LazyOACallback_cplex

(class from pyomo.contrib.mindtpy.single_tree)

class pyomo.contrib.mindtpy.single_tree.LazyOACallback_cplex[source]

Bases: object

Inherent class in CPLEX to call Lazy callback.

__init__()

Methods

__init__()

add_lazy_affine_cuts(mindtpy_solver, config, opt)

Adds affine cuts using MCPP.

add_lazy_no_good_cuts(var_values, ...[, ...])

Adds no-good cuts.

add_lazy_oa_cuts(target_model, dual_values, ...)

Linearizes nonlinear constraints; add the OA cuts through CPLEX inherent function self.add() For nonconvex problems, turn on 'config.add_slack'.

copy_lazy_var_list_values(opt, from_list, ...)

This function copies variable values from one list to another.

handle_lazy_main_feasible_solution(main_mip, ...)

This function is called during the branch and bound of main mip, more exactly when a feasible solution is found and LazyCallback is activated.

handle_lazy_subproblem_infeasible(fixed_nlp, ...)

Solves feasibility NLP subproblem and adds cuts according to the specified strategy.

handle_lazy_subproblem_optimal(fixed_nlp, ...)

This function copies the optimal solution of the fixed NLP subproblem to the MIP main problem(explanation see below), updates bound, adds OA and no-good cuts, stores incumbent solution if it has been improved.

handle_lazy_subproblem_other_termination(...)

Handles the result of the latest iteration of solving the NLP subproblem given a solution that is neither optimal nor infeasible.

Member Documentation

add_lazy_affine_cuts(mindtpy_solver, config, opt)[source]

Adds affine cuts using MCPP.

Add affine cuts through CPLEX inherent function self.add().

Parameters:

  • mindtpy_solver (object) – The mindtpy solver class.

  • config (ConfigBlock) – The specific configurations for MindtPy.

  • opt (SolverFactory) – The cplex_persistent solver.

add_lazy_no_good_cuts(var_values, mindtpy_solver, config, opt, feasible=False)[source]

Adds no-good cuts.

Add the no-good cuts through Cplex inherent function self.add().

Parameters:

  • var_values (list) – The variable values of the incumbent solution, used to generate the cut.

  • mindtpy_solver (object) – The mindtpy solver class.

  • config (ConfigBlock) – The specific configurations for MindtPy.

  • opt (SolverFactory) – The cplex_persistent solver.

  • feasible (bool, optional) – Whether the integer combination yields a feasible or infeasible NLP, by default False.

Raises:

ValueError – The value of binary variable is not 0 or 1.

add_lazy_oa_cuts(target_model, dual_values, mindtpy_solver, config, opt, linearize_active=True, linearize_violated=True)[source]

Linearizes nonlinear constraints; add the OA cuts through CPLEX inherent function self.add() For nonconvex problems, turn on ‘config.add_slack’. Slack variables will always be used for nonlinear equality constraints.

Parameters:

  • target_model (Pyomo model) – The MIP main problem.

  • dual_values (list) – The value of the duals for each constraint.

  • mindtpy_solver (object) – The mindtpy solver class.

  • config (ConfigBlock) – The specific configurations for MindtPy.

  • opt (SolverFactory) – The cplex_persistent solver.

  • linearize_active (bool, optional) – Whether to linearize the active nonlinear constraints, by default True.

  • linearize_violated (bool, optional) – Whether to linearize the violated nonlinear constraints, by default True.

copy_lazy_var_list_values(opt, from_list, to_list, config, skip_stale=False, skip_fixed=True)[source]

This function copies variable values from one list to another. Rounds to Binary/Integer if necessary. Sets to zero for NonNegativeReals if necessary.

Parameters:

  • opt (SolverFactory) – The cplex_persistent solver.

  • from_list (list) – The variable list that provides the values to copy from.

  • to_list (list) – The variable list that needs to set value.

  • config (ConfigBlock) – The specific configurations for MindtPy.

  • skip_stale (bool, optional) – Whether to skip the stale variables, by default False.

  • skip_fixed (bool, optional) – Whether to skip the fixed variables, by default True.

handle_lazy_main_feasible_solution(main_mip, mindtpy_solver, config, opt)[source]

This function is called during the branch and bound of main mip, more exactly when a feasible solution is found and LazyCallback is activated. Copy the result to working model and update upper or lower bound. In LP-NLP, upper or lower bound are updated during solving the main problem.

Parameters:

  • main_mip (Pyomo model) – The MIP main problem.

  • mindtpy_solver (object) – The mindtpy solver class.

  • config (ConfigBlock) – The specific configurations for MindtPy.

  • opt (SolverFactory) – The cplex_persistent solver.

handle_lazy_subproblem_infeasible(fixed_nlp, mindtpy_solver, config, opt)[source]

Solves feasibility NLP subproblem and adds cuts according to the specified strategy.

Parameters:

  • fixed_nlp (Pyomo model) – Integer-variable-fixed NLP model.

  • mindtpy_solver (object) – The mindtpy solver class.

  • config (ConfigBlock) – The specific configurations for MindtPy.

  • opt (SolverFactory) – The cplex_persistent solver.

handle_lazy_subproblem_optimal(fixed_nlp, mindtpy_solver, config, opt)[source]

This function copies the optimal solution of the fixed NLP subproblem to the MIP main problem(explanation see below), updates bound, adds OA and no-good cuts, stores incumbent solution if it has been improved.

Parameters:

  • fixed_nlp (Pyomo model) – Integer-variable-fixed NLP model.

  • mindtpy_solver (object) – The mindtpy solver class.

  • config (ConfigBlock) – The specific configurations for MindtPy.

  • opt (SolverFactory) – The cplex_persistent solver.

handle_lazy_subproblem_other_termination(fixed_nlp, termination_condition, mindtpy_solver, config)[source]

Handles the result of the latest iteration of solving the NLP subproblem given a solution that is neither optimal nor infeasible.

Parameters:

  • fixed_nlp (Pyomo model) – Integer-variable-fixed NLP model.

  • termination_condition (Pyomo TerminationCondition) – The termination condition of the fixed NLP subproblem.

  • mindtpy_solver (object) – The mindtpy solver class.

  • config (ConfigBlock) – The specific configurations for MindtPy.

Raises:

ValueError – MindtPy unable to handle the termination condition of the fixed NLP subproblem.