KSLCore/ksl.simopt.solvers/Solver/Companion

Companion

object Companion(source)

Properties

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@JvmStatic
var defaultMaxNumberIterations: Int

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.

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@JvmStatic
var defaultReplicationsPerEvaluation: Int

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

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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 0.001

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@JvmStatic
val logger: KLogger

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

Functions

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@JvmStatic
@JvmOverloads
fun crossEntropySolver(problemDefinition: ProblemDefinition, modelBuilder: ModelBuilderIfc, ceSampler: CESamplerIfc = CENormalSampler(problemDefinition), startingPoint: MutableMap<String, Double>? = null, maxIterations: Int = defaultMaxNumberIterations, replicationsPerEvaluation: Int = defaultReplicationsPerEvaluation, solutionCache: SolutionCacheIfc = MemorySolutionCache(), simulationRunCache: SimulationRunCacheIfc? = null, printer: (Solver) -> Unit? = null, experimentRunParameters: ExperimentRunParametersIfc? = null, defaultKSLDatabaseObserverOption: Boolean = false): CrossEntropySolver

Creates and configures a cross-entropy optimization algorithm for a given problem definition.

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@JvmStatic
@JvmOverloads
fun crossEntropySolverWithRestarts(problemDefinition: ProblemDefinition, modelBuilder: ModelBuilderIfc, maxNumRestarts: Int = defaultMaxRestarts, ceSampler: CESamplerIfc = CENormalSampler(problemDefinition), maxIterations: Int = defaultMaxNumberIterations, replicationsPerEvaluation: Int = defaultReplicationsPerEvaluation, solutionCache: SolutionCacheIfc = MemorySolutionCache(), simulationRunCache: SimulationRunCacheIfc? = null, restartPrinter: (Solver) -> Unit? = null, printer: (Solver) -> Unit? = null, experimentRunParameters: ExperimentRunParametersIfc? = null, defaultKSLDatabaseObserverOption: Boolean = false): RandomRestartSolver

Creates and configures a cross-entropy optimization algorithm for a given problem definition that uses a random restart approach.

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@JvmStatic
@JvmOverloads
fun rSplineSolver(problemDefinition: ProblemDefinition, modelBuilder: ModelBuilderIfc, initialNumReps: Int = defaultInitialSampleSize, sampleSizeGrowthRate: Double = defaultReplicationGrowthRate, maxNumReplications: Int = defaultMaxNumReplications, startingPoint: MutableMap<String, Double>? = null, maxIterations: Int = defaultMaxNumberIterations, solutionCache: SolutionCacheIfc = MemorySolutionCache(), simulationRunCache: SimulationRunCacheIfc? = null, printer: (Solver) -> Unit? = null, experimentRunParameters: ExperimentRunParametersIfc? = null, defaultKSLDatabaseObserverOption: Boolean = false): RSplineSolver

Creates and configures an R-SPLINE optimization algorithm for a given problem definition.

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@JvmStatic
@JvmOverloads
fun rSplineSolverWithRestarts(problemDefinition: ProblemDefinition, modelBuilder: ModelBuilderIfc, maxNumRestarts: Int = defaultMaxRestarts, initialNumReps: Int = defaultInitialSampleSize, sampleSizeGrowthRate: Double = defaultReplicationGrowthRate, maxNumReplications: Int = defaultMaxNumReplications, maxIterations: Int = defaultMaxNumberIterations, replicationsPerEvaluation: Int = defaultReplicationsPerEvaluation, solutionCache: SolutionCacheIfc = MemorySolutionCache(), simulationRunCache: SimulationRunCacheIfc? = null, restartPrinter: (Solver) -> Unit? = null, printer: (Solver) -> Unit? = null, experimentRunParameters: ExperimentRunParametersIfc? = null, defaultKSLDatabaseObserverOption: Boolean = false): RandomRestartSolver

Creates and configures an R-SPLINE optimization algorithm for a given problem definition that uses a random restart approach.

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@JvmStatic
@JvmOverloads
fun simulatedAnnealingSolver(problemDefinition: ProblemDefinition, modelBuilder: ModelBuilderIfc, startingPoint: MutableMap<String, Double>? = null, initialTemperature: Double = defaultInitialTemperature, maxIterations: Int = defaultMaxNumberIterations, replicationsPerEvaluation: Int = defaultReplicationsPerEvaluation, solutionCache: SolutionCacheIfc = MemorySolutionCache(), simulationRunCache: SimulationRunCacheIfc? = null, printer: (Solver) -> Unit? = null, experimentRunParameters: ExperimentRunParametersIfc? = null, defaultKSLDatabaseObserverOption: Boolean = false): SimulatedAnnealing

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

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@JvmStatic
@JvmOverloads
fun simulatedAnnealingSolverWithRestarts(problemDefinition: ProblemDefinition, modelBuilder: ModelBuilderIfc, maxNumRestarts: Int = defaultMaxRestarts, initialTemperature: Double = defaultInitialTemperature, maxIterations: Int = defaultMaxNumberIterations, replicationsPerEvaluation: Int = defaultReplicationsPerEvaluation, solutionCache: SolutionCacheIfc = MemorySolutionCache(), simulationRunCache: SimulationRunCacheIfc? = null, restartPrinter: (Solver) -> Unit? = null, printer: (Solver) -> Unit? = null, experimentRunParameters: ExperimentRunParametersIfc? = null, defaultKSLDatabaseObserverOption: Boolean = false): RandomRestartSolver

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

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@JvmStatic
@JvmOverloads
fun stochasticHillClimbingSolver(problemDefinition: ProblemDefinition, modelBuilder: ModelBuilderIfc, startingPoint: MutableMap<String, Double>? = null, maxIterations: Int = defaultMaxNumberIterations, replicationsPerEvaluation: Int = defaultReplicationsPerEvaluation, solutionCache: SolutionCacheIfc = MemorySolutionCache(), simulationRunCache: SimulationRunCacheIfc? = null, printer: (Solver) -> Unit? = null, experimentRunParameters: ExperimentRunParametersIfc? = null, defaultKSLDatabaseObserverOption: Boolean = false): 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.

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@JvmStatic
@JvmOverloads
fun stochasticHillClimbingSolverWithRestarts(problemDefinition: ProblemDefinition, modelBuilder: ModelBuilderIfc, maxNumRestarts: Int = defaultMaxRestarts, maxIterations: Int = defaultMaxNumberIterations, replicationsPerEvaluation: Int = defaultReplicationsPerEvaluation, solutionCache: SolutionCacheIfc = MemorySolutionCache(), simulationRunCache: SimulationRunCacheIfc? = null, restartPrinter: (Solver) -> Unit? = null, printer: (Solver) -> Unit? = null, experimentRunParameters: ExperimentRunParametersIfc? = null, defaultKSLDatabaseObserverOption: Boolean = false): RandomRestartSolver

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