Changeset 373 for branches/alta/mystic-0.2a1/test_constraints_range.py

Timestamp:

08/04/10 16:07:50 (6 years ago)

Author:

altafang

Message:

Fixing minor things, adjusting termination conditions on tests

File:

• branches/alta/mystic-0.2a1/test_constraints_range.py (modified) (9 diffs)

branches/alta/mystic-0.2a1/test_constraints_range.py

@@ -9 +9 @@
 
 Mathematica: x -> -0.61479, y -> 0.38521
+
+OpenOpt Code and Results:
+-------------------------
+from openopt import NLP, GLP
+from math import *
+from mystic.models import rosen as f
+def c(x):
+    return -(max(x) - min(x) - 1.)
+#x0 = [0., 1.75]
+x0 = [0., 0.]
+
+problem = GLP(f, x0, c=c, lb=[-1., -1.], ub=[3., 3.], population=40, 
+baseVectorStrategy='random', searchDirectionStrategy='random')
+soln = problem.solve('de') 
+
+#problem = NLP(f, x0, c=c)
+#soln = problem.solve('ralg') #[-0.61778501  0.38221429]
+#soln = problem.solve('scipy_slsqp') #[-0.61478988  0.38521012] also, very fast!
+print soln.xf
+
+slsqp is great with a feasible x0, but fails with infeasible. ralg also does
+poorly with infeasible x0. de does ok with any x0, as long as lb and ub is
+narrow enough.
 """
 
@@ -36 +59 @@
 #x0 = [0., 0.] # Another infeasible starting point.
 #x0 = [0., -0.5] # Difficult. Considerably closer to local min than global min
+#x0 = [1.5, -0.5] # Even more difficult. Feasible, but in a disjoint feasible 
+                 # region from the global min.
 
 constraints_string = """
@@ -57 +82 @@
     from mystic.termination import VTR
     from mystic.termination import CandidateRelativeTolerance as CRT
+    from mystic.termination import ChangeOverGeneration as COG
     from mystic.strategy import Rand1Bin
     solver = DifferentialEvolutionSolver(ndim, npop)
@@ -64 +90 @@
     term = VTR()
     #term = CRT()
-    solver.Solve(costfunc, term, constraints=constraints_string, \
-                constraints_method='penalty')
+    #term = COG()
+    solver.Solve(costfunc, term, constraints=constraints_string, \
+                constraints_method='penalty', penalty=1e6, strategy=Rand1Bin)
     soln = solver.Solution()
 
@@ -282 +309 @@
     time_before = time.time()
 
-    from differential_evolution import DifferentialEvolutionSolver
+    from mystic.differential_evolution import DifferentialEvolutionSolver
     from mystic.termination import VTR
     from mystic.termination import CandidateRelativeTolerance as CRT
@@ -293 +320 @@
     #term = CRT()
     solver.Solve(costfunc, term, constraints=constraints_string, \
-                 constraints_method='auglag', rh=1e3, rg=1e3)
+                 constraints_method='auglag', strategy=Rand1Bin, rh=1e3, rg=1e3)
     soln = solver.Solution()
 
@@ -315 +342 @@
     time_before = time.time()
 
-    from differential_evolution import DifferentialEvolutionSolver2
-    from mystic.termination import VTR
+    from mystic.differential_evolution import DifferentialEvolutionSolver2
+    from mystic.termination import VTR
+    from mystic.termination import CandidateRelativeTolerance as CRT
     from mystic.strategy import Rand1Bin
     solver = DifferentialEvolutionSolver2(ndim, npop)
-      
-    solver.SetInitialPoints(x0)
-    solver.enable_signal_handler()
-    term = VTR()
-    solver.Solve(costfunc, term, constraints=constraints_string, \
-                constraints_method='auglag', rg=1e3, rh=1e3)#, strategy=Rand1Bin)
-    soln = solver.Solution()
-
-    time_elapsed = time.time() - time_before
-
-    print "soln=", soln
-    print "f value =", costfunc(soln)
-    print "Time elapsed=", time_elapsed
-
-    from constraint_tools import verify_constraints_satisfied
-    print verify_constraints_satisfied(constraints_string, soln, disp=False)
+    solver.SetInitialPoints(x0)
+    solver.enable_signal_handler()
+    term = VTR()
+    #term = CRT()
+    solver.Solve(costfunc, term, constraints=constraints_string, \
+                constraints_method='auglag', strategy=Rand1Bin, rg=1e3, rh=1e3)
+    soln = solver.Solution()
+
+    time_elapsed = time.time() - time_before
+
+    print "soln=", soln
+    print "f value =", costfunc(soln)
+    print "Time elapsed=", time_elapsed
+
+    from constraint_tools import verify_constraints_satisfied
+    print verify_constraints_satisfied(constraints_string, soln)#, disp=False)
 
 def test_neldermead_auglag():
@@ -714 +742 @@
     # with x0=[0., 0.], BFGS, NCG give non-minimum answers with penalty.
     # with x0=[-0.5, -0.5] and penalty method, local optimizers can only find
-    # local minima.
+    # local minima. With [0., -0.5], all penalty method are stuck in local min.
     test_diffev_penalty()
     test_diffev2_penalty()
@@ -726 +754 @@
     # infeasible region and end up at a local minimum. See
     # http://ugcs.caltech.edu/~altafang/constrained_rosenbrock.html
+    # With [0., -0.5], all but diffev, diffev2, and bfgs get stuck in local min.
+    # With [1.5, -0.5], diffev works, and diffev2 works only with high 
+    # CrossProbability and ScalingFactor.
     test_diffev_auglag() 
-    test_diffev2_auglag() # strange bug: with x0=[-0.5, -0.5],
+    test_diffev2_auglag() # strange bug: with x0=[-0.5, -0.5] and [0., -0.5],
                           # get different answer depending on 
                           # whether previous line is commented out or not.
+                         # The effect also exists if you switch their orders.
     test_neldermead_auglag()
     test_powelldirectional_auglag() # needs rg and rh = 1e4 (for feasible x0)