KSLCore/ksl.simopt.solvers/Solver/Companion

Companion

Properties

Represents the default maximum number of iterations to be executed in a given process or algorithm. This value acts as a safeguard to prevent indefinite looping or excessive computation.

defaultReplicationsPerEvaluation

@JvmStatic

var defaultReplicationsPerEvaluation: Int

Represents the default number of replications to be performed during an evaluation.

defaultSolutionPrecision

var defaultSolutionPrecision: Double

Many algorithms compare solutions. This factor serves as the default precision between two solutions such that if the solutions are within this value they are considered equal. The default is 0.01

logger

@JvmStatic

val logger: KLogger

Logger instance used for logging messages. Utilizes the KotlinLogging framework to facilitate structured and leveled logging.

Functions

crossEntropySolver

@JvmStatic

@JvmOverloads

fun crossEntropySolver(problemDefinition: ProblemDefinition, modelBuilder: ModelBuilderIfc, ceSampler: CESamplerIfc = CENormalSampler(problemDefinition), startingPoint: MutableMap<String, Double>? = null, maxIterations: Int = defaultMaxNumberIterations, replicationsPerEvaluation: Int = defaultReplicationsPerEvaluation, printer: (Solver) -> Unit? = null): CrossEntropySolver

Creates and configures a simulated annealing optimization algorithm for a given problem definition.

simulatedAnnealingSolver

@JvmStatic

@JvmOverloads

fun simulatedAnnealingSolver(problemDefinition: ProblemDefinition, modelBuilder: ModelBuilderIfc, startingPoint: MutableMap<String, Double>? = null, initialTemperature: Double = defaultInitialTemperature, maxIterations: Int = defaultMaxNumberIterations, replicationsPerEvaluation: Int = defaultReplicationsPerEvaluation, printer: (Solver) -> Unit? = null): SimulatedAnnealing

Creates and configures a simulated annealing optimization algorithm for a given problem definition.

stochasticHillClimbingSolver

@JvmStatic

@JvmOverloads

fun stochasticHillClimbingSolver(problemDefinition: ProblemDefinition, modelBuilder: ModelBuilderIfc, startingPoint: MutableMap<String, Double>? = null, maxIterations: Int = defaultMaxNumberIterations, replicationsPerEvaluation: Int = defaultReplicationsPerEvaluation, printer: (Solver) -> Unit? = null): StochasticHillClimber

Creates and configures a stochastic hill climber to solve the given problem definition using a simulation-based evaluation approach. The default configuration has the evaluator configured to use a solution cache.