def GDPopt_iteration_loop(solve_data, config):
"""Algorithm main loop.
Returns True if successful convergence is obtained. False otherwise.
"""
while solve_data.master_iteration < config.iterlim:
# Set iteration counters for new master iteration.
solve_data.master_iteration += 1
solve_data.mip_iteration = 0
solve_data.nlp_iteration = 0
# print line for visual display
config.logger.info(
'---GDPopt Master Iteration %s---'
% solve_data.master_iteration)
# solve linear master problem
with time_code(solve_data.timing, 'mip'):
mip_result = solve_LOA_master(solve_data, config)
# Check termination conditions
if algorithm_should_terminate(solve_data, config):
break
# Solve NLP subproblem
if solve_data.active_strategy == 'LOA':
with time_code(solve_data.timing, 'nlp'):
nlp_result = solve_local_subproblem(mip_result, solve_data, config)
if nlp_result.feasible:
add_outer_approximation_cuts(nlp_result, solve_data, config)
elif solve_data.active_strategy == 'GLOA':
with time_code(solve_data.timing, 'nlp'):
nlp_result = solve_global_subproblem(mip_result, solve_data, config)
if nlp_result.feasible:
add_affine_cuts(nlp_result, solve_data, config)
elif solve_data.active_strategy == 'RIC':
with time_code(solve_data.timing, 'nlp'):
nlp_result = solve_local_subproblem(mip_result, solve_data, config)
else:
raise ValueError('Unrecognized strategy: ' + solve_data.active_strategy)
# Add integer cut
add_integer_cut(
mip_result.var_values, solve_data.linear_GDP, solve_data, config,
feasible=nlp_result.feasible)
# Check termination conditions
if algorithm_should_terminate(solve_data, config):
break
def GDPopt_iteration_loop(solve_data, config):
"""Algorithm main loop.
Returns True if successful convergence is obtained. False otherwise.
"""
while solve_data.master_iteration < config.iterlim:
# Set iteration counters for new master iteration.
solve_data.master_iteration += 1
solve_data.mip_iteration = 0
solve_data.nlp_iteration = 0
# print line for visual display
config.logger.info(
'---GDPopt Master Iteration %s---'
% solve_data.master_iteration)
# solve linear master problem
with time_code(solve_data.timing, 'mip'):
mip_result = solve_LOA_master(solve_data, config)
# Check termination conditions
if algorithm_should_terminate(solve_data, config):
break
# Solve NLP subproblem
if solve_data.current_strategy == 'LOA':
with time_code(solve_data.timing, 'nlp'):
nlp_result = solve_local_subproblem(mip_result, solve_data, config)
if nlp_result.feasible:
add_outer_approximation_cuts(nlp_result, solve_data, config)
elif solve_data.current_strategy == 'GLOA':
with time_code(solve_data.timing, 'nlp'):
nlp_result = solve_global_subproblem(mip_result, solve_data, config)
if nlp_result.feasible:
add_affine_cuts(nlp_result, solve_data, config)
# Add integer cut
add_integer_cut(
mip_result.var_values, solve_data.linear_GDP, solve_data, config,
feasible=nlp_result.feasible)
# Check termination conditions
if algorithm_should_terminate(solve_data, config):
break