LSPhase Class¶

class localsolver.LSPhase

Optimization phase. Since LocalSolver allows lexicographic multiobjective optimization, we allow you to parameterize the time or the number of iterations to spend in optimizing each objective. When multiple objectives are declared, these objectives are optimized in lexicographic order (in the order of their declaration in the model). Thus, if k objectives 0, ..., k-1 are declared, each objective i can be optimized while maintaining the values of objectives 0, ..., i-1 previously obtained and ignoring the values of objectives i+1, ..., k-1. During such an optimization phase, objective i is called the optimized objective.

At least one phase must be created to launch the solver. If you have only one objective to optimize, then create a phase, set the time or the number of iterations, and run the solver. By default, the optimized objective of a phase corresponds to the last objective declared in the model (in this case, all objectives of the model are optimized together).

Summary¶

 time_limit Time limit of this phase in seconds. iteration_limit Iteration limit of this phase. optimized_objective Index of the optimized objective of this phase. enabled Enabled status of this phase.
 set_time_limit Sets the time limit of this phase in seconds. get_time_limit Gets the time limit of this phase in seconds. set_iteration_limit Sets the number of iterations of this phase. get_iteration_limit Gets the number of iterations of this phase. set_optimized_objective Sets the optimized objective of this phase. get_optimized_objective Gets the index of the optimized objective. set_enabled Sets the enabled status of this phase. is_enabled Returns true if the phase is enabled, False otherwise.
 __str__ Returns a string representation of this phase.

Instance methods¶

LSPhase.set_time_limit(limit)

Sets the time limit of this phase in seconds. Note that the “real” time (that is, total time) spent to resolve the model is considered here (and not only the CPU time). The default time limit is set to the largest positive integer on 32 bits, that is 2^31-1 = 2,147,483,647 > 10^9. Only allowed in states LSState.MODELING or LSState.STOPPED.

Note that if the optimal value of an objective is found before the end of the corresponding phase, the remaining time will be transferred to the next phase.

You can also use the shortcut member time_limit

Parameters: limit (int) – Time limit in seconds.
LSPhase.get_time_limit()

Gets the time limit of this phase in seconds.

You can also use the shortcut member time_limit

Returns: Time limit in seconds. int
LSPhase.set_iteration_limit(limit)

Sets the number of iterations of this phase. Fixing the seed and the number of iterations of all phases ensures the reproducibility of results over several runs. The default number of iterations is set to the largest positive integer on 64 bits, that is 2^63-1 = 9,223,372,036,854,775,807 > 10^18. Only allowed in states LSState.MODELING or LSState.STOPPED.

Note that if the optimal value of an objective is found before the end of the corresponding phase, the remaining iterations will be transferred to the next phase.

You can also use the shortcut member iteration_limit

Parameters: limit (int) – Iteration limit
LSPhase.get_iteration_limit()

Gets the number of iterations of this phase. Only allowed in states LSState.PAUSED or LSState.STOPPED.

You can also use the shortcut member iteration_limit

Returns: Iteration limit int
LSPhase.set_optimized_objective(obj_index)

Sets the optimized objective of this phase. When multiple objectives are declared, these objectives are optimized in lexicographic order (in the order of their declaration in the model). Thus, if k objectives 0, ..., k-1 are declared, each objective i can be optimized while maintaining the values of objectives 0, ..., i-1 previously obtained and ignoring the values of objectives i+1, ..., k-1. By default, the optimized objective corresponds to the last objective declared in the model. Only allowed in states LSState.MODELING or LSState.STOPPED.

You can also use the shortcut member optimized_objective

Parameters: obj_index (int) – Index of the optimized objective
LSPhase.get_optimized_objective(obj_index)

Gets the index of the optimized objective.

You can also use the shortcut member optimized_objective

Returns: Index of the optimized objective. int
LSPhase.set_enabled(enabled)

Sets the enabled status of this phase. The solver only optimize enabled phases. Disabling a phase can be useful when an objective has been removed from the model by LSModel.remove_objective(). By default, a phase is enabled. Only allowed in states LSState.MODELING or LSState.STOPPED.

You can also use the shortcut member enabled

Parameters: enabled (bool) – True to enable the phase, False to disable it 5.0
LSPhase.is_enabled()

Returns true if the phase is enabled, False otherwise.

You can also use the shortcut member enabled

Returns: True if phase is enabled bool 5.0

Instance attributes¶

All get/set methods have their attribute counterpart. You can use them as shortcuts to improve the readability or your models and codes.

LSPhase.time_limit

Time limit of this phase in seconds. It is a shortcut for get_time_limit() and set_time_limit().

LSPhase.iteration_limit

Iteration limit of this phase. It is a shortcut for get_iteration_limit() and set_iteration_limit().

LSPhase.optimized_objective

Index of the optimized objective of this phase. It is a shortcut for get_optimized_objective() and set_optimized_objective().

LSPhase.enabled

Enabled status of this phase. It is a shortcut for is_enabled() and set_enabled().

Special operators and methods¶

LSPhase.__str__()

Returns a string representation of this phase. Useful for debugging or logging purposes.

Returns: String representation of this phase. str