LinearOpsTensorAlgebra
Common linear algebra operations. Operates on Tensor.
Parameters
the type of items closed under division in the tensors.
Inheritors
Functions
Cholesky decomposition.
Computes the determinant of a square matrix input, or of each square matrix in a batched input. For more information: https://pytorch.org/docs/stable/linalg.html#torch.linalg.det
Creates a tensor whose diagonals of certain 2D planes (specified by dim1 and dim2) are filled by diagonalEntries. To facilitate creating batched diagonal matrices, the 2D planes formed by the last two dimensions of the returned tensor are chosen by default.
Matrix product of two tensors.
Computes the multiplicative inverse matrix of a square matrix input, or of each square matrix in a batched input. Given a square matrix A
, return the matrix AInv
satisfying A dot AInv == AInv dot A == eye(a.shape[0])
. For more information: https://pytorch.org/docs/stable/linalg.html#torch.linalg.inv
LUP decomposition
Each element of the tensor arg is subtracted from this value. The resulting tensor is returned.
Subtracts the scalar arg from each element of this tensor and returns a new resulting tensor.
Each element of the tensor arg is subtracted from each element of this tensor. The resulting tensor is returned.
QR decomposition.
Singular Value Decomposition.
Returns eigenvalues and eigenvectors of a real symmetric matrix input
or a batch of real symmetric matrices, represented by a pair eigenvalues to eigenvectors
. For more information: https://pytorch.org/docs/stable/generated/torch.symeig.html
Numerical negative, element-wise.
Returns a single tensor value of unit dimension. The tensor shape must be equal to 1.
Returns a single tensor value of unit dimension if tensor shape equals to 1.
Returns a new tensor with the same data as the self tensor but of a different shape. The returned tensor shares the same data and must have the same number of elements, but may have a different size For more information: https://pytorch.org/docs/stable/tensor_view.html