add the convergence criterion, the method is :

1. calculate the L2 norm of the residual of all the stress points
2. store the relative errors of the L2 norm
3. if the standard deviation of the relative errors of last five fittings is less than 0.05, that it is considered that the relative errors is stabilized, so the fitting is finished.

processing/misc/yieldSurface.py

@@ -1098,7 +1098,7 @@ class Criterion(object):
       print('fitting to the %s criterion'%name)
 
   def fit(self,stress):
-    global fitResults; fitErrors
+    global fitResults; fitErrors; fitResidual
     if options.exponent > 0.0: nExponent = nExpo
     else:                      nExponent = 0
     nameCriterion = self.name.lower()
@@ -1120,6 +1120,9 @@ class Criterion(object):
                        bounds=bounds, Dfun=criteria.jac,   full_output=True)
       if ierr not in [1, 2, 3, 4]:
         raise RuntimeError("Optimal parameters not found: " + errmsg)
+      else:
+        residual = criteria.fun(popt, ydata, stress)
+        fitResidual.append(np.linalg.norm(residual)/len(residual))
       if (len(ydata) > len(initialguess)) and pcov is not None:
         s_sq = (criteria.fun(popt, *(ydata,stress))**2).sum()/(len(ydata)-len(initialguess))
         pcov = pcov * s_sq
@@ -1257,8 +1260,12 @@ def loadcaseNo():
   return N_simulations
 
 def converged():        # is there any meaningfull stopping criterion?
-  global N_simulations
+  global N_simulations; fitResidual
+
   if N_simulations < options.max:
+    if len(fitResidual) > 5:
+      residualList = np.array(fitResidual[len(fitResidual)-5:])
+      if np.std(residualList)/np.max(residualList) < 0.05: return True
     return False
   else:
     return True
@@ -1364,7 +1371,7 @@ myLoad = Loadcase(options.load[0],options.load[1],options.load[2],
 stressAll= [np.zeros(0,'d').reshape(0,0) for i in xrange(int(options.yieldValue[2]))]
 strainAll= [np.zeros(0,'d').reshape(0,0) for i in xrange(int(options.yieldValue[2]))]
 
-fitResults = []; fitErrors = []; threads=[]
+fitResults = []; fitErrors = []; fitResidual = []; threads=[]
 myFit = Criterion(options.exponent,options.eqStress, options.dimension, options.criterion)
 for i in range(options.threads):
   threads.append(myThread(i))