SS.Optimization 1.3.0

export function armijoBacktracking(VectorObjective objective, double[] point, double[] gradient, double initialStep, double contraction, double sufficientDecrease, int maximumEvaluations) -> LineSearchResult

export function boundedQuadraticMinimum(double quadratic, double linear, double constant, double lower, double upper) -> ScalarOptimum

export function boxBfgs(VectorObjective objective, double[] initial, double[] lower, double[] upper, double[] scale, double gradientTolerance, double stepTolerance, int maximumIterations)->BoxOptimizationResult

export record BoxOptimizationResult(public double[] point, public double value, public int iterations, public int evaluations, public double projectedGradientNorm, public double[] valueHistory, public string status, public bool converged, public string diagnostic)

export function centralGradient(double[] before, double[] after, double spacing) -> double[]

export function checkObjectiveGradient(VectorObjective objective, double[] point, double relativeStep, double absoluteTolerance, double relativeTolerance)->DerivativeCheck

export function checkResidualJacobian(GeneralResidualModel model, double[] point, double relativeStep, double absoluteTolerance, double relativeTolerance)->DerivativeCheck

export function constrainedAugmentedLagrangian(ConstrainedObjective objective, double[] initial, double[] lower, double[] upper, double[] scale, double feasibilityTolerance, double kktTolerance, int maximumIterations)->ConstrainedOptimizationResult

export interface ConstrainedObjective

export record ConstrainedOptimizationResult(public double[] point, public double value, public int iterations, public int evaluations, public double equalityViolation, public double inequalityViolation, public double kktNorm, public double[] equalityMultipliers, public double[] inequalityMultipliers, public double[] valueHistory, public string status, public bool converged, public string diagnostic)

export function constrainedSqp(ConstrainedObjective objective, double[] initial, double[] lower, double[] upper, double[] scale, double feasibilityTolerance, double kktTolerance, int maximumIterations)->ConstrainedOptimizationResult

export record ConstraintSensitivityResult(public int[] activeConstraintIndices, public double[][] activeConstraintJacobian, public double[] activeMultipliers, public double[] lagrangianGradient, public double stationarityNorm, public double[] objectiveSensitivity, public string diagnostic)

export record DenseStep(public double[] values, public int rank, public bool valid)

export record DerivativeCheck(public bool valid, public int row, public int column, public double maximumAbsoluteError, public double maximumRelativeError, public string diagnostic)

export interface GeneralResidualModel

export function globalGrid2(VectorObjective objective, double[] lower, double[] upper, int divisions)->SampledOptimum

export function goldenSection(double[] sampledPoints, double[] sampledValues, double tolerance) -> ScalarOptimum

export function goldenSectionSearch(ScalarObjective objective, double lower, double upper, double tolerance, int maximumIterations) -> ScalarOptimum

export function gradientOptimize(VectorObjective objective, double[] initial, double initialStep, double gradientTolerance, int maximumIterations) -> OptimizationResult

export function integerGridMinimum(ScalarObjective objective, int lower, int upper)->ScalarOptimum

export record LeastSquares2Result(public double firstParameter, public double secondParameter, public double sumSquaredError, public int iterations, public double conditionEstimate, public double[] valueHistory, public bool converged, public string diagnostic)

export record LeastSquaresResult(public double slope, public double intercept, public double sumSquaredError, public bool converged, public string diagnostic)

export function levenbergMarquardt(GeneralResidualModel model, double[] initial, double[] scale, double gradientTolerance, double stepTolerance, int maximumIterations)->LevenbergMarquardtResult

export record LevenbergMarquardtResult(public double[] point, public double sumSquaredError, public int iterations, public int evaluations, public int acceptedSteps, public int rejectedSteps, public int rank, public double gradientNorm, public double damping, public double[] valueHistory, public string status, public bool converged, public string diagnostic)

export record LineSearchResult(public double step, public double value, public int evaluations, public bool accepted, public string diagnostic)

export function multiObjectiveConstrained(double[][] points, double[][] objectives, bool[] feasible)->MultiObjectiveConstrainedResult

export record MultiObjectiveConstrainedResult(public double[][] points, public double[][] objectives, public int[] candidateIndices, public int feasibleCandidates, public int rejectedCandidates, public string diagnostic)

export function nonlinearConstraintSensitivity(double[] objectiveGradient, double[][] equalityJacobian, double[] equalityMultipliers, double[] inequalityValues, double[][] inequalityJacobian, double[] inequalityMultipliers, double activeTolerance)->ConstraintSensitivityResult

export function nonlinearLeastSquares2(ResidualModel2 model, double firstInitial, double secondInitial, double stepTolerance, int maximumIterations) -> LeastSquares2Result

export function nonlinearLeastSquaresLine(double[] predictors, double[] responses) -> LeastSquaresResult

export function optimizationCheckpoint(string method, double[] point, double[] valueHistory, int iteration, int evaluations)->string

export function optimizationRestart(string text)->OptimizationRestartState

export record OptimizationRestartState(public string method, public double[] point, public double[] valueHistory, public int iteration, public int evaluations, public string schema)

export record OptimizationResult(public double[] point, public double value, public int iterations, public double gradientNorm, public double[] valueHistory, public bool converged, public string diagnostic)

export function optimizerDiagnosticSummary(bool converged, double feasibilityViolation, double stationarityNorm, double objectiveImprovement, bool unboundedEvidence, bool iterationLimitReached, double feasibilityTolerance, double stationarityTolerance)->OptimizerDiagnosticSummary

export record OptimizerDiagnosticSummary(public string status, public bool converged, public bool infeasible, public bool unbounded, public bool stationary, public bool iterationLimited, public string diagnostic)

export function paretoFront(double[][] points, double[] firstObjective, double[] secondObjective)->ParetoFront

export record ParetoFront(public ParetoPoint[] points, public int candidates)

export record ParetoPoint(public double[] point, public double firstObjective, public double secondObjective)

export function patternSearch(VectorObjective objective, double[] initial, double initialStep, double stepTolerance, int maximumIterations) -> OptimizationResult

export function projectedGradientOptimize(VectorObjective objective, double[] initial, double[] lower, double[] upper, double initialStep, double gradientTolerance, int maximumIterations) -> OptimizationResult

export function projectedGradientStep(double[] point, double[] gradient, double step, double[] lower, double[] upper) -> double[]

export interface ResidualModel2

export function robustGridMinimum(ScenarioObjective objective, double lower, double upper, double[] scenarios, int divisions)->ScalarOptimum

export record SampledOptimum(public double[] point, public double value, public int evaluations, public string method)

export interface ScalarObjective

export record ScalarOptimum(public double point, public double value, public int iterations, public bool converged, public string diagnostic)

export interface ScenarioObjective

export interface VectorObjective