householder_product(A, tau, *, out=None) → Tensor¶
Computes the first n columns of a product of Householder matrices.
Letting be or , for a matrix with columns with and a vector with , this function computes the first columns of the matrix
where is the m-dimensional identity matrix and is the conjugate transpose when is complex, and the transpose when is real-valued.
See Representation of Orthogonal or Unitary Matrices for further details.
Supports inputs of float, double, cfloat and cdouble dtypes. Also supports batches of matrices, and if the inputs are batches of matrices then the output has the same batch dimensions.
This function only uses the values strictly below the main diagonal of
A. The other values are ignored.
torch.ormqr()is a related function that computes the matrix multiplication of a product of Householder matrices with another matrix. However, that function is not supported by autograd.
- Keyword Arguments
out (Tensor, optional) – output tensor. Ignored if None. Default: None.
RuntimeError – if
Adoesn’t satisfy the requirement m >= n, or
taudoesn’t satisfy the requirement n >= k.
>>> A = torch.randn(2, 2) >>> h, tau = torch.geqrf(A) >>> Q = torch.linalg.householder_product(h, tau) >>> torch.dist(Q, torch.linalg.qr(A).Q) tensor(0.) >>> h = torch.randn(3, 2, 2, dtype=torch.complex128) >>> tau = torch.randn(3, 1, dtype=torch.complex128) >>> Q = torch.linalg.householder_product(h, tau) >>> Q tensor([[[ 1.8034+0.4184j, 0.2588-1.0174j], [-0.6853+0.7953j, 2.0790+0.5620j]], [[ 1.4581+1.6989j, -1.5360+0.1193j], [ 1.3877-0.6691j, 1.3512+1.3024j]], [[ 1.4766+0.5783j, 0.0361+0.6587j], [ 0.6396+0.1612j, 1.3693+0.4481j]]], dtype=torch.complex128)