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¶

Attributes¶
`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.

Methods¶
`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.

Special methods¶
`__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 state 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. Only allowed in states LSState.PAUSED or LSState.STOPPED.

You can also use the shortcut member time_limit

Returns:	Time limit in seconds.
Return type:	`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 state 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
Return type:	`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 state 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. Only allowed in states LSState.PAUSED or LSState.STOPPED.

You can also use the shortcut member optimized_objective

Returns:	Index of the optimized objective.
Return type:	`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 state LSState.STOPPED.

You can also use the shortcut member enabled

Parameters:	enabled (`bool`) – True to enable the phase, False to disable it
Since:	5.0

LSPhase.is_enabled()¶

Returns true if the phase is enabled, False otherwise. Only allowed in states LSState.PAUSED or LSState.STOPPED.

You can also use the shortcut member enabled

Returns:	True if phase is enabled
Return type:	`bool`
Since:	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.
Return type:	`str`