Linear Algebra Symbol API¶

Overview¶

This document lists the linear algebra routines of the symbolic expression package:

mxnet.symbol.linalg Linear Algebra Symbol API of MXNet.

The Linear Algebra Symbol API, defined in the symbol.linalg package, provides symbolic expressions for linear algebra routines.

In the rest of this document, we list routines provided by the symbol.linalg package.

Linear Algebra¶

`gemm`	Performs general matrix multiplication and accumulation.
`gemm2`	Performs general matrix multiplication.
`potrf`	Performs Cholesky factorization of a symmetric positive-definite matrix.
`potri`	Performs matrix inversion from a Cholesky factorization.
`trmm`	Performs multiplication with a lower triangular matrix.
`trsm`	Solves matrix equation involving a lower triangular matrix.
`sumlogdiag`	Computes the sum of the logarithms of the diagonal elements of a square matrix.
`syrk`	Multiplication of matrix with its transpose.
`gelqf`	LQ factorization for general matrix.
`syevd`	Eigendecomposition for symmetric matrix.

API Reference¶

Linear Algebra Symbol API of MXNet.

mxnet.symbol.linalg.gelqf(A=None, name=None, attr=None, out=None, **kwargs)¶

LQ factorization for general matrix. Input is a tensor A of dimension n >= 2.

If n=2, we compute the LQ factorization (LAPACK gelqf, followed by orglq). A must have shape (x, y) with x <= y, and must have full rank =x. The LQ factorization consists of L with shape (x, x) and Q with shape (x, y), so that:

A = L * Q

Here, L is lower triangular (upper triangle equal to zero) with nonzero diagonal, and Q is row-orthonormal, meaning that

Q * Q^T

is equal to the identity matrix of shape (x, x).

If n>2, gelqf is performed separately on the trailing two dimensions for all inputs (batch mode).

Note

The operator supports float32 and float64 data types only.

Examples:

// Single LQ factorization
A = [[1., 2., 3.], [4., 5., 6.]]
Q, L = gelqf(A)
Q = [[-0.26726124, -0.53452248, -0.80178373],
     [0.87287156, 0.21821789, -0.43643578]]
L = [[-3.74165739, 0.],
     [-8.55235974, 1.96396101]]

// Batch LQ factorization
A = [[[1., 2., 3.], [4., 5., 6.]],
     [[7., 8., 9.], [10., 11., 12.]]]
Q, L = gelqf(A)
Q = [[[-0.26726124, -0.53452248, -0.80178373],
      [0.87287156, 0.21821789, -0.43643578]],
     [[-0.50257071, -0.57436653, -0.64616234],
      [0.7620735, 0.05862104, -0.64483142]]]
L = [[[-3.74165739, 0.],
      [-8.55235974, 1.96396101]],
     [[-13.92838828, 0.],
      [-19.09768702, 0.52758934]]]

Defined in src/operator/tensor/la_op.cc:L528

Parameters:	A (Symbol) – Tensor of input matrices to be factorized name (string, optional.) – Name of the resulting symbol.
Returns:	The result symbol.
Return type:	Symbol

mxnet.symbol.linalg.gemm(A=None, B=None, C=None, transpose_a=_Null, transpose_b=_Null, alpha=_Null, beta=_Null, name=None, attr=None, out=None, **kwargs)¶

Performs general matrix multiplication and accumulation. Input are tensors A, B, C, each of dimension n >= 2 and having the same shape on the leading n-2 dimensions.

If n=2, the BLAS3 function gemm is performed:

out = alpha * op(A) * op(B) + beta * C

Here, alpha and beta are scalar parameters, and op() is either the identity or matrix transposition (depending on transpose_a, transpose_b).

If n>2, gemm is performed separately on the trailing two dimensions for all inputs (batch mode).

Note

The operator supports float32 and float64 data types only.

Examples:

// Single matrix multiply-add
A = [[1.0, 1.0], [1.0, 1.0]]
B = [[1.0, 1.0], [1.0, 1.0], [1.0, 1.0]]
C = [[1.0, 1.0, 1.0], [1.0, 1.0, 1.0]]
gemm(A, B, C, transpose_b=True, alpha=2.0, beta=10.0)
        = [[14.0, 14.0, 14.0], [14.0, 14.0, 14.0]]

// Batch matrix multiply-add
A = [[[1.0, 1.0]], [[0.1, 0.1]]]
B = [[[1.0, 1.0]], [[0.1, 0.1]]]
C = [[[10.0]], [[0.01]]]
gemm(A, B, C, transpose_b=True, alpha=2.0 , beta=10.0)
        = [[[104.0]], [[0.14]]]

Defined in src/operator/tensor/la_op.cc:L68

Parameters:	A (Symbol) – Tensor of input matrices B (Symbol) – Tensor of input matrices C (Symbol) – Tensor of input matrices transpose_a (boolean, optional, default=0) – Multiply with transposed of first input (A). transpose_b (boolean, optional, default=0) – Multiply with transposed of second input (B). alpha (double, optional, default=1) – Scalar factor multiplied with AB. beta* (double, optional, default=1) – Scalar factor multiplied with C. name (string, optional.) – Name of the resulting symbol.
Returns:	The result symbol.
Return type:	Symbol

mxnet.symbol.linalg.gemm2(A=None, B=None, transpose_a=_Null, transpose_b=_Null, alpha=_Null, name=None, attr=None, out=None, **kwargs)¶

Performs general matrix multiplication. Input are tensors A, B, each of dimension n >= 2 and having the same shape on the leading n-2 dimensions.

If n=2, the BLAS3 function gemm is performed:

out = alpha * op(A) * op(B)

Here alpha is a scalar parameter and op() is either the identity or the matrix transposition (depending on transpose_a, transpose_b).

If n>2, gemm is performed separately on the trailing two dimensions for all inputs (batch mode).

Note

The operator supports float32 and float64 data types only.

Examples:

// Single matrix multiply
A = [[1.0, 1.0], [1.0, 1.0]]
B = [[1.0, 1.0], [1.0, 1.0], [1.0, 1.0]]
gemm2(A, B, transpose_b=True, alpha=2.0)
         = [[4.0, 4.0, 4.0], [4.0, 4.0, 4.0]]

// Batch matrix multiply
A = [[[1.0, 1.0]], [[0.1, 0.1]]]
B = [[[1.0, 1.0]], [[0.1, 0.1]]]
gemm2(A, B, transpose_b=True, alpha=2.0)
        = [[[4.0]], [[0.04 ]]]

Defined in src/operator/tensor/la_op.cc:L127

Parameters:	A (Symbol) – Tensor of input matrices B (Symbol) – Tensor of input matrices transpose_a (boolean, optional, default=0) – Multiply with transposed of first input (A). transpose_b (boolean, optional, default=0) – Multiply with transposed of second input (B). alpha (double, optional, default=1) – Scalar factor multiplied with AB. name* (string, optional.) – Name of the resulting symbol.
Returns:	The result symbol.
Return type:	Symbol

mxnet.symbol.linalg.potrf(A=None, name=None, attr=None, out=None, **kwargs)¶

Performs Cholesky factorization of a symmetric positive-definite matrix. Input is a tensor A of dimension n >= 2.

If n=2, the Cholesky factor L of the symmetric, positive definite matrix A is computed. L is lower triangular (entries of upper triangle are all zero), has positive diagonal entries, and:

A = L * L^T

If n>2, potrf is performed separately on the trailing two dimensions for all inputs (batch mode).

Note

The operator supports float32 and float64 data types only.

Examples:

// Single matrix factorization
A = [[4.0, 1.0], [1.0, 4.25]]
potrf(A) = [[2.0, 0], [0.5, 2.0]]

// Batch matrix factorization
A = [[[4.0, 1.0], [1.0, 4.25]], [[16.0, 4.0], [4.0, 17.0]]]
potrf(A) = [[[2.0, 0], [0.5, 2.0]], [[4.0, 0], [1.0, 4.0]]]

Defined in src/operator/tensor/la_op.cc:L177

Parameters:	A (Symbol) – Tensor of input matrices to be decomposed name (string, optional.) – Name of the resulting symbol.
Returns:	The result symbol.
Return type:	Symbol

mxnet.symbol.linalg.potri(A=None, name=None, attr=None, out=None, **kwargs)¶

Performs matrix inversion from a Cholesky factorization. Input is a tensor A of dimension n >= 2.

If n=2, A is a lower triangular matrix (entries of upper triangle are all zero) with positive diagonal. We compute:

out = A^-T * A^-1

In other words, if A is the Cholesky factor of a symmetric positive definite matrix B (obtained by potrf), then

out = B^-1

If n>2, potri is performed separately on the trailing two dimensions for all inputs (batch mode).

Note

The operator supports float32 and float64 data types only.

Note

Use this operator only if you are certain you need the inverse of B, and cannot use the Cholesky factor A (potrf), together with backsubstitution (trsm). The latter is numerically much safer, and also cheaper.

Examples:

// Single matrix inverse
A = [[2.0, 0], [0.5, 2.0]]
potri(A) = [[0.26563, -0.0625], [-0.0625, 0.25]]

// Batch matrix inverse
A = [[[2.0, 0], [0.5, 2.0]], [[4.0, 0], [1.0, 4.0]]]
potri(A) = [[[0.26563, -0.0625], [-0.0625, 0.25]],
            [[0.06641, -0.01562], [-0.01562, 0,0625]]]

Defined in src/operator/tensor/la_op.cc:L235

Parameters:	A (Symbol) – Tensor of lower triangular matrices name (string, optional.) – Name of the resulting symbol.
Returns:	The result symbol.
Return type:	Symbol

mxnet.symbol.linalg.sumlogdiag(A=None, name=None, attr=None, out=None, **kwargs)¶

Computes the sum of the logarithms of the diagonal elements of a square matrix. Input is a tensor A of dimension n >= 2.

If n=2, A must be square with positive diagonal entries. We sum the natural logarithms of the diagonal elements, the result has shape (1,).

If n>2, sumlogdiag is performed separately on the trailing two dimensions for all inputs (batch mode).

Note

The operator supports float32 and float64 data types only.

Examples:

// Single matrix reduction
A = [[1.0, 1.0], [1.0, 7.0]]
sumlogdiag(A) = [1.9459]

// Batch matrix reduction
A = [[[1.0, 1.0], [1.0, 7.0]], [[3.0, 0], [0, 17.0]]]
sumlogdiag(A) = [1.9459, 3.9318]

Defined in src/operator/tensor/la_op.cc:L404

Parameters:	A (Symbol) – Tensor of square matrices name (string, optional.) – Name of the resulting symbol.
Returns:	The result symbol.
Return type:	Symbol

mxnet.symbol.linalg.syevd(A=None, name=None, attr=None, out=None, **kwargs)¶

Eigendecomposition for symmetric matrix. Input is a tensor A of dimension n >= 2.

If n=2, A must be symmetric, of shape (x, x). We compute the eigendecomposition, resulting in the orthonormal matrix U of eigenvectors, shape (x, x), and the vector L of eigenvalues, shape (x,), so that:

U * A = diag(L) * U

Here:

U * U^T = U^T * U = I

where I is the identity matrix. Also, L(0) <= L(1) <= L(2) <= ... (ascending order).

If n>2, syevd is performed separately on the trailing two dimensions of A (batch mode). In this case, U has n dimensions like A, and L has n-1 dimensions.

Note

The operator supports float32 and float64 data types only.

Note

Derivatives for this operator are defined only if A is such that all its eigenvalues are distinct, and the eigengaps are not too small. If you need gradients, do not apply this operator to matrices with multiple eigenvalues.

Examples:

// Single symmetric eigendecomposition
A = [[1., 2.], [2., 4.]]
U, L = syevd(A)
U = [[0.89442719, -0.4472136],
     [0.4472136, 0.89442719]]
L = [0., 5.]

// Batch symmetric eigendecomposition
A = [[[1., 2.], [2., 4.]],
     [[1., 2.], [2., 5.]]]
U, L = syevd(A)
U = [[[0.89442719, -0.4472136],
      [0.4472136, 0.89442719]],
     [[0.92387953, -0.38268343],
      [0.38268343, 0.92387953]]]
L = [[0., 5.],
     [0.17157288, 5.82842712]]

Defined in src/operator/tensor/la_op.cc:L597

Parameters:	A (Symbol) – Tensor of input matrices to be factorized name (string, optional.) – Name of the resulting symbol.
Returns:	The result symbol.
Return type:	Symbol

mxnet.symbol.linalg.syrk(A=None, transpose=_Null, alpha=_Null, name=None, attr=None, out=None, **kwargs)¶

Multiplication of matrix with its transpose. Input is a tensor A of dimension n >= 2.

If n=2, the operator performs the BLAS3 function syrk:

out = alpha * A * A^T

if transpose=False, or

out = alpha * A^T * A

if transpose=True.

If n>2, syrk is performed separately on the trailing two dimensions for all inputs (batch mode).

Note

The operator supports float32 and float64 data types only.

Examples:

// Single matrix multiply
A = [[1., 2., 3.], [4., 5., 6.]]
syrk(A, alpha=1., transpose=False)
         = [[14., 32.],
            [32., 77.]]
syrk(A, alpha=1., transpose=True)
         = [[17., 22., 27.],
            [22., 29., 36.],
            [27., 36., 45.]]

// Batch matrix multiply
A = [[[1., 1.]], [[0.1, 0.1]]]
syrk(A, alpha=2., transpose=False) = [[[4.]], [[0.04]]]

Defined in src/operator/tensor/la_op.cc:L460

Parameters:	A (Symbol) – Tensor of input matrices transpose (boolean, optional, default=0) – Use transpose of input matrix. alpha (double, optional, default=1) – Scalar factor to be applied to the result. name (string, optional.) – Name of the resulting symbol.
Returns:	The result symbol.
Return type:	Symbol

mxnet.symbol.linalg.trmm(A=None, B=None, transpose=_Null, rightside=_Null, alpha=_Null, name=None, attr=None, out=None, **kwargs)¶

Performs multiplication with a lower triangular matrix. Input are tensors A, B, each of dimension n >= 2 and having the same shape on the leading n-2 dimensions.

If n=2, A must be lower triangular. The operator performs the BLAS3 function trmm:

out = alpha * op(A) * B

if rightside=False, or

out = alpha * B * op(A)

if rightside=True. Here, alpha is a scalar parameter, and op() is either the identity or the matrix transposition (depending on transpose).

If n>2, trmm is performed separately on the trailing two dimensions for all inputs (batch mode).

Note

The operator supports float32 and float64 data types only.

Examples:

// Single triangular matrix multiply
A = [[1.0, 0], [1.0, 1.0]]
B = [[1.0, 1.0, 1.0], [1.0, 1.0, 1.0]]
trmm(A, B, alpha=2.0) = [[2.0, 2.0, 2.0], [4.0, 4.0, 4.0]]

// Batch triangular matrix multiply
A = [[[1.0, 0], [1.0, 1.0]], [[1.0, 0], [1.0, 1.0]]]
B = [[[1.0, 1.0, 1.0], [1.0, 1.0, 1.0]], [[0.5, 0.5, 0.5], [0.5, 0.5, 0.5]]]
trmm(A, B, alpha=2.0) = [[[2.0, 2.0, 2.0], [4.0, 4.0, 4.0]],
                         [[1.0, 1.0, 1.0], [2.0, 2.0, 2.0]]]

Defined in src/operator/tensor/la_op.cc:L292

Parameters:	A (Symbol) – Tensor of lower triangular matrices B (Symbol) – Tensor of matrices transpose (boolean, optional, default=0) – Use transposed of the triangular matrix rightside (boolean, optional, default=0) – Multiply triangular matrix from the right to non-triangular one. alpha (double, optional, default=1) – Scalar factor to be applied to the result. name (string, optional.) – Name of the resulting symbol.
Returns:	The result symbol.
Return type:	Symbol

mxnet.symbol.linalg.trsm(A=None, B=None, transpose=_Null, rightside=_Null, alpha=_Null, name=None, attr=None, out=None, **kwargs)¶

Solves matrix equation involving a lower triangular matrix. Input are tensors A, B, each of dimension n >= 2 and having the same shape on the leading n-2 dimensions.

If n=2, A must be lower triangular. The operator performs the BLAS3 function trsm, solving for out in:

op(A) * out = alpha * B

if rightside=False, or

out * op(A) = alpha * B

if rightside=True. Here, alpha is a scalar parameter, and op() is either the identity or the matrix transposition (depending on transpose).

If n>2, trsm is performed separately on the trailing two dimensions for all inputs (batch mode).

Note

The operator supports float32 and float64 data types only.

Examples:

// Single matrix solve
A = [[1.0, 0], [1.0, 1.0]]
B = [[2.0, 2.0, 2.0], [4.0, 4.0, 4.0]]
trsm(A, B, alpha=0.5) = [[1.0, 1.0, 1.0], [1.0, 1.0, 1.0]]

// Batch matrix solve
A = [[[1.0, 0], [1.0, 1.0]], [[1.0, 0], [1.0, 1.0]]]
B = [[[2.0, 2.0, 2.0], [4.0, 4.0, 4.0]],
     [[4.0, 4.0, 4.0], [8.0, 8.0, 8.0]]]
trsm(A, B, alpha=0.5) = [[[1.0, 1.0, 1.0], [1.0, 1.0, 1.0]],
                         [[2.0, 2.0, 2.0], [2.0, 2.0, 2.0]]]

Defined in src/operator/tensor/la_op.cc:L355

Parameters:	A (Symbol) – Tensor of lower triangular matrices B (Symbol) – Tensor of matrices transpose (boolean, optional, default=0) – Use transposed of the triangular matrix rightside (boolean, optional, default=0) – Multiply triangular matrix from the right to non-triangular one. alpha (double, optional, default=1) – Scalar factor to be applied to the result. name (string, optional.) – Name of the resulting symbol.
Returns:	The result symbol.
Return type:	Symbol