Solver Option Guide

How to Set Solver Option
List of Options

How to Set Solver Option

The options of the PRINTEMPS solver can be set by a printemps::option::Option object. The following code shows an example of setting the options verbose and tabu_search.iteration_max to Full and 10000, respectively.

printemps::model::IPModel model;

/// The modeling code is omitted.

printemps::option::Option option;
option.output.verbose = printemps::option:verbose::Full;
option.tabu_search.iteration_max = 10000;

/// The option object is set as the 2nd argument of printemps::solver::solve().
auto result = printemps::solver::solve(&model, option);

List of Options

General Options

Name	Type	Default	Description
`iteration_max`	`int`	`100`	Allowed maximum number of outer loop iterations.
`time_max`	`double`	`120`	Allowed maximum computational time for optimization calculation (specified in seconds).
`target_objective`	`double`	`1E100` or `-1E100` or `0`	Target objective function value. If the objective the function of the feasible incumbent reaches `target_objective` , optimization calculation will be terminated. For minimization problems and maximization problems, `-1E100` and `1E100` will be set as default values, respectively. If the objective function is not defined, the target will be set `0` and the optimization will be terminated if a feasible solution is found.

Penalty Options

Name	Type	Default	Description
`penalty_coefficient_relaxing_rate`	`double`	`0.9`	If the augmented objective incumbent obtained by a tabu search is feasible, all penalty coefficients will be relaxed by the multiplying `penalty_coefficient_relaxing_rate`.
`penalty_coefficient_tightening_rate`	`double`	`1.0`	If the augmented objective incumbent obtained by a tabu search is not feasible, penalty coefficients corresponding violative constraints will be tightened by the adding violation multiplied by `penalty_coefficient_tightening_rate`.
`initial_penalty_coefficient`	`double`	`1E7`	Initial penalty coefficient value. The penalty coefficients corresponding to all constraints are initialized by `initial_penalty_coefficient`.
`is_enabled_grouping_penalty_coefficient`	`bool`	`false`	If this option is set `true`, the penalty coefficients for constraints belonging to the same constraint group are aligned to the largest one in the group. Here, "constraint groups" are constraints which share common symbol and are distinguished by their indices.

Parallel Options

Name	Type	Default	Description
`is_enabled_move_evaluation_parallelization`	`bool`	`true`	If this option is set `true`, evaluations of solutions will be parallelized using OpenMP.
`is_enabled_move_update_parallelization`	`bool`	`true`	If this option is set `true`, updating the neighborhood of the current solution will be parallelized using OpenMP.
`is_enabled_automatic_move_update_parallelization`	`bool`	`true`	If this option is set `true`, parallelization of evaluating solutions are automatically enabled or disabled according to real-measured computational speed.
`is_enabled_automatic_move_evaluation_parallelization`	`bool`	`true`	If this option is set `true`, parallelization of updating neighborhood moves are automatically enabled or disabled according to real-measured computational speed.

Preprocess Options

Name	Type	Default	Description
`is_enabled_presolve`	`bool`	`true`	If this option is set `true`, redundant decision variables and constraints will be fixed or moved.
`is_enabled_remove_duplicated_constraints`	`bool`	`true`	If this option is set `true`, duplicated constraints will be extracted and removed. This option works only if `is_enabled_presolve` is set `true`.
`is_enabled_remove_redundant_set_variables`	`bool`	`true`	If this option is set `true`, redundant set partitioning/packing/covering constraints will be extracted and removed. This option works only if `is_enabled_presolve` is set `true`.
`is_enabled_remove_redundant_set_constraints`	`bool`	`true`	If this option is set `true`, redundant decision variables included in set partitioning constraints will be extracted and fixed at `0`. This option works only if `is_enabled_presolve` is set `true`.
`is_enabled_extract_implicit_equality_constraint`	`bool`	`true`	If this option is set `true`, inequality constraint pairs that implicitly define equality constraints will be extracted and replaced with the corresponding equality constraints.
`is_enabled_online_bounding`	`bool`	`true`	If this option is set `true`, lower and upper bounds of decision variables will be successively tightened based on incumbent objective value obtained in optimization process.
`is_enabled_initial_value_correction`	`bool`	`true`	If this option is set `true`, initial values of decision variables will be corrected necessarily so that they satisfy ther lower and upper bounds and so on.
`is_enabled_extract_dependent_exclusive_or`	`bool`	`true`	If this option is set `true`, dependent variables are extracted from `Exclusive OR` constraints.
`is_enabled_extract_dependent_exclusive_nor`	`bool`	`true`	If this option is set `true`, dependent variables are extracted from `Exclusive NOR` constraints.
`is_enabled_extract_dependent_inverted_integers`	`bool`	`true`	If this option is set `true`, dependent variables are extracted from `Inverted Integers` constraints.
`is_enabled_extract_dependent_balanced_integers`	`bool`	`true`	If this option is set `true`, dependent variables are extracted from `Balanced Integers` constraints.
`is_enabled_extract_dependent_constant_sum_integers`	`bool`	`true`	If this option is set `true`, dependent variables are extracted from `Constant Sum Integers` constraints.
`is_enabled_extract_dependent_constant_difference_integers`	`bool`	`true`	If this option is set `true`, dependent variables are extracted from `Constant Difference Integers` constraints.
`is_enabled_extract_dependent_constant_ratio_integers`	`bool`	`true`	If this option is set `true`, dependent variables are extracted from `Constant Ratio Integers` constraints.
`is_enabled_extract_dependent_intermediate`	`bool`	`true`	If this option is set `true`, dependent variables are extracted from `Intermediate` constraints.

Neighborhood Options

Name	Type	Default	Description
`is_enabled_binary_move`	`bool`	`true`	If this option is set `true`, neighborhood moves that flip binary variables are enabled.
`is_enabled_integer_move`	`bool`	`true`	If this option is set `true`, neighborhood moves that increment or decrement integer variables are enabled.
`is_enabled_exclusive_or_move`	`bool`	`true`	If this option is set `true`, neighborhood moves specialized for `Exclusive OR` constraints are enabled.
`is_enabled_exclusive_nor_move`	`bool`	`true`	If this option is set `true`, neighborhood moves specialized for `Exclusive NOR` constraints are enabled.
`is_enabled_inverted_integers_move`	`bool`	`true`	If this option is set `true`, neighborhood moves specialized for `Inverted Integers` constraints are enabled.
`is_enabled_balanced_integers_move`	`bool`	`true`	If this option is set `true`, neighborhood moves specialized for `Balanced Integers` constraints are enabled.
`is_enabled_constant_sum_integers_move`	`bool`	`true`	If this option is set `true`, neighborhood moves specialized for `Constant Sum Integers` constraints are enabled.
`is_enabled_constant_difference_integers_move`	`bool`	`true`	If this option is set `true`, neighborhood moves specialized for `Constant Difference Integers` constraints are enabled.
`is_enabled_constant_rario_integers_move`	`bool`	`true`	If this option is set `true`, neighborhood moves specialized for `Constant Ratio Integers` constraints are enabled.
`is_enabled_aggregation_move`	`bool`	`true`	If this option is set `true`, neighborhood moves specialized for Aggregation constraints are enabled.
`is_enabled_precedence_move`	`bool`	`false`	If this option is set `true`, neighborhood moves specialized for Precedence constraints are enabled.
`is_enabled_variable_bound_move`	`bool`	`false`	If this option is set `true`, neighborhood moves specialized for Variable Bound constraints are enabled.
`is_enabled_trinomial_exclusive_nor_move`	`bool`	`false`	If this option is set `true`, neighborhood moves specialized for `Trinomial Exclusive NOR` constraints are enabled.
`is_enabled_soft_selection_move`	`bool`	`false`	If this option is set `true`, neighborhood moves specialized for `Soft Selection` constraints are enabled.
`is_enabled_chain_move`	`bool`	`true`	If this option is set `true`, two or more moves will be combined adaptively.
`selection_mode`	`printemps::option::` `selection_mode`	`Independent`	Rule to detect and extract selection constraints. `None`: No selection constraints will be extracted. `Defined`: If a decision variable is included in more than one selection constraints, the selection constraint defined at first will extracted in priority. `Smaller`: If a decision variable is included in more than one selection constraints, the selection constraint with the smaller number of decision variables will extracted in priority. `Larger`: If a decision variable is included in more than one selection constraints, the selection constraint with the larger number of decision variables will extracted in priority. `Independent` Selection constraints that do not share decision variables with each other will be extracted.
`improvability_screening_mode`	`printemps::option::` `improvability_screening_mode`	`Automatic`	Rule for screening neighborhood moves. `Off`: No screening. `Soft`: Only moves with objective or feasibility improvement will be selected. `Aggressive`: If the current solution is infeasible, only moves with feasibility improvement will be selected. `Intensive`: Similar to `Aggressive`, but moves will be more narrowly defined. `Automatic`: Depending on the search situation, either `Soft`, `Aggressive`, or `Intensive` screening will be automatically applied.

Output Options

Name	Type	Default	Description
`verbose`	`printemps::option::` `verbose`	`Off`	Log level of standard output. `Off`: No output. `Warning`: Output only warning messages. `Outer`: Output warning messages and intermediate results of outer loops. `Inner`: Output warning messages and intermediate results of outer and inner tabu search loops. `Full`: Output full information.
`is_enabled_write_trend`	`bool`	`false`	If this option is set `true`, search trend will be written in a text file named `trend.txt`. The ouput file can be visualized by `script/visualize_trend.py`.
`is_enabled_store_feasible_solutions`	`bool`	`false`	If this option is set `true`, feasible solutions found in the search will be stored, and written in a JSON file named `feasible.json`. The ouput file can be visualized by `script/visualize_solutions.py`. Activating this option with large `feasible_solutions_capacity` would affect search performance especially for problems with too many feasible solutions.
`feasible_solutions_capacity`	`int`	`1000`	Allowed maximum number of the stored feasible solutions. If the capacity is exceeded, worse solutions will be clipped.

Lagrange Dual Search Options

Name	Type	Default	Description
`is_enabled`	`bool`	`false`	If this option is set `true`, an initial solution will be prepared by solving a lagrange dual problem by a subgradient method.
`iteration_max`	`int`	`10000`	Allowed maximum number of the lagrange dual search loop iterations.
`time_max`	`double`	`120`	Allowed maximum computational time for lagrange dual search (specified in seconds).
`step_size_extend_rate`	`double`	`1.05`	If it is observed that the step size is too small, the subgradient algorithm will extend the step size according to the value of this option. Larger value accelerates the convergence speed but it may also affect the stability.
`step_size_reduce_rate`	`double`	`0.95`	If it is observed that the step size is too large, the subgradient algorithm will reduce the step size according to the value of this option.
`tolerance`	`double`	`1E-5`	The tolerance for determining convergence of the subgradient algorithm. It should be specified as a ratio to the absolute value of the Lagrangian.
`log_interval`	`int`	`10`	Iteration interval to print the calculation log to standard output. This option is effective only if `verbose` is set `Full`.

Local Search Options

Name	Type	Default	Description
`is_enabled`	`bool`	`true`	If this option is set `true`, the initial solution will be improved by a greedy local search.
`iteration_max`	`int`	`10000`	Allowed maximum number of the local search loop iterations.
`time_max`	`double`	`120`	Allowed maximum computational time for local search (specified in seconds).
`log_interval`	`int`	`10`	Iteration interval to print the calculation log to standard output. This option is effective only if `verbose` is set `Full`.

Tabu Search Options

Name	Type	Default	Description
`iteration_max`	`int`	`200`	Allowed maximum number of one tabu search loop iterations.
`time_max`	`double`	`120`	Allowed maximum computational time for one tabu search (specified in seconds).
`log_interval`	`int`	`10`	Iteration interval to print the calculation log to standard output. This option is effective only if `verbose` is set `Full`.
`initial_tabu_tenure`	`int`	`10`	Initial tabu tenure. The value of a decision variable can not be changed again until the number of tabu tenure iterations has passed.
`tabu_tenure_randomize_rate`	`double`	`0.3`	Tabu tenure for each move will be randomized according to the value of this option. The value should be specified as the rate to the tabu tenure. For example, if the nominal tabu tenure is internally set `20` and `tabu_tenure_randomize_rate` is set `0.5`, the actual tabu tenure for each move will be set randomly in the range `[10, 30]`.
`tabu_mode`	`printemps::option::` `tabu_mode`	`All`	Rule to determine the acceptability (tabu or not) of a move from the current solution to a neighborhood solution for the case that the move consists of multiple variable changes. `All`: The move is not acceptable if all of the variables included in the move are "tabu". `Any`: The move is not acceptable if any variable includes in the move is "tabu".