- torch.ormqr(input, tau, other, left=True, transpose=False, *, out=None) Tensor ¶
Computes the matrix-matrix multiplication of a product of Householder matrices with a general matrix.
Multiplies a matrix C (given by
other) with a matrix Q, where Q is represented using Householder reflectors (input, tau). See Representation of Orthogonal or Unitary Matrices for further details.
leftis True then op(Q) times C is computed, otherwise the result is C times op(Q). When
leftis True, the implicit matrix Q has size . It has size otherwise. If
transposeis True then op is the conjugate transpose operation, otherwise it’s a no-op.
Supports inputs of float, double, cfloat and cdouble dtypes. Also supports batched inputs, and, if the input is batched, the output is batched with the same dimensions.
torch.geqrf()can be used to form the Householder representation (input, tau) of matrix Q from the QR decomposition.
This function supports backward but it is only fast when
(input, tau)do not require gradients and/or
tau.size(-1)is very small. ``
input (Tensor) – tensor of shape (*, mn, k) where * is zero or more batch dimensions and mn equals to m or n depending on the
tau (Tensor) – tensor of shape (*, min(mn, k)) where * is zero or more batch dimensions.
other (Tensor) – tensor of shape (*, m, n) where * is zero or more batch dimensions.
left (bool) – controls the order of multiplication.
transpose (bool) – controls whether the matrix Q is conjugate transposed or not.
- Keyword Arguments:
out (Tensor, optional) – the output Tensor. Ignored if None. Default: None.