pylops_gpu.MatrixMult¶

class pylops_gpu.MatrixMult(A, dims=None, device='cpu', togpu=(False, False), tocpu=(False, False), dtype=torch.float32)[source]¶

Matrix multiplication.

Simple wrapper to torch.matmul for an input matrix \(\mathbf{A}\).

Parameters:

A : torch.Tensor or pytorch_complex_tensor.ComplexTensor or numpy.ndarray: Matrix.
dims : tuple, optional: Number of samples for each other dimension of model (model/data will be reshaped and A applied multiple times to each column of the model/data).
device : str, optional: Device to be used
togpu : tuple, optional: Move model and data from cpu to gpu prior to applying matvec and rmatvec, respectively (only when device='gpu')
tocpu : tuple, optional: Move data and model from gpu to cpu after applying matvec and rmatvec, respectively (only when device='gpu')
dtype : torch.dtype or np.dtype, optional: Type of elements in input array.

Notes

Refer to pylops.basicoperators.MatrixMult for implementation details.

Attributes:	shape : `tuple` Operator shape explicit : `bool` Operator contains a matrix that can be solved explicitly (`True`) or not (`False`)

Methods

`__init__`(A[, dims, device, togpu, tocpu, dtype])	Initialize this LinearOperator.
`adjoint`()	Hermitian adjoint.
`apply_columns`(cols)	Apply subset of columns of operator
`cond`([uselobpcg])	Condition number of linear operator.
`conj`()	Complex conjugate operator
`div`(y[, niter, tol])	Solve the linear problem \(\mathbf{y}=\mathbf{A}\mathbf{x}\).
`dot`(x)	Matrix-vector multiplication.
`eigs`([neigs, symmetric, niter, uselobpcg])	Most significant eigenvalues of linear operator.
`inv`()	Return the inverse of \(\mathbf{A}\).
`matmat`(X[, kfirst])	Matrix-matrix multiplication.
`matvec`(x)	Matrix-vector multiplication.
`rmatmat`(X[, kfirst])	Adjoint matrix-matrix multiplication.
`rmatvec`(x)	Adjoint matrix-vector multiplication.
`todense`([backend])	Return dense matrix.
`toimag`([forw, adj])	Imag operator
`toreal`([forw, adj])	Real operator
`tosparse`()	Return sparse matrix.
`transpose`()	Transpose this linear operator.