# ndarray.linalg¶

Linear Algebra NDArray API of MXNet.

Functions

 det([A, out, name]) Compute the determinant of a matrix. extractdiag([A, offset, out, name]) Extracts the diagonal entries of a square matrix. extracttrian([A, offset, lower, out, name]) Extracts a triangular sub-matrix from a square matrix. gelqf([A, out, name]) LQ factorization for general matrix. gemm([A, B, C, transpose_a, transpose_b, …]) Performs general matrix multiplication and accumulation. gemm2([A, B, transpose_a, transpose_b, …]) Performs general matrix multiplication. inverse([A, out, name]) Compute the inverse of a matrix. makediag([A, offset, out, name]) Constructs a square matrix with the input as diagonal. maketrian([A, offset, lower, out, name]) Constructs a square matrix with the input representing a specific triangular sub-matrix. potrf([A, out, name]) Performs Cholesky factorization of a symmetric positive-definite matrix. potri([A, out, name]) Performs matrix inversion from a Cholesky factorization. slogdet([A, out, name]) Compute the sign and log of the determinant of a matrix. sumlogdiag([A, out, name]) Computes the sum of the logarithms of the diagonal elements of a square matrix. syevd([A, out, name]) Eigendecomposition for symmetric matrix. syrk([A, transpose, alpha, out, name]) Multiplication of matrix with its transpose. trmm([A, B, transpose, rightside, lower, …]) Performs multiplication with a lower triangular matrix. trsm([A, B, transpose, rightside, lower, …]) Solves matrix equation involving a lower triangular matrix.
mxnet.ndarray.linalg.det(A=None, out=None, name=None, **kwargs)

Compute the determinant of a matrix. Input is a tensor A of dimension n >= 2.

If n=2, A is a square matrix. We compute:

out = det(A)

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

Note

The operator supports float32 and float64 data types only.

Note

There is no gradient backwarded when A is non-invertible (which is equivalent to det(A) = 0) because zero is rarely hit upon in float point computation and the Jacobi’s formula on determinant gradient is not computationally efficient when A is non-invertible.

Examples:

Single matrix determinant
A = [[1., 4.], [2., 3.]]
det(A) = [-5.]

Batch matrix determinant
A = [[[1., 4.], [2., 3.]],
[[2., 3.], [1., 4.]]]
det(A) = [-5., 5.]


Defined in src/operator/tensor/la_op.cc:L973

Parameters
• A (NDArray) – Tensor of square matrix

• out (NDArray, optional) – The output NDArray to hold the result.

Returns

out – The output of this function.

Return type

NDArray or list of NDArrays

mxnet.ndarray.linalg.extractdiag(A=None, offset=_Null, out=None, name=None, **kwargs)

Extracts the diagonal entries of a square matrix. Input is a tensor A of dimension n >= 2.

If n=2, then A represents a single square matrix which diagonal elements get extracted as a 1-dimensional tensor.

If n>2, then A represents a batch of square matrices on the trailing two dimensions. The extracted diagonals are returned as an n-1-dimensional tensor.

Note

The operator supports float32 and float64 data types only.

Examples:

Single matrix diagonal extraction
A = [[1.0, 2.0],
[3.0, 4.0]]

extractdiag(A) = [1.0, 4.0]

extractdiag(A, 1) = [2.0]

Batch matrix diagonal extraction
A = [[[1.0, 2.0],
[3.0, 4.0]],
[[5.0, 6.0],
[7.0, 8.0]]]

extractdiag(A) = [[1.0, 4.0],
[5.0, 8.0]]


Defined in src/operator/tensor/la_op.cc:L495

Parameters
• A (NDArray) – Tensor of square matrices

• offset (int, optional, default='0') – Offset of the diagonal versus the main diagonal. 0 corresponds to the main diagonal, a negative/positive value to diagonals below/above the main diagonal.

• out (NDArray, optional) – The output NDArray to hold the result.

Returns

out – The output of this function.

Return type

NDArray or list of NDArrays

mxnet.ndarray.linalg.extracttrian(A=None, offset=_Null, lower=_Null, out=None, name=None, **kwargs)

Extracts a triangular sub-matrix from a square matrix. Input is a tensor A of dimension n >= 2.

If n=2, then A represents a single square matrix from which a triangular sub-matrix is extracted as a 1-dimensional tensor.

If n>2, then A represents a batch of square matrices on the trailing two dimensions. The extracted triangular sub-matrices are returned as an n-1-dimensional tensor.

The offset and lower parameters determine the triangle to be extracted:

• When offset = 0 either the lower or upper triangle with respect to the main diagonal is extracted depending on the value of parameter lower.

• When offset = k > 0 the upper triangle with respect to the k-th diagonal above the main diagonal is extracted.

• When offset = k < 0 the lower triangle with respect to the k-th diagonal below the main diagonal is extracted.

Note

The operator supports float32 and float64 data types only.

Examples:

Single triagonal extraction
A = [[1.0, 2.0],
[3.0, 4.0]]

extracttrian(A) = [1.0, 3.0, 4.0]
extracttrian(A, lower=False) = [1.0, 2.0, 4.0]
extracttrian(A, 1) = [2.0]
extracttrian(A, -1) = [3.0]

Batch triagonal extraction
A = [[[1.0, 2.0],
[3.0, 4.0]],
[[5.0, 6.0],
[7.0, 8.0]]]

extracttrian(A) = [[1.0, 3.0, 4.0],
[5.0, 7.0, 8.0]]


Defined in src/operator/tensor/la_op.cc:L605

Parameters
• A (NDArray) – Tensor of square matrices

• offset (int, optional, default='0') – Offset of the diagonal versus the main diagonal. 0 corresponds to the main diagonal, a negative/positive value to diagonals below/above the main diagonal.

• lower (boolean, optional, default=1) – Refer to the lower triangular matrix if lower=true, refer to the upper otherwise. Only relevant when offset=0

• out (NDArray, optional) – The output NDArray to hold the result.

Returns

out – The output of this function.

Return type

NDArray or list of NDArrays

mxnet.ndarray.linalg.gelqf(A=None, out=None, name=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 * QT

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:L798

Parameters
• A (NDArray) – Tensor of input matrices to be factorized

• out (NDArray, optional) – The output NDArray to hold the result.

Returns

out – The output of this function.

Return type

NDArray or list of NDArrays

mxnet.ndarray.linalg.gemm(A=None, B=None, C=None, transpose_a=_Null, transpose_b=_Null, alpha=_Null, beta=_Null, axis=_Null, out=None, name=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 for a batch of matrices. The column indices of the matrices are given by the last dimensions of the tensors, the row indices by the axis specified with the axis parameter. By default, the trailing two dimensions will be used for matrix encoding.

For a non-default axis parameter, the operation performed is equivalent to a series of swapaxes/gemm/swapaxes calls. For example let A, B, C be 5 dimensional tensors. Then gemm(A, B, C, axis=1) is equivalent to the following without the overhead of the additional swapaxis operations:

A1 = swapaxes(A, dim1=1, dim2=3)
B1 = swapaxes(B, dim1=1, dim2=3)
C = swapaxes(C, dim1=1, dim2=3)
C = gemm(A1, B1, C)
C = swapaxis(C, dim1=1, dim2=3)


When the input data is of type float32 and the environment variables MXNET_CUDA_ALLOW_TENSOR_CORE and MXNET_CUDA_TENSOR_OP_MATH_ALLOW_CONVERSION are set to 1, this operator will try to use pseudo-float16 precision (float32 math with float16 I/O) precision in order to use Tensor Cores on suitable NVIDIA GPUs. This can sometimes give significant speedups.

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]]

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:L89

Parameters
• A (NDArray) – Tensor of input matrices

• B (NDArray) – Tensor of input matrices

• C (NDArray) – 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 A*B.

• beta (double, optional, default=1) – Scalar factor multiplied with C.

• axis (int, optional, default='-2') – Axis corresponding to the matrix rows.

• out (NDArray, optional) – The output NDArray to hold the result.

Returns

out – The output of this function.

Return type

NDArray or list of NDArrays

mxnet.ndarray.linalg.gemm2(A=None, B=None, transpose_a=_Null, transpose_b=_Null, alpha=_Null, axis=_Null, out=None, name=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 for a batch of matrices. The column indices of the matrices are given by the last dimensions of the tensors, the row indices by the axis specified with the axis parameter. By default, the trailing two dimensions will be used for matrix encoding.

For a non-default axis parameter, the operation performed is equivalent to a series of swapaxes/gemm/swapaxes calls. For example let A, B be 5 dimensional tensors. Then gemm(A, B, axis=1) is equivalent to the following without the overhead of the additional swapaxis operations:

A1 = swapaxes(A, dim1=1, dim2=3)
B1 = swapaxes(B, dim1=1, dim2=3)
C = gemm2(A1, B1)
C = swapaxis(C, dim1=1, dim2=3)


When the input data is of type float32 and the environment variables MXNET_CUDA_ALLOW_TENSOR_CORE and MXNET_CUDA_TENSOR_OP_MATH_ALLOW_CONVERSION are set to 1, this operator will try to use pseudo-float16 precision (float32 math with float16 I/O) precision in order to use Tensor Cores on suitable NVIDIA GPUs. This can sometimes give significant speedups.

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:L163

Parameters
• A (NDArray) – Tensor of input matrices

• B (NDArray) – 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 A*B.

• axis (int, optional, default='-2') – Axis corresponding to the matrix row indices.

• out (NDArray, optional) – The output NDArray to hold the result.

Returns

out – The output of this function.

Return type

NDArray or list of NDArrays

mxnet.ndarray.linalg.inverse(A=None, out=None, name=None, **kwargs)

Compute the inverse of a matrix. Input is a tensor A of dimension n >= 2.

If n=2, A is a square matrix. We compute:

out = A-1

If n>2, inverse 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 inverse
A = [[1., 4.], [2., 3.]]
inverse(A) = [[-0.6, 0.8], [0.4, -0.2]]

Batch matrix inverse
A = [[[1., 4.], [2., 3.]],
[[1., 3.], [2., 4.]]]
inverse(A) = [[[-0.6, 0.8], [0.4, -0.2]],
[[-2., 1.5], [1., -0.5]]]


Defined in src/operator/tensor/la_op.cc:L919

Parameters
• A (NDArray) – Tensor of square matrix

• out (NDArray, optional) – The output NDArray to hold the result.

Returns

out – The output of this function.

Return type

NDArray or list of NDArrays

mxnet.ndarray.linalg.makediag(A=None, offset=_Null, out=None, name=None, **kwargs)

Constructs a square matrix with the input as diagonal. Input is a tensor A of dimension n >= 1.

If n=1, then A represents the diagonal entries of a single square matrix. This matrix will be returned as a 2-dimensional tensor. If n>1, then A represents a batch of diagonals of square matrices. The batch of diagonal matrices will be returned as an n+1-dimensional tensor.

Note

The operator supports float32 and float64 data types only.

Examples:

Single diagonal matrix construction
A = [1.0, 2.0]

makediag(A)    = [[1.0, 0.0],
[0.0, 2.0]]

makediag(A, 1) = [[0.0, 1.0, 0.0],
[0.0, 0.0, 2.0],
[0.0, 0.0, 0.0]]

Batch diagonal matrix construction
A = [[1.0, 2.0],
[3.0, 4.0]]

makediag(A) = [[[1.0, 0.0],
[0.0, 2.0]],
[[3.0, 0.0],
[0.0, 4.0]]]


Defined in src/operator/tensor/la_op.cc:L547

Parameters
• A (NDArray) – Tensor of diagonal entries

• offset (int, optional, default='0') – Offset of the diagonal versus the main diagonal. 0 corresponds to the main diagonal, a negative/positive value to diagonals below/above the main diagonal.

• out (NDArray, optional) – The output NDArray to hold the result.

Returns

out – The output of this function.

Return type

NDArray or list of NDArrays

mxnet.ndarray.linalg.maketrian(A=None, offset=_Null, lower=_Null, out=None, name=None, **kwargs)

Constructs a square matrix with the input representing a specific triangular sub-matrix. This is basically the inverse of linalg.extracttrian. Input is a tensor A of dimension n >= 1.

If n=1, then A represents the entries of a triangular matrix which is lower triangular if offset<0 or offset=0, lower=true. The resulting matrix is derived by first constructing the square matrix with the entries outside the triangle set to zero and then adding offset-times an additional diagonal with zero entries to the square matrix.

If n>1, then A represents a batch of triangular sub-matrices. The batch of corresponding square matrices is returned as an n+1-dimensional tensor.

Note

The operator supports float32 and float64 data types only.

Examples:

Single  matrix construction
A = [1.0, 2.0, 3.0]

maketrian(A)              = [[1.0, 0.0],
[2.0, 3.0]]

maketrian(A, lower=false) = [[1.0, 2.0],
[0.0, 3.0]]

maketrian(A, offset=1)    = [[0.0, 1.0, 2.0],
[0.0, 0.0, 3.0],
[0.0, 0.0, 0.0]]
maketrian(A, offset=-1)   = [[0.0, 0.0, 0.0],
[1.0, 0.0, 0.0],
[2.0, 3.0, 0.0]]

Batch matrix construction
A = [[1.0, 2.0, 3.0],
[4.0, 5.0, 6.0]]

maketrian(A)           = [[[1.0, 0.0],
[2.0, 3.0]],
[[4.0, 0.0],
[5.0, 6.0]]]

maketrian(A, offset=1) = [[[0.0, 1.0, 2.0],
[0.0, 0.0, 3.0],
[0.0, 0.0, 0.0]],
[[0.0, 4.0, 5.0],
[0.0, 0.0, 6.0],
[0.0, 0.0, 0.0]]]


Defined in src/operator/tensor/la_op.cc:L673

Parameters
• A (NDArray) – Tensor of triangular matrices stored as vectors

• offset (int, optional, default='0') – Offset of the diagonal versus the main diagonal. 0 corresponds to the main diagonal, a negative/positive value to diagonals below/above the main diagonal.

• lower (boolean, optional, default=1) – Refer to the lower triangular matrix if lower=true, refer to the upper otherwise. Only relevant when offset=0

• out (NDArray, optional) – The output NDArray to hold the result.

Returns

out – The output of this function.

Return type

NDArray or list of NDArrays

mxnet.ndarray.linalg.potrf(A=None, out=None, name=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 B of the symmetric, positive definite matrix A is computed. B is triangular (entries of upper or lower triangle are all zero), has positive diagonal entries, and:

A = B * BT if lower = true A = BT * B if lower = false

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:L214

Parameters
• A (NDArray) – Tensor of input matrices to be decomposed

• out (NDArray, optional) – The output NDArray to hold the result.

Returns

out – The output of this function.

Return type

NDArray or list of NDArrays

mxnet.ndarray.linalg.potri(A=None, out=None, name=None, **kwargs)

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

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

out = A-T * A-1 if lower = true out = A-1 * A-T if lower = false

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:L275

Parameters
• A (NDArray) – Tensor of lower triangular matrices

• out (NDArray, optional) – The output NDArray to hold the result.

Returns

out – The output of this function.

Return type

NDArray or list of NDArrays

mxnet.ndarray.linalg.slogdet(A=None, out=None, name=None, **kwargs)

Compute the sign and log of the determinant of a matrix. Input is a tensor A of dimension n >= 2.

If n=2, A is a square matrix. We compute:

sign = sign(det(A)) logabsdet = log(abs(det(A)))

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

Note

The operator supports float32 and float64 data types only.

Note

The gradient is not properly defined on sign, so the gradient of it is not backwarded.

Note

No gradient is backwarded when A is non-invertible. Please see the docs of operator det for detail.

Examples:

Single matrix signed log determinant
A = [[2., 3.], [1., 4.]]
sign, logabsdet = slogdet(A)
sign = [1.]
logabsdet = [1.609438]

Batch matrix signed log determinant
A = [[[2., 3.], [1., 4.]],
[[1., 2.], [2., 4.]],
[[1., 2.], [4., 3.]]]
sign, logabsdet = slogdet(A)
sign = [1., 0., -1.]
logabsdet = [1.609438, -inf, 1.609438]


Defined in src/operator/tensor/la_op.cc:L1031

Parameters
• A (NDArray) – Tensor of square matrix

• out (NDArray, optional) – The output NDArray to hold the result.

Returns

out – The output of this function.

Return type

NDArray or list of NDArrays

mxnet.ndarray.linalg.sumlogdiag(A=None, out=None, name=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:L445

Parameters
• A (NDArray) – Tensor of square matrices

• out (NDArray, optional) – The output NDArray to hold the result.

Returns

out – The output of this function.

Return type

NDArray or list of NDArrays

mxnet.ndarray.linalg.syevd(A=None, out=None, name=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 * UT = UT * 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:L868

Parameters
• A (NDArray) – Tensor of input matrices to be factorized

• out (NDArray, optional) – The output NDArray to hold the result.

Returns

out – The output of this function.

Return type

NDArray or list of NDArrays

mxnet.ndarray.linalg.syrk(A=None, transpose=_Null, alpha=_Null, out=None, name=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 * AT

if transpose=False, or

out = alpha * AT * 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:L730

Parameters
• A (NDArray) – 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.

• out (NDArray, optional) – The output NDArray to hold the result.

Returns

out – The output of this function.

Return type

NDArray or list of NDArrays

mxnet.ndarray.linalg.trmm(A=None, B=None, transpose=_Null, rightside=_Null, lower=_Null, alpha=_Null, out=None, name=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 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:L333

Parameters
• A (NDArray) – Tensor of lower triangular matrices

• B (NDArray) – 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.

• lower (boolean, optional, default=1) – True if the triangular matrix is lower triangular, false if it is upper triangular.

• alpha (double, optional, default=1) – Scalar factor to be applied to the result.

• out (NDArray, optional) – The output NDArray to hold the result.

Returns

out – The output of this function.

Return type

NDArray or list of NDArrays

mxnet.ndarray.linalg.trsm(A=None, B=None, transpose=_Null, rightside=_Null, lower=_Null, alpha=_Null, out=None, name=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 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:L396

Parameters
• A (NDArray) – Tensor of lower triangular matrices

• B (NDArray) – 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.

• lower (boolean, optional, default=1) – True if the triangular matrix is lower triangular, false if it is upper triangular.

• alpha (double, optional, default=1) – Scalar factor to be applied to the result.

• out (NDArray, optional) – The output NDArray to hold the result.

Returns

out – The output of this function.

Return type

NDArray or list of NDArrays