add subset param

flexsolve/iterative_solvers.py

@@ -19,15 +19,15 @@
 
 @register_jitable(cache=True)
 def fixed_point(f, x, xtol=5e-8, args=(), maxiter=50, checkiter=True,
-                checkconvergence=True, convergenceiter=0):
+                checkconvergence=True, convergenceiter=0, subset=0):
     """Iterative fixed-point solver."""
     x0 = x1 = x
     errors = np.zeros(convergenceiter)
     fixedpoint_converged = utils.fixedpoint_converged
     for iter in range(maxiter):
         x1 = f(x0, *args)
         e = np.abs(x1 - x0)
-        if fixedpoint_converged(e, xtol): return x1
+        if fixedpoint_converged(e, xtol, subset): return x1
         if convergenceiter:
             mean = utils.mean(e)
             if iter > convergenceiter and mean > errors.mean():
@@ -51,7 +51,7 @@ def conditional_fixed_point(f, x):
 
 @register_jitable(cache=True)
 def wegstein(f, x, xtol=5e-8, args=(), maxiter=50, checkiter=True,
-             checkconvergence=True, convergenceiter=0):
+             checkconvergence=True, convergenceiter=0, subset=0):
     """Iterative Wegstein solver."""
     errors = np.zeros(convergenceiter)
     x0 = x
@@ -65,7 +65,7 @@ def wegstein(f, x, xtol=5e-8, args=(), maxiter=50, checkiter=True,
             x1 = g0
             g1 = f(x1, *args)
         e = np.abs(g1 - x1)
-        if fixedpoint_converged(e, xtol): return g1
+        if fixedpoint_converged(e, xtol, subset): return g1
         x0 = x1
         x1 = wegstein_iter(x1, dx, g1, g0)
         g0 = g1
@@ -100,7 +100,7 @@ def conditional_wegstein(f, x):
 
 @register_jitable(cache=True)
 def aitken(f, x, xtol=5e-8, args=(), maxiter=50, checkiter=True,
-           checkconvergence=True, convergenceiter=0):
+           checkconvergence=True, convergenceiter=0, subset=0):
     """Iterative Aitken solver."""
     gg = x
     errors = np.zeros(convergenceiter)
@@ -113,10 +113,10 @@ def aitken(f, x, xtol=5e-8, args=(), maxiter=50, checkiter=True,
             g = f(x, *args)
         dxg = x - g
         e = np.abs(dxg)
-        if fixedpoint_converged(e, xtol): return g
+        if fixedpoint_converged(e, xtol, subset): return g
         gg = f(g, *args)
         dgg_g = gg - g
-        if fixedpoint_converged(np.abs(dgg_g), xtol): return gg
+        if fixedpoint_converged(np.abs(dgg_g), xtol, subset): return gg
         x = aitken_iter(x, gg, dxg, dgg_g)
         if convergenceiter:
             mean = utils.mean(e)
@@ -143,4 +143,4 @@ def conditional_aitken(f, x):
         if not condition: return g
         gg, condition = f(g)
         x = aitken_iter(x, gg, x - g, gg - g)
-    return x
+    return x