Merge pull request scipy#5910 from pv/gcrotmk

ENH: sparse.linalg: add GCROT(m,k)

benchmarks/benchmarks/sparse_linalg_solve.py

@@ -8,7 +8,7 @@
 
 try:
     from scipy import linalg, sparse
-    from scipy.sparse.linalg import cg, minres, spsolve
+    from scipy.sparse.linalg import cg, minres, gmres, spsolve
 except ImportError:
     pass
 
@@ -17,6 +17,11 @@
 except ImportError:
     pass
 
+try:
+    from scipy.sparse.linalg import gcrotmk
+except ImportError:
+    pass
+
 from .common import Benchmark
 
 
@@ -38,7 +43,7 @@ def _create_sparse_poisson2d(n):
 class Bench(Benchmark):
     params = [
         [4, 6, 10, 16, 25, 40, 64, 100],
-        ['dense', 'spsolve', 'cg', 'minres', 'lgmres']
+        ['dense', 'spsolve', 'cg', 'minres', 'gmres', 'lgmres', 'gcrotmk']
     ]
     param_names = ['(n,n)', 'solver']
 
@@ -59,8 +64,12 @@ def time_solve(self, n, solver):
             cg(self.P_sparse, self.b)
         elif solver == 'minres':
             minres(self.P_sparse, self.b)
+        elif solver == 'gmres':
+            gmres(self.P_sparse, self.b)
         elif solver == 'lgmres':
             lgmres(self.P_sparse, self.b)
+        elif solver == 'gcrotmk':
+            gcrotmk(self.P_sparse, self.b)
         elif solver == 'spsolve':
             spsolve(self.P_sparse, self.b)
         else:

scipy/optimize/nonlin.py

@@ -1422,6 +1422,7 @@ def __init__(self, rdiff=None, method='lgmres', inner_maxiter=20,
             self.method_kw['maxiter'] = 1
             # Carry LGMRES's `outer_v` vectors across nonlinear iterations
             self.method_kw.setdefault('outer_v', [])
+            self.method_kw.setdefault('prepend_outer_v', True)
             # But don't carry the corresponding Jacobian*v products, in case
             # the Jacobian changes a lot in the nonlinear step
             #

scipy/sparse/linalg/__init__.py

@@ -60,6 +60,7 @@
    lgmres -- Solve a matrix equation using the LGMRES algorithm
    minres -- Use MINimum RESidual iteration to solve Ax = b
    qmr -- Use Quasi-Minimal Residual iteration to solve A x = b
+   gcrotmk -- Solve a matrix equation using the GCROT(m,k) algorithm
 
 Iterative methods for least-squares problems:
 

scipy/sparse/linalg/isolve/__init__.py

@@ -8,6 +8,7 @@
 from .lgmres import lgmres
 from .lsqr import lsqr
 from .lsmr import lsmr
+from ._gcrotmk import gcrotmk
 
 __all__ = [s for s in dir() if not s.startswith('_')]