# pylops.MatrixMult¶

class pylops.MatrixMult(A, dims=None, dtype='float64')[source]

Matrix multiplication.

Simple wrapper to numpy.dot and numpy.vdot for an input matrix $$\mathbf{A}$$.

Parameters: A : 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). dtype : str, optional Type of elements in input array. shape : tuple Operator shape explicit : bool Operator contains a matrix that can be solved explicitly (True) or not (False) complex : bool Matrix has complex numbers (True) or not (False)

Methods

 __init__(A[, dims, 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, densesolver]) Solve the linear problem $$\mathbf{y}=\mathbf{A}\mathbf{x}$$. dot(x) Matrix-matrix or matrix-vector multiplication. eigs([neigs, symmetric, niter, uselobpcg]) Most significant eigenvalues of linear operator. inv() Return the inverse of $$\mathbf{A}$$. matmat(X) Matrix-matrix multiplication. matvec(x) Matrix-vector multiplication. rmatmat(X) 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. trace([neval, method, backend]) Trace of linear operator. transpose() Transpose this linear operator.
inv()[source]

Return the inverse of $$\mathbf{A}$$.

Returns: Ainv : numpy.ndarray Inverse matrix.