Searched refs:nlp_model (Results 1 – 4 of 4) sorted by relevance
| /dports/math/py-Pyomo/Pyomo-6.1.2/pyomo/contrib/pyros/ |
| H A D | master_problem_methods.py | 262 nlp_model = model_data.master_model
265 for dr_var in nlp_model.scenarios[0, 0].util.decision_rule_vars:
269 nlp_model.const_efficiency_applied = False
270 nlp_model.linear_efficiency_applied = False
272 nlp_model.const_efficiency_applied = True
280 nlp_model.linear_efficiency_applied = True
295 nlp_model = model_data.master_model
314 results = solver.solve(nlp_model, tee=config.tee)
333 idx = max(nlp_model.scenarios.keys())[0]
338 master_soln.nominal_block = nlp_model.scenarios[0, 0]
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| H A D | separation_problem_methods.py | 427 nlp_model = model_data.separation_model
436 results = solver.solve(nlp_model, tee=config.tee)
461 objective = str(list(nlp_model.component_data_objects(Objective, active=True))[0].name)
463 config.uncertainty_set.type + "_" + nlp_model.name + "_separation_" +
465 nlp_model.write(name, io_options={'symbolic_solver_labels':True})
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| /dports/math/py-Pyomo/Pyomo-6.1.2/pyomo/contrib/gdpopt/ |
| H A D | nlp_solve.py | 95 def solve_NLP(nlp_model, solve_data, config): argument
102 unfixed_discrete_vars = detect_unfixed_discrete_vars(nlp_model)
107 GDPopt = nlp_model.GDPopt_utils
109 initialize_subproblem(nlp_model, solve_data)
112 config.call_before_subproblem_solve(nlp_model, solve_data)
128 results = nlp_solver.solve(nlp_model, **nlp_args)
142 nlp_model.dual.get(c, None)
156 if is_feasible(nlp_model, config):
192 config.call_after_subproblem_solve(nlp_model, solve_data)
379 nlp_model = solve_data.working_model.clone()
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| /dports/math/py-Pyomo/Pyomo-6.1.2/pyomo/contrib/gdpopt/tests/ |
| H A D | test_gdpopt.py | 347 def assert_correct_disjuncts_active(nlp_model, solve_data): argument
354 nlp_model.GDPopt_utils.disjunct_list
558 def assert_correct_disjuncts_active(nlp_model, solve_data): argument
565 nlp_model.GDPopt_utils.disjunct_list
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