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# torch.ormqr

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 m×n m \times n m×n 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.

  If `left` is True then op(Q) times C is computed, otherwise the result is C times op(Q). When `left` is True, the implicit matrix Q has size m×m m \times m m×m. It has size n×n n \times n n×n otherwise. If `transpose` is 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.

  See also

  `torch.geqrf()` can be used to form the Householder representation (input, tau) of matrix Q from the QR decomposition.

  Note

  This function supports backward but it is only fast when `(input, tau)` do not require gradients and/or `tau.size(-1)` is very small. ``

  Parameters:

    - input (Tensor) – tensor of shape (*, mn, k) where * is zero or more batch dimensions and mn equals to m or n depending on the `left`.

    - 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.

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