# 14.9 Class LinAlg¶

mosek.LinAlg

BLAS/LAPACK linear algebra routines.

Static members:

LinAlg.axpy – Computes vector addition and multiplication by a scalar.

LinAlg.dot – Computes the inner product of two vectors.

LinAlg.gemm – Performs a dense matrix multiplication.

LinAlg.gemv – Computes dense matrix times a dense vector product.

LinAlg.potrf – Computes a Cholesky factorization of a dense matrix.

LinAlg.syeig – Computes all eigenvalues of a symmetric dense matrix.

LinAlg.syevd – Computes all the eigenvalues and eigenvectors of a symmetric dense matrix, and thus its eigenvalue decomposition.

LinAlg.syrk – Performs a rank-k update of a symmetric matrix.

LinAlg.axpy
LinAlg.axpy(int n, float alpha, float[] x, float[] y)


Adds $$\alpha x$$ to $$y$$, i.e. performs the update

$y := \alpha x + y.$

Note that the result is stored overwriting $$y$$. It must not overlap with the other input arrays.

Parameters:
• n (int) – Length of the vectors.

• alpha (float) – The scalar that multiplies $$x$$.

• x (float[]) – The $$x$$ vector.

• y (float[]) – The $$y$$ vector.

LinAlg.dot
LinAlg.dot(int n, float[] x, float[] y) -> float


Computes the inner product of two vectors $$x,y$$ of length $$n\geq 0$$, i.e

$x\cdot y= \sum_{i=1}^n x_i y_i.$

Note that if $$n=0$$, then the result of the operation is 0.

Parameters:
• n (int) – Length of the vectors.

• x (float[]) – The $$x$$ vector.

• y (float[]) – The $$y$$ vector.

Return:

(float)

LinAlg.gemm
LinAlg.gemm(mosek.transpose transa, mosek.transpose transb, int m, int n, int k, float alpha, float[] a, float[] b, float beta, float[] c)


Performs a matrix multiplication plus addition of dense matrices. Given $$A$$, $$B$$ and $$C$$ of compatible dimensions, this function computes

$C:= \alpha op(A)op(B) + \beta C$

where $$\alpha,\beta$$ are two scalar values. The function $$op(X)$$ denotes $$X$$ if transX is NO, or $$X^T$$ if set to YES. The matrix $$C$$ has $$m$$ rows and $$n$$ columns, and the other matrices must have compatible dimensions.

The result of this operation is stored in $$C$$. It must not overlap with the other input arrays.

Parameters:
• transa (transpose) – Indicates if $$A$$ should be transposed. See the Optimizer API documentation for the definition of these constants.

• transb (transpose) – Indicates if $$B$$ should be transposed. See the Optimizer API documentation for the definition of these constants.

• m (int) – Indicates the number of rows of matrix $$C$$.

• n (int) – Indicates the number of columns of matrix $$C$$.

• k (int) – Specifies the common dimension along which $$op(A)$$ and $$op(B)$$ are multiplied. For example, if neither $$A$$ nor $$B$$ are transposed, then this is the number of columns in $$A$$ and also the number of rows in $$B$$.

• alpha (float) – A scalar value multiplying the result of the matrix multiplication.

• a (float[]) – The pointer to the array storing matrix $$A$$ in a column-major format.

• b (float[]) – The pointer to the array storing matrix $$B$$ in a column-major format.

• beta (float) – A scalar value that multiplies $$C$$.

• c (float[]) – The pointer to the array storing matrix $$C$$ in a column-major format.

LinAlg.gemv
LinAlg.gemv(mosek.transpose trans, int m, int n, float alpha, float[] a, float[] x, float beta, float[] y)


Computes the multiplication of a scaled dense matrix times a dense vector, plus a scaled dense vector. Precisely, if trans is NO then the update is

$y := \alpha A x + \beta y,$

and if trans is YES then

$y := \alpha A^T x + \beta y,$

where $$\alpha,\beta$$ are scalar values and $$A$$ is a matrix with $$m$$ rows and $$n$$ columns.

Note that the result is stored overwriting $$y$$. It must not overlap with the other input arrays.

Parameters:
• trans (transpose) – Indicates if $$A$$ should be transposed. See the Optimizer API documentation for the definition of these constants.

• m (int) – Specifies the number of rows of the matrix $$A$$.

• n (int) – Specifies the number of columns of the matrix $$A$$.

• alpha (float) – A scalar value multiplying the matrix $$A$$.

• a (float[]) – A pointer to the array storing matrix $$A$$ in a column-major format.

• x (float[]) – A pointer to the array storing the vector $$x$$.

• beta (float) – A scalar value multiplying the vector $$y$$.

• y (float[]) – A pointer to the array storing the vector $$y$$.

LinAlg.potrf
LinAlg.potrf(mosek.uplo uplo, int n, float[] a)


Computes a Cholesky factorization of a real symmetric positive definite dense matrix.

Parameters:
• uplo (uplo) – Indicates whether the upper or lower triangular part of the matrix is used. See the Optimizer API documentation for the definition of these constants.

• n (int) – Specifies the dimension of the symmetric matrix.

• a (float[]) – A symmetric matrix stored in column-major order. Only the lower or the upper triangular part is used, accordingly with the uplo argument. It will contain the result on exit.

LinAlg.syeig
LinAlg.syeig(mosek.uplo uplo, int n, float[] a, float[] w)


Computes all eigenvalues of a real symmetric matrix $$A$$. Given a matrix $$A\in\real^{n\times n}$$ it returns a vector $$w\in\real^n$$ containing the eigenvalues of $$A$$.

Parameters:
• uplo (uplo) – Indicates whether the upper or lower triangular part of the matrix is used. See the Optimizer API documentation for the definition of these constants.

• n (int) – Specifies the dimension of the symmetric matrix.

• a (float[]) – A symmetric matrix stored in column-major order. Only the lower or the upper triangular part is used, accordingly with the uplo argument. It will contain the result on exit.

• w (float[]) – Array of length at least n containing the eigenvalues of $$A$$.

LinAlg.syevd
LinAlg.syevd(mosek.uplo uplo, int n, float[] a, float[] w)


Computes all the eigenvalues and eigenvectors a real symmetric matrix. Given the input matrix $$A\in \real^{n\times n}$$, this function returns a vector $$w\in \real^n$$ containing the eigenvalues of $$A$$ and it also computes the eigenvectors of $$A$$. Therefore, this function computes the eigenvalue decomposition of $$A$$ as

$A= U V U^T,$

where $$V=\diag(w)$$ and $$U$$ contains the eigenvectors of $$A$$.

Note that the matrix $$U$$ overwrites the input data $$A$$.

Parameters:
• uplo (uplo) – Indicates whether the upper or lower triangular part of the matrix is used. See the Optimizer API documentation for the definition of these constants.

• n (int) – Specifies the dimension of the symmetric matrix.

• a (float[]) – A symmetric matrix stored in column-major order. Only the lower or the upper triangular part is used, accordingly with the uplo argument. It will contain the result on exit.

• w (float[]) – Array of length at least n containing the eigenvalues of $$A$$.

LinAlg.syrk
LinAlg.syrk(mosek.uplo uplo, mosek.transpose trans, int n, int k, float alpha, float[] a, float beta, float[] c)


Performs a symmetric rank-$$k$$ update for a symmetric matrix.

Given a symmetric matrix $$C\in \real^{n\times n}$$, two scalars $$\alpha,\beta$$ and a matrix $$A$$ of rank $$k\leq n$$, it computes either

$C := \alpha A A^T + \beta C,$

when trans is set to NO and $$A\in \real^{n\times k}$$, or

$C := \alpha A^T A + \beta C,$

when trans is set to YES and $$A\in \real^{k\times n}$$.

Only the part of $$C$$ indicated by uplo is used and only that part is updated with the result. It must not overlap with the other input arrays.

Parameters:
• uplo (uplo) – Indicates whether the upper or lower triangular part of $$C$$ is used. See the Optimizer API documentation for the definition of these constants.

• trans (transpose) – Indicates if $$A$$ should be transposed. See the Optimizer API documentation for the definition of these constants.

• n (int) – Specifies the order of $$C$$.

• k (int) – Indicates the number of rows or columns of $$A$$, depending on whether or not it is transposed, and its rank.

• alpha (float) – A scalar value multiplying the result of the matrix multiplication.

• a (float[]) – The pointer to the array storing matrix $$A$$ in a column-major format.

• beta (float) – A scalar value that multiplies $$C$$.

• c (float[]) – The pointer to the array storing matrix $$C$$ in a column-major format.