Comments, and reverting funky use of l_norm in exponential. (checked …

…with SimPleAC_table from robustSPpaper).

robust/robust.py

@@ -55,6 +55,10 @@ def set(self, option_name, value):
 
 
 class RobustModel:
+    """
+    RobustModel extends gpkit.Model through the robust counterpart.
+    It uses the nominal solution of the GP or SP to
+    """
     def __init__(self, model, type_of_uncertainty_set, **options):
         self.nominal_model = model
         self.substitutions = model.substitutions
@@ -113,9 +117,8 @@ def __init__(self, model, type_of_uncertainty_set, **options):
                 else:
                     p = cs.as_posyslt1()[0]
                     if len(p.exps) == 1:
-                        robust_monomial, l_norm = self.robustify_monomial(p)
-                        a = Variable()
-                        self.ready_gp_constraints += [robust_monomial <= a**(1/l_norm), a <= 1]
+                        robust_monomial = self.robustify_monomial(p)
+                        self.ready_gp_constraints += [robust_monomial <= 1]
                     else:
                         self.to_linearize_gp_posynomials += [p]
             del safe_model_constraints
@@ -262,9 +265,8 @@ def classify_gp_constraints(self, gp_posynomials, offset=0):
         large_gp_posynomials = []
         for i, p in enumerate(data_gp_posynomials):
             if len(p.exps) == 1:
-                robust_monomial, l_norm = self.robustify_monomial(p)
-                a = Variable()
-                ready_gp_constraints += [robust_monomial <= a**(1/l_norm), a <= 1]
+                robust_monomial = self.robustify_monomial(p)
+                ready_gp_constraints += [robust_monomial <= 1]
             elif len(p.exps) == 2 and self.setting.get('linearizeTwoTerm'):
                 to_linearize_gp_posynomials += [p]
             else:
@@ -303,16 +305,15 @@ def robustify_monomial(self, monomial):
             l_norm = np.sqrt(l_norm)
         g = self.setting.get('gamma')
         # Fifth order Taylor approx of the e**gamma, so that gamma can be a variable
-        robust_monomial =  monomial**(1/l_norm) * (1.+g+1./2.*g**2+1./6.*g**3+1./24.*g**4+1./120.*g**5)
-        return robust_monomial, l_norm
+        robust_monomial =  monomial * (1.+g+1./2.*g**2+1./6.*g**3+1./24.*g**4+1./120.*g**5)**l_norm
+        return robust_monomial
 
     def robustify_set_of_monomials(self, set_of_monomials, feasible=False):
         robust_set_of_monomial_constraints = []
         slackvar = Variable()
         for monomial in set_of_monomials:
-            robust_monomial, l_norm = self.robustify_monomial(monomial)
-            a = Variable()
-            robust_set_of_monomial_constraints += [robust_monomial <= a**(1/l_norm), a <= slackvar ** feasible]
+            robust_monomial = self.robustify_monomial(monomial)
+            robust_set_of_monomial_constraints += [robust_monomial <= slackvar ** feasible]
         robust_set_of_monomial_constraints += [slackvar >= 1, slackvar <= 1000]
         return robust_set_of_monomial_constraints, slackvar
 
@@ -342,15 +343,15 @@ def calculate_value_of_two_term_approximated_posynomial(self, two_term_approxima
             subs_monomials = []
             for j in xrange(len(monomials)):
                 # st3 = time()
-                robust_monomial, l_norm = self.robustify_monomial(monomials[j])
+                robust_monomial = self.robustify_monomial(monomials[j])
                 monomials[j] = robust_monomial.sub(solution['variables'])
                 # print "subs for a monomial is taking too much time", time()-st3
                 subs_monomials.append(monomials[j].cs[0])
             values.append(max(subs_monomials))
         if number_of_two_terms % 2 != 0:
             monomial = Monomial(two_term_approximation.p.exps[permutation[len(permutation) - 1]],
                                     two_term_approximation.p.cs[permutation[len(permutation) - 1]])
-            robust_monomial, l_norm = self.robustify_monomial(monomial)
+            robust_monomial = self.robustify_monomial(monomial)
             monomial = robust_monomial.sub(solution['variables'])
             values.append(monomial.cs[0])
         return sum(values)