def solve_model(scores, parents, solver, cycle_finding, gomory_cut, sink_heuristic, **kwargs):
# Variables to be globally used throughout ilp_model.cussens module
# Not using class here because we want everything to be accessed as cussens.<name>
global scores_input, parents_input, solver_input, cycle_finding_input, gomory_cut_input
scores_input = scores
parents_input = parents
solver_input = solver
cycle_finding_input = cycle_finding
gomory_cut_input = gomory_cut
# Generate initial problem
initial_problem = main_model.model_writer(scores_input, parents_input)
# Generate solver options for the solver selected
solver_options = generate_solver_options(solver_input, gomory_cut_input)
# Branch-and-Cut
optimal_solution_found = False
solver_results = None
problem_list = []
objective_upper_bound = float(- sys.maxint) # Arbitrarily small negative number (maximum supported by the system)
best_solution = None
best_solution_solver_results = None
# Add the initial formulation to problem_list
problem_list.append(initial_problem)
global current_problem
if sink_heuristic:
best_cutoff_value = - sys.maxint
while len(problem_list) > 0:
print ''
print 'Current best solution = ' + str(objective_upper_bound)
if sink_heuristic:
print 'Current cutoff value = ' + str(best_cutoff_value)
print 'Current number of problems in the list: ' + str(len(problem_list))
# Pop out the first problem on problem_list
current_problem = problem_list.pop(0)
solver_results = bayene.ilp_solver.call_solver(current_problem.main_model, solver_options, solver = solver, warmstart = True)
# If the problem is found infeasible, stop the solving process and go back to the beginning of the loop
if solver_results.solver.termination_condition == TerminationCondition.infeasible:
continue
else:
print 'Current problem solved successfully, Objective Value = ' + str(current_problem.main_model.objective())
# New Problem to be added to take all the cuts and heuristics results
new_problem = copy.deepcopy(current_problem)
########################
#### Cutting Planes ####
########################
# Cluster (Sub-IP)
new_problem_cluster_cut_applied = False
print('Searching for CLUSTER CUTS..')
# Get all the non-zero variables in the main model
# Also at the same time, check the solutions to find a non-integer variable.
current_non_zero_solution = {}
for key, value in current_problem.main_model.chosen_parent_variable.iteritems():
if float(value.value) > 0:
current_non_zero_solution[key] = float(value.value)
cluster_cuts_sub_ip_problem = cluster_cut_model.model_writer(current_non_zero_solution, len(scores_input), parents_input)
cluster_cuts_sub_ip_solver_options = {}
cluster_cuts_sub_ip_solver_options["LogFile"] = ''
# Send the cluster cut IP problem to the solver
cluster_cuts_sub_ip_solver_results = bayene.ilp_solver.call_solver(cluster_cuts_sub_ip_problem.main_model,
cluster_cuts_sub_ip_solver_options, solver=solver)
if cluster_cuts_sub_ip_solver_results.solver.termination_condition == TerminationCondition.optimal and cluster_cuts_sub_ip_problem.main_model.objective() > -1:
# Check if found cluster size > 0
cluster_members = [cluster_node for cluster_node in range(len(scores_input)) if cluster_cuts_sub_ip_problem.main_model.cluster_member_variable[cluster_node].value > 0]
if len(cluster_members) > 0:
print('Adding CLUSTER cuts to new problem.')
new_problem.add_cluster_cuts(cluster_members)
new_problem_cluster_cut_applied = True
else:
print('NO CLUSTER cuts applicable.')
else:
print('NO CLUSTER cuts applicable. Cluster Cut Sub-IP could not be solved.')
# Cycle cuts
new_problem_cycle_cut_applied = False
if cycle_finding_input:
print('Searching for CYCLE cuts..')
cycles_found = find_cycles(current_problem.main_model)
if len(cycles_found) > 0:
print('Adding CYCLE cuts to new problem.')
new_problem.add_cycle_cuts(cycles_found)
new_problem_cycle_cut_applied = True
else:
print('NO CYCLE cuts applicable.')
####################
#### Heuristics ####
####################
# Sink-Finding Heuristic
if sink_heuristic:
heuristic_total_score, heuristic_solutions, sink_heuristic_found = find_sink_heuristic(current_problem)
if new_problem_cluster_cut_applied or new_problem_cycle_cut_applied:
if sink_heuristic and sink_heuristic_found:
print 'Sink heuristic solution score = ' + str(heuristic_total_score)
# Use the total score obtained to be used as cutoff value
if heuristic_total_score > best_cutoff_value:
best_cutoff_value = heuristic_total_score
solver_options["Cutoff"] = best_cutoff_value
# Clear all the current variable values
new_problem.main_model.chosen_parent_variable.reset()
# Insert the solutions found from sink-finding for warmstart
for heuristic_key, heuristic_value in heuristic_solutions.iteritems():
new_problem.main_model.chosen_parent_variable[(heuristic_key[0], heuristic_key[1])].set_value(heuristic_value)
problem_list.append(new_problem)
continue
# If current_problem's objective value is lower than objective_upper_bound, move on to the next problem
if float(current_problem.main_model.objective()) <= objective_upper_bound:
print('Objective value is LOWER than or EQUAL to the incumbent.')
continue
all_solutions_integer = True
variable_to_branch_key = -1
non_integer_closeness_to_one = sys.maxint
for parent_key, parent_value in current_problem.main_model.chosen_parent_variable.iteritems():
if parent_value.value > 0:
if parent_value.value < 1:
all_solutions_integer = False
# Smaller the closer
if (1.0 - float(parent_value.value)) < non_integer_closeness_to_one:
variable_to_branch_key = copy.deepcopy(parent_key)
non_integer_closeness_to_one = 1.0 - float(parent_value.value)
if all_solutions_integer == True:
print('INTEGER solution found!')
objective_upper_bound = float(current_problem.main_model.objective())
best_solution = copy.deepcopy(current_problem)
best_solution_solver_results = solver_results
else:
# Get the variable to branch on: choose the one that has the closest value to 1
print 'Branching on ' + str(variable_to_branch_key) + ', ' + str(current_problem.main_model.chosen_parent_variable[variable_to_branch_key].value)
print('BRANCHING..')
new_problem_branch_1 = copy.deepcopy(current_problem)
new_problem_branch_2 = copy.deepcopy(current_problem)
# One problem with less than or equal to floor(non-integer)
new_problem_branch_1.add_branching(variable_to_branch_key, 'leq')
new_problem_branch_2.add_branching(variable_to_branch_key, 'geq')
problem_list.append(new_problem_branch_1)
problem_list.append(new_problem_branch_2)
print 'Final Objective Value: ' + str(best_solution.main_model.objective())
return best_solution, best_solution_solver_results
def solve_model(scores, parents, solver, cycle_finding, gomory_cut, sink_heuristic, **kwargs):
# Variables to be globally used throughout ilp_model.cussens module
# Not using class here because we want everything to be accessed as cussens.<name>
global scores_input, parents_input, solver_input, cycle_finding_input, gomory_cut_input
scores_input = scores
parents_input = parents
solver_input = solver
cycle_finding_input = cycle_finding
gomory_cut_input = gomory_cut
# Generate initial problem
current_problem = main_model.model_writer(scores_input, parents_input)
# Generate solver options for the solver selected
solver_options = generate_solver_options(solver_input, gomory_cut_input)
# Solution Process Control
optimal_solution_found = False
solver_results = None
best_solution = None
best_cutoff_value = - sys.maxint
objective_progress = []
heuristic_progress = []
while optimal_solution_found == False:
# Print empty lines between each iteration for better readability
print ''
if sink_heuristic:
print 'Current cutoff value = ' + str(best_cutoff_value)
# Send the current problem to the solver
solver_results = bayene.ilp_solver.call_solver(current_problem.main_model, solver_options, solver = solver, warmstart = True)
# If the problem is found infeasible, stop the solving process
if solver_results.solver.termination_condition == TerminationCondition.infeasible:
optimal_solution_found = True
continue
else:
print 'Current problem solved successfully, Objective Value = ' + str(current_problem.main_model.objective())
# Get all the non-zero variables in the main model
current_non_zero_solution = {}
# We need float() and .value because non_zero_value here is a Pyomo object (Don't make a fuss with Pyomo functions)
for non_zero_key, non_zero_value in current_problem.main_model.chosen_parent_variable.iteritems():
if float(non_zero_value.value) > 0.0:
current_non_zero_solution[(non_zero_key[0], non_zero_key[1])] = float(non_zero_value.value)
########################
#### Cutting Planes ####
########################
# Cluster (Sub-IP)
# Generate IP Cluster Cut Finding Model
print 'Searching for CLUSTER CUTS..'
cluster_cut_applied = False
# Generate cluster cut sub-ip problem
cluster_cuts_sub_ip_problem = cluster_cut_model.model_writer(current_non_zero_solution, len(scores_input), parents_input)
cluster_cuts_sub_ip_solver_options = {}
cluster_cuts_sub_ip_solver_options["LogFile"] = ''
# Send the cluster cut IP problem to the solver
cluster_cuts_sub_ip_solver_results = bayene.ilp_solver.call_solver(cluster_cuts_sub_ip_problem.main_model,
cluster_cuts_sub_ip_solver_options, solver=solver)
# If the problem was properly solved (objective should be strictly larger than -1)
if cluster_cuts_sub_ip_solver_results.solver.termination_condition == TerminationCondition.optimal \
and cluster_cuts_sub_ip_problem.main_model.objective() > -1:
# Check if found cluster size > 0
cluster_members = [node_key for node_key in xrange(len(scores_input))
if float(cluster_cuts_sub_ip_problem.main_model.cluster_member_variable[node_key].value) > 0]
if len(cluster_members) > 0:
print 'Adding CLUSTER cuts to new problem.'
current_problem.add_cluster_cuts(cluster_members)
cluster_cut_applied = True
else:
print 'NO CLUSTER cuts applicable.'
else:
print 'NO CLUSTER cuts applicable. Cluster Cut Sub-IP could not be solved.'
# Cycle cuts
cycle_cut_applied = False
if cycle_finding_input:
print 'Searching for CYCLE cuts..'
cycles_found = find_cycles(current_problem.main_model)
if len(cycles_found) > 0:
print 'Adding CYCLE cuts to new problem.'
current_problem.add_cycle_cuts(cycles_found)
cycle_cut_applied = True
else:
print 'NO CYCLE cuts applicable.'
####################
#### Heuristics ####
####################
# Sink-Finding Heuristic
if sink_heuristic:
print 'Performing Sink-finding algorithm..'
heuristic_total_score, heuristic_solutions, sink_heuristic_found = find_sink_heuristic(current_problem)
#####################################
#### Moving on to Next Iteration ####
#####################################
# If either cluster or cycle cut got applied, we should solve the problem again
if cluster_cut_applied or cycle_cut_applied:
objective_progress.append(current_problem.main_model.objective())
# If we have a heuristic solution, substitute current_problem with new_problem (which contains a heuristic solution)
# to allow the solver to make use of the solution.
if sink_heuristic and sink_heuristic_found:
print 'Sink heuristic solution score = ' + str(heuristic_total_score)
# Use the total score obtained to be used as cutoff value
if heuristic_total_score > best_cutoff_value:
best_cutoff_value = heuristic_total_score
# solver_options["Cutoff"] = best_cutoff_value
heuristic_progress.append(best_cutoff_value)
# Clear all the current variable values
current_problem.main_model.chosen_parent_variable.reset()
# Insert the solutions found from sink-finding for warmstart
for heuristic_key, heuristic_value in heuristic_solutions.iteritems():
current_problem.main_model.chosen_parent_variable[(heuristic_key[0], heuristic_key[1])].set_value(heuristic_value)
# Go to the next iteration of this loop
continue
# If we don't we need to solve the problem again, the optimal solution is found.
print 'INTEGER solution found!'
best_solution = copy.deepcopy(current_problem)
optimal_solution_found = True
print 'Final Objective Value: ' + str(best_solution.main_model.objective())
print 'Number of Cluster Cut Iteration: ' + str(best_solution.add_cluster_cuts_count)
print 'Number of Cycle Cut Iteration: ' + str(best_solution.add_cycle_cuts_count)
print 'Number of Total Cycle Cuts: ' + str(best_solution.add_cycle_total_count)
return best_solution, solver_results, objective_progress, heuristic_progress
