cross
Creates and configures a cross-entropy optimization algorithm for a given problem definition that uses a random restart approach.
Return
An instance of RandomRestartSolver that encapsulates the optimization process and results.
Parameters
The definition of the optimization problem, including constraints and objectives.
The model builder interface used to create models for evaluation.
The maximum number of restarts to be performed.
The cross-entropy sampler. By default, it is CENormalSampler
The maximum number of iterations the algorithm will run. Defaults to 1000.
The number of replications to use during each evaluation to reduce stochastic noise. Defaults to 50.
Specifies if the evaluator uses a solution cache. By default, this is MemorySolutionCache.
Specifies if the simulation oracle will use a SimulationRunCache. The default is null (no cache).
Optional callback function to print or handle intermediate solutions. Can be used to observe the restart optimization process.
Optional callback function to print or handle intermediate solutions. Can be used to observe the inner solver optimization process.
the run parameters to apply to the model during the building process
indicates if a default KSL database should be created and attached to the model. The default is false.