Merge pull request #357 from mrava87/npsolve_v1

feature: add option to choose dense solver to div

pylops/LinearOperator.py

@@ -4,7 +4,9 @@
 
 import numpy as np
 import scipy as sp
-from scipy.linalg import eigvals, lstsq, solve
+from numpy.linalg import solve as np_solve
+from scipy.linalg import eigvals, lstsq
+from scipy.linalg import solve as sp_solve
 from scipy.sparse import csr_matrix
 from scipy.sparse.linalg import LinearOperator as spLinearOperator
 from scipy.sparse.linalg import eigs as sp_eigs
@@ -19,6 +21,7 @@
     from scipy.sparse.linalg.interface import _ProductLinearOperator
 else:
     from scipy.sparse.linalg._interface import _ProductLinearOperator
+
 from pylops.optimization.solver import cgls
 from pylops.utils.backend import get_array_module, get_module, get_sparse_eye
 from pylops.utils.estimators import trace_hutchinson, trace_hutchpp, trace_nahutchpp
@@ -260,7 +263,7 @@ def dot(self, x):
             else:
                 raise ValueError("expected 1-d or 2-d array or matrix, got %r" % x)
 
-    def div(self, y, niter=100):
+    def div(self, y, niter=100, densesolver="scipy"):
         r"""Solve the linear problem :math:`\mathbf{y}=\mathbf{A}\mathbf{x}`.
 
         Overloading of operator ``/`` to improve expressivity of `Pylops`
@@ -272,17 +275,19 @@ def div(self, y, niter=100):
             Data
         niter : :obj:`int`, optional
             Number of iterations (to be used only when ``explicit=False``)
+        densesolver : :obj:`str`, optional
+            Use scipy (``scipy``) or numpy (``numpy``) dense solver
 
         Returns
         -------
         xest : :obj:`np.ndarray`
             Estimated model
 
         """
-        xest = self.__truediv__(y, niter=niter)
+        xest = self.__truediv__(y, niter=niter, densesolver=densesolver)
         return xest
 
-    def __truediv__(self, y, niter=100):
+    def __truediv__(self, y, niter=100, densesolver="scipy"):
         if self.explicit is True:
             if sp.sparse.issparse(self.A):
                 # use scipy solver for sparse matrices
@@ -291,7 +296,10 @@ def __truediv__(self, y, niter=100):
                 # use scipy solvers for dense matrices (used for backward
                 # compatibility, could be switched to numpy equivalents)
                 if self.A.shape[0] == self.A.shape[1]:
-                    xest = solve(self.A, y)
+                    if densesolver == "scipy":
+                        xest = sp_solve(self.A, y)
+                    else:
+                        xest = np_solve(self.A, y)
                 else:
                     xest = lstsq(self.A, y)[0]
             else: