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Fixing goal programming issues.
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@1ozturkbe
1ozturkbe committed Nov 12, 2019
1 parent 5036ba7 commit f60ce4b
Showing 1 changed file with 26 additions and 18 deletions.
44 changes: 26 additions & 18 deletions robust/robust.py
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Expand Up @@ -118,7 +118,7 @@ def __init__(self, model, type_of_uncertainty_set, **options):
else:
p = cs.as_posyslt1()[0]
if len(p.exps) == 1:
robust_monomial = self.robustify_monomial(p)
robust_monomial, _ = self.robustify_monomial(p)
self.ready_gp_constraints += [robust_monomial <= 1]
else:
self.to_linearize_gp_posynomials += [p]
Expand All @@ -133,6 +133,7 @@ def __init__(self, model, type_of_uncertainty_set, **options):

gp_posynomials = []

# Classifying all constraints
for cs in all_constraints:
if isinstance(cs, SingleSignomialEquality):
self.sp_equality_constraints.append(cs)
Expand Down Expand Up @@ -200,7 +201,13 @@ def setup(self, verbosity=0, **options):
self.robust_solve_properties['numoflinearsections'], new_solution, self._robust_model = self. \
find_number_of_piece_wise_linearization(two_term_data_posynomials, ready_constraints,
feasible=True)
rel_tol = np.abs((new_solution['cost'] - old_solution['cost']) / old_solution['cost'])
try:
rel_tol = np.abs((new_solution['cost'] - old_solution['cost']) / old_solution['cost'])
except:
# Triggers when nominal solution to goal problem is the nominal problem.
if verbosity > 0:
print("Goal program proceeding...")
rel_tol = 1
if verbosity > 0:
if not reached_feasibility:
print("feasibility is not reached yet")
Expand Down Expand Up @@ -265,7 +272,7 @@ 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 = self.robustify_monomial(p)
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]
Expand Down Expand Up @@ -303,20 +310,21 @@ 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.+g+1./2.*g**2+1./6.*g**3+1./24.*g**4+1./120.*g**5)**l_norm
return robust_monomial
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

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 = self.robustify_monomial(monomial)
robust_set_of_monomial_constraints += [robust_monomial <= slackvar ** feasible]
robust_monomial, l_norm = self.robustify_monomial(monomial)
robust_set_of_monomial_constraints += [robust_monomial <= slackvar ** (feasible/l_norm)]
robust_set_of_monomial_constraints += [slackvar >= 1, slackvar <= 1000]
return robust_set_of_monomial_constraints, slackvar

def calculate_value_of_two_term_approximated_posynomial(self, two_term_approximation, index_of_permutation,
solution):
"""... for a given index among all permutations, and a solution to the model. """
permutation = two_term_approximation.list_of_permutations[index_of_permutation]

number_of_two_terms = int(len(permutation) / 2)
Expand All @@ -340,19 +348,20 @@ def calculate_value_of_two_term_approximated_posynomial(self, two_term_approxima
subs_monomials = []
for j in range(len(monomials)):
# st3 = time()
robust_monomial = 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 = mons[len(permutation) - 1]
robust_monomial = self.robustify_monomial(monomial)
robust_monomial, _ = self.robustify_monomial(monomial)
monomial = robust_monomial.sub(solution['variables'])
values.append(monomial.cs[0])
return sum(values)

def find_permutation_with_minimum_value(self, two_term_approximation, solution):
""" ... to be able to find the least conservative two term approximation. """
minimum_value = np.inf
minimum_index = len(two_term_approximation.list_of_permutations)
for i in range(len(two_term_approximation.list_of_permutations)):
Expand Down Expand Up @@ -393,16 +402,15 @@ def linearize_and_return_upper_lower_models(self, two_term_data_posynomials, r,

def find_number_of_piece_wise_linearization(self, two_term_data_posynomials, ready_constraints, feasible=False):

the_min_r = self.setting.get('minNumOfLinearSections')
the_max_r = self.setting.get('maxNumOfLinearSections')
min_r = self.setting.get('minNumOfLinearSections')
max_r = self.setting.get('maxNumOfLinearSections')
r = None
error = None
sol_upper = None

model_upper = None

while the_min_r <= the_max_r:
r = int((the_min_r + the_max_r) / 2.0)
while min_r <= max_r:
r = int((min_r + max_r) / 2.0)
model_upper, model_lower = self. \
linearize_and_return_upper_lower_models(two_term_data_posynomials, r, ready_constraints, feasible)
upper_model_infeasible = 0
Expand All @@ -420,17 +428,17 @@ def find_number_of_piece_wise_linearization(self, two_term_data_posynomials, rea
if upper_model_infeasible != 1:
error = (sol_upper.get('cost') - sol_lower.get('cost')) / sol_lower.get('cost')
if error <= self.setting.get('linearizationTolerance'):
the_max_r = r
max_r = r
else:
the_min_r = r + 1
min_r = r + 1
elif r == self.setting.get('maxNumOfLinearSections'):
self.lower_approximation_is_feasible = True
raise Exception("The model is infeasible. The lower approximation of the model is feasible, try "
"increasing the maximum number of linear sections")
else:
the_min_r = r + 1
min_r = r + 1
del model_lower, sol_lower
if the_max_r == the_min_r and r == the_max_r:
if max_r == min_r and r == max_r:
break
self.robust_solve_properties['upperLowerRelError'] = error
return r, sol_upper, model_upper
Expand Down

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