some cleaning

Team25/shape.py

@@ -3,8 +3,8 @@
 #
 
 import numpy as np
-import conveks
 import onelab
+import conveks
 
 c = onelab.client(__file__)
 
@@ -106,13 +106,6 @@ conveks.mma.initialize(initialPoint, lowerBound, upperBound)
 
 # Set some options for MMA
 conveks.mma.option.setNumber('General.Verbosity', 4)
-conveks.mma.option.setNumber('General.SaveHistory', 0)
-conveks.mma.option.setNumber('SubProblem.isRobustMoveLimit', 1)
-conveks.mma.option.setNumber('SubProblem.isRobustAsymptotes', 1)
-conveks.mma.option.setNumber('SubProblem.type', 0)
-conveks.mma.option.setNumber('SubProblem.addConvexity', 0)
-conveks.mma.option.setNumber('SubProblem.asymptotesRmax', 100.0)
-conveks.mma.option.setNumber('SubProblem.asymptotesRmin', 0.001)
 
 # Get iteration count (here it will be 1 - could be different in case of restart)
 it = conveks.mma.getOuterIteration()
@@ -121,10 +114,10 @@ change = 1.0
 while it <= maxIter and c.getString('shape/Action') != 'stop':
 
     c.setNumber('Optimization/01Current iteration', value=it, readOnly=1,
-                attributes={'Highlight':'LightYellow'})
+        attributes={'Highlight':'LightYellow'})
 
     # get (copy of) current point
-    xFromMMA = np.array(conveks.mma.getCurrentPoint())
+    xFromMMA = conveks.mma.getCurrentPoint()
 
     # send the current point to GetDP model
     for label, var in x.iteritems():
@@ -137,30 +130,23 @@ while it <= maxIter and c.getString('shape/Action') != 'stop':
     # mesh the geometry
     c.runSubClient('myGmsh', mygmsh + ' -2')
 
-    # generate the velocity field of each design variable
-    getVelocityField(x)
-
     # compute objective function and constraints
     c.runSubClient('myGetDP', mygetdp + '-solve GetPerformances')
+    objective = np.sum(readSimpleTable(c.getPath('res_opt/w.txt')))
+    constraints = np.array([np.sum(xFromMMA)/np.sum(upperBound)-1.0])
 
-    # as well as their sensitivity with respect to design variables at `x'
+    # generate the velocity field of each design variable
+    getVelocityField(x)
+
+    # compute the sensitivity with respect to design variables at `x'
     for dv, var in x.iteritems():
         c.runNonBlockingSubClient('myGetDP', mygetdp \
           + ' -setnumber VelocityTag '+str(dv)\
           + ' -solve GetGradient_wrt_dv')
     c.waitOnSubClients()
-
-    # get the value of the objective function and of the constraints
-    # as well as their sensitivity with respect to design variables at `x'
-    objective = np.sum(readSimpleTable(c.getPath('res_opt/w.txt')))
-    constraints = np.array([np.sum(xFromMMA)/100.0-1.0])
     grad_objective = np.asarray([np.sum(readSimpleTable(c.getPath('res_opt/Grad_w_wrt_dv_'+str(dv)+'.txt')))\
         for dv in xrange(numVariables)])
-    grad_constraints = np.ones(numVariables)/100.0
-
-    if it == 1: fscale = 1.0 / objective
-    objective *= fscale
-    grad_objective *= fscale
+    grad_constraints = np.ones(numVariables)/np.sum(upperBound)
 
     print('*'*50)
     print('iteration: ', it)
@@ -171,7 +157,7 @@ while it <= maxIter and c.getString('shape/Action') != 'stop':
     c.sendInfo('Optimization: it. {}, obj. {}, constr. {}'.format(it,objective,constraints[0]))
 
     # call MMA and update the current point
-    conveks.mma.setMoveLimits(lowerBound, upperBound, 1.0e-4)
+    conveks.mma.setMoveLimits(lowerBound, upperBound, 1.0e-3)
     conveks.mma.updateCurrentPoint(constraints,grad_objective,grad_constraints)
     change = conveks.mma.getDesignChange()
     it = conveks.mma.getOuterIteration()