quad (\mathrm{trans} = \mathrm{'T'}),\end{split}\]

where \(A\) is a triangular band matrix of with nonzero diagonal elements. The arguments A and x must have the same type ('d' or 'z').

cvxopt.blas.ger(x, y, A[, alpha = 1.0])

General rank-1 update:

\[A := A + \alpha x y^H,\]

where \(A\) is a general matrix. The arguments A, x, and y must have the same type ('d' or 'z'). Complex values of alpha are only allowed if A is complex.

cvxopt.blas.geru(x, y, A[, alpha = 1.0])

General rank-1 update:

\[A := A + \alpha x y^T,\]

where \(A\) is a general matrix. The arguments A, x, and y must have the same type ('d' or 'z'). Complex values of alpha are only allowed if A is complex.

cvxopt.blas.syr(x, A[, uplo = 'L', alpha = 1.0])

Symmetric rank-1 update:

\[A := A + \alpha xx^T,\]

where \(A\) is a real symmetric matrix. The arguments A and x must have type 'd'. alpha must be a real number.

cvxopt.blas.her(x, A[, uplo = 'L', alpha = 1.0])

Hermitian rank-1 update:

\[A := A + \alpha xx^H,\]

where \(A\) is a real symmetric or complex Hermitian matrix. The arguments A and x must have the same type ('d' or 'z'). alpha must be a real number.

cvxopt.blas.syr2(x, y, A[, uplo = 'L', alpha = 1.0])

Symmetric rank-2 update:

\[A := A + \alpha (xy^T + yx^T),\]

where \(A\) is a real symmetric matrix. The arguments A, x, and y must have type 'd'. alpha must be real.

cvxopt.blas.her2(x, y, A[, uplo = 'L', alpha = 1.0])

Symmetric rank-2 update:

\[A := A + \alpha xy^H + \bar \alpha yx^H,\]

where \(A\) is a a real symmetric or complex Hermitian matrix. The arguments A, x, and y must have the same type ('d' or 'z'). Complex values of alpha are only allowed if A is complex.

As an example, the following code multiplies the tridiagonal matrix

\[\begin{split}A = \left[\begin{array}{rrrr} 1 & 6 & 0 & 0 \\ 2 & -4 & 3 & 0 \\ 0 & -3 & -1 & 1 \end{array}\right]\end{split}\]

with the vector \(x = (1,-1,2,-2)\).

>>> from cvxopt import matrix
>>> from cvxopt.blas import gbmv
>>> A = matrix([[0., 1., 2.],  [6., -4., -3.],  [3., -1., 0.],  [1., 0., 0.]])
>>> x = matrix([1., -1., 2., -2.])
>>> y = matrix(0., (3,1))
>>> gbmv(A, 3, 1, x, y)
>>> print(y)
[-5.00e+00]
[ 1.20e+01]
[-1.00e+00]

The following example illustrates the use of tbsv.

>>> from cvxopt import matrix
>>> from cvxopt.blas import tbsv
>>> A = matrix([-6., 5., -1., 2.], (1,4))
>>> x = matrix(1.0, (4,1))
>>> tbsv(A, x)  # x := diag(A)^{-1}*x
>>> print(x)
[-1.67e-01]
[ 2.00e-01]
[-1.00e+00]
[ 5.00e-01]

Level 3 BLAS

The level 3 BLAS include functions for matrix-matrix multiplication.

cvxopt.blas.gemm(A, B, C[, transA = 'N', transB = 'N', alpha = 1.0, beta = 0.0])

Matrix-matrix product of two general matrices:

\[\newcommand{\op}{\mathop{\mathrm{op}}} C := \alpha \op(A) \op(B) + \beta C\]

where

\[\begin{split}\newcommand{\op}{\mathop{\mathrm{op}}} \op(A) = \left\{ \begin{array}{ll} A & \mathrm{transA} = \mathrm{'N'} \\ A^T & \mathrm{transA} = \mathrm{'T'} \\ A^H & \mathrm{transA} = \mathrm{'C'} \end{array} \right. \qquad \op(B) = \left\{ \begin{array}{ll} B & \mathrm{transB} = \mathrm{'N'} \\ B^T & \mathrm{transB} = \mathrm{'T'} \\ B^H & \mathrm{transB} = \mathrm{'C'}. \end{array} \right.\end{split}\]

The arguments A, B, and C must have the same type ('d' or 'z'). Complex values of alpha and beta are only allowed if A is complex.

cvxopt.blas.symm(A, B, C[, side = 'L', uplo = 'L', alpha =1.0, beta = 0.0])

Product of a real or complex symmetric matrix \(A\) and a general matrix \(B\):

\[\begin{split}C & := \alpha AB + \beta C \quad (\mathrm{side} = \mathrm{'L'}), \\ C & := \alpha BA + \beta C \quad (\mathrm{side} = \mathrm{'R'}).\end{split}\]

The arguments A, B, and C must have the same type ('d' or 'z'). Complex values of alpha and beta are only allowed if A is complex.

cvxopt.blas.hemm(A, B, C[, side = 'L', uplo = 'L', alpha = 1.0, beta = 0.0])

Product of a real symmetric or complex Hermitian matrix \(A\) and a general matrix \(B\):

\[\begin{split}C & := \alpha AB + \beta C \quad (\mathrm{side} = \mathrm{'L'}), \\ C & := \alpha BA + \beta C \quad (\mathrm{side} = \mathrm{'R'}).\end{split}\]

The arguments A, B, and C must have the same type ('d' or 'z'). Complex values of alpha and beta are only allowed if A is complex.

cvxopt.blas.trmm(A, B[, side = 'L', uplo = 'L', transA = 'N', diag = 'N', alpha = 1.0])

Product of a triangular matrix \(A\) and a general matrix \(B\):

\[\begin{split}\newcommand{\op}{\mathop{\mathrm{op}}} \begin{split} B & := \alpha\op(A)B \quad (\mathrm{side} = \mathrm{'L'}), \\ B & := \alpha B\op(A) \quad (\mathrm{side} = \mathrm{'R'}) \end{split}\end{split}\]

where

\[\begin{split}\newcommand{\op}{\mathop{\mathrm{op}}} \op(A) = \left\{ \begin{array}{ll} A & \mathrm{transA} = \mathrm{'N'} \\ A^T & \mathrm{transA} = \mathrm{'T'} \\ A^H & \mathrm{transA} = \mathrm{'C'}. \end{array} \right.\end{split}\]

The arguments A and B must have the same type ('d' or 'z'). Complex values of alpha are only allowed if A is complex.

cvxopt.blas.trsm(A, B[, side = 'L', uplo = 'L', transA = 'N', diag = 'N', alpha = 1.0])

Solution of a nonsingular triangular system of equations:

\[\begin{split}\newcommand{\op}{\mathop{\mathrm{op}}} \begin{split} B & := \alpha \op(A)^{-1}B \quad (\mathrm{side} = \mathrm{'L'}), \\ B & := \alpha B\op(A)^{-1} \quad (\mathrm{side} = \mathrm{'R'}), \end{split}\end{split}\]

where

\[\begin{split}\newcommand{\op}{\mathop{\mathrm{op}}} \op(A) = \left\{ \begin{array}{ll} A & \mathrm{transA} = \mathrm{'N'} \\ A^T & \mathrm{transA} = \mathrm{'T'} \\ A^H & \mathrm{transA} = \mathrm{'C'}, \end{array} \right.\end{split}\]

\(A\) is triangular and \(B\) is a general matrix. The arguments A and B must have the same type ('d' or 'z'). Complex values of alpha are only allowed if A is complex.

cvxopt.blas.syrk(A, C[, uplo = 'L', trans = 'N', alpha = 1.0, beta = 0.0])

Rank-\(k\) update of a real or complex symmetric matrix \(C\):

\[\begin{split}C & := \alpha AA^T + \beta C \quad (\mathrm{trans} = \mathrm{'N'}), \\ C & := \alpha A^TA + \beta C \quad (\mathrm{trans} = \mathrm{'T'}),\end{split}\]

where \(A\) is a general matrix. The arguments A and C must have the same type ('d' or 'z'). Complex values of alpha and beta are only allowed if A is complex.

cvxopt.blas.herk(A, C[, uplo = 'L', trans = 'N', alpha = 1.0, beta = 0.0])

Rank-\(k\) update of a real symmetric or complex Hermitian matrix \(C\):

\[\begin{split}C & := \alpha AA^H + \beta C \quad (\mathrm{trans} = \mathrm{'N'}), \\ C & := \alpha A^HA + \beta C \quad (\mathrm{trans} = \mathrm{'C'}),\end{split}\]

where \(A\) is a general matrix. The arguments A and C must have the same type ('d' or 'z'). alpha and beta must be real.

cvxopt.blas.syr2k(A, B, C[, uplo = 'L', trans = 'N', alpha = 1.0, beta = 0.0])

Rank-\(2k\) update of a real or complex symmetric matrix \(C\):

\[\begin{split}C & := \alpha (AB^T + BA^T) + \beta C \quad (\mathrm{trans} = \mathrm{'N'}), \\ C & := \alpha (A^TB + B^TA) + \beta C \quad (\mathrm{trans} = \mathrm{'T'}).\end{split}\]

\(A\) and \(B\) are general real or complex matrices. The arguments A, B, and C must have the same type. Complex values of alpha and beta are only allowed if A is complex.

cvxopt.blas.her2k(A, B, C[, uplo = 'L', trans = 'N', alpha = 1.0, beta = 0.0])

Rank-\(2k\) update of a real symmetric or complex Hermitian matrix \(C\):

\[\begin{split}C & := \alpha AB^H + \bar \alpha BA^H + \beta C \quad (\mathrm{trans} = \mathrm{'N'}), \\ C & := \alpha A^HB + \bar\alpha B^HA + \beta C \quad (\mathrm{trans} = \mathrm{'C'}),\end{split}\]

where \(A\) and \(B\) are general matrices. The arguments A, B, and C must have the same type ('d' or 'z'). Complex values of alpha are only allowed if A is complex. beta must be real.