Linearized order¶

class
rtctools.optimization.linearized_order_goal_programming_mixin.
LinearizedOrderGoalProgrammingMixin
(**kwargs)[source]¶ Bases:
rtctools.optimization.goal_programming_mixin_base._GoalProgrammingMixinBase
Adds support for linearization of the goal objective functions, i.e. the violation variables to a certain power. This can be used to keep a problem fully linear and/or make sure that no quadratic constraints appear when using the goal programming option
keep_soft_constraints
.
goal_programming_options
()[source]¶ If
linearize_goal_order
is set toTrue
, the goal’s order will be approximated linearly for any goals where order > 1. Note that this option does not work with minimization goals of higher order. Instead, it is suggested to transform these minimization goals into goals with a target (and function range) when using this option. Note that this option can be overriden on the level of a goal by using aLinearizedOrderGoal
(seeLinearizedOrderGoal.linearize_order
).


class
rtctools.optimization.linearized_order_goal_programming_mixin.
LinearizedOrderGoal
[source]¶ Bases:
rtctools.optimization.goal_programming_mixin_base.Goal

linearize_order
= None¶ Override linearization of goal order. Related global goal programming option is
linearize_goal_order
(seeLinearizedOrderGoalProgrammingMixin.goal_programming_options()
). The default value of None defers to the global option, but the user can explicitly override it per goal by setting this value to True or False.


class
rtctools.optimization.linearized_order_goal_programming_mixin.
LinearizedOrderStateGoal
(optimization_problem)[source]¶ Bases:
rtctools.optimization.linearized_order_goal_programming_mixin.LinearizedOrderGoal
,rtctools.optimization.goal_programming_mixin_base.StateGoal
Convenience class definition for linearized order state goals. Note that it is possible to just inherit from
LinearizedOrderGoal
to get the needed functionality for control of the linearization at goal level.
__init__
(optimization_problem)¶ Initialize the state goal object.
Parameters: optimization_problem – OptimizationProblem
instance.
