mindspore.ops.Ormqr

class mindspore.ops.Ormqr(left=True, transpose=False)[source]

Computes the matrix-matrix multiplication of a product of Householder matrices with a general matrix. Multiplies a(m, n) matrix C (given by other) with a matrix Q, where Q is represented using Householder reflectors (x, tau), which is the output of geqrf().

Refer to mindspore.ops.ormqr() for more details.

Warning

This is an experimental API that is subject to change or deletion.

Parameters

  • left (bool, optional) – controls the order of multiplication. If true, compute op(Q)*C. If false, compute C*op(Q). Default: True.

  • transpose (bool, optional) – controls whether the matrix Q is conjugate transposed or not.Default: False.

Inputs:

  • x (Tensor) - Tensor of shape \((*, mn, k)\) where the value of mn depending on left, When left is True, the value of mn is equal to m; otherwise, the value of mn is equal to n. and * is zero or more batch dimensions.

  • tau (Tensor) - Tensor of shape \((*, min(mn, k))\) where * is zero or more batch dimensions, and its type is the same as x.

  • other (Tensor) - Tensor of shape \((*, m, n)\) where * is zero or more batch dimensions, and its type is the same as x.

Outputs:

  • y (Tensor) - the output Tensor, has the same shape and data type as other.

Raises

  • TypeError – If x or tau or other is not Tensor.

  • TypeError – If dtype of x or tau or other is not one of: float64, float32, complex64, complex128.

  • ValueError – If x or other is less than 2D.

  • ValueError – If rank(x) - rank(tau) != 1.

  • ValueError – If tau.shape[:-2] != x.shape[:-2]

  • ValueError – If other.shape[:-2] != x.shape[:-2]

  • ValueError – If left == true, other.shape[-2] < tau.shape[-1].

  • ValueError – If left == true, other.shape[-2] != x.shape[-2].

  • ValueError – If left == false, other.shape[-1] < tau.shape[-1].

  • ValueError – If left == false, other.shape[-1] != x.shape[-2].

Supported Platforms:

GPU

Examples

>>> x = Tensor(np.array([[-114.6, 10.9, 1.1], [-0.304, 38.07, 69.38], [-0.45, -0.17, 62]]), mindspore.float32)
>>> tau = Tensor(np.array([1.55, 1.94, 3.0]), mindspore.float32)
>>> other = Tensor(np.array([[-114.6, 10.9, 1.1],
                             [-0.304, 38.07, 69.38],
                             [-0.45, -0.17, 62]]), mindspore.float32)
>>> net = ops.Ormqr()
>>> y = net(x, tau, other)
>>> print(y)
[[  63.82713   -13.823125 -116.28614 ]
 [ -53.659264  -28.157839  -70.42702 ]
 [ -79.54292    24.00183   -41.34253 ]]