Uses of Class
agi.foundation.numericalmethods.OptimizerMultivariableFunctionResults

Packages that use OptimizerMultivariableFunctionResults

Package	Description
agi.foundation.numericalmethods	Contains general numerical algorithms.

Uses of OptimizerMultivariableFunctionResults in agi.foundation.numericalmethods

Subclasses of OptimizerMultivariableFunctionResults in agi.foundation.numericalmethods

Modifier and Type	Class and Description
class	TargetedSegmentListOptimizerFunctionResults The results that get returned when a TargetedSegmentListOptimizerFunction is run.

Methods in agi.foundation.numericalmethods that return OptimizerMultivariableFunctionResults

Modifier and Type	Method and Description
OptimizerMultivariableFunctionResults	TargetedSegmentListOptimizerFunction.evaluate(double[] variables, ITrackCalculationProgress progressTracker) Evaluates the function.
abstract OptimizerMultivariableFunctionResults	OptimizerMultivariableFunction.evaluate(double[] variables, ITrackCalculationProgress progressTracker) Evaluates the function.
OptimizerMultivariableFunctionResults	ParameterOptimizerIterationResults.getFunctionResult() Gets the results from the Function (get / set).
OptimizerMultivariableFunctionResults[]	NumericallyComputedOptimizerFunctionDerivativeResults.getFunctionResultsUsedToComputeDerivative() Gets the results of the function that were computed at the perturbed values of the variables.
OptimizerMultivariableFunctionResults	ParameterOptimizerIterationResults.getPerturbedResults(int index) Gets the results of the Function (get / set) when the variable with the same index in the ParameterOptimizer was perturbed for this iteration.
OptimizerMultivariableFunctionResults	OptimizerFunctionEvaluatedEventArgs.getResults() Gets the OptimizerMultivariableFunctionResults from the ParameterOptimizer that raised the event.
OptimizerMultivariableFunctionResults	NumericallyComputedOptimizerFunctionDerivativeResults.getValue() Gets the results of the function at the nominal values of the variables.

Methods in agi.foundation.numericalmethods that return types with arguments of type OptimizerMultivariableFunctionResults

Modifier and Type	Method and Description
abstract MultivariableFunctionSolverStepResult<OptimizerMultivariableFunctionResults,OptimizerMultivariableFunctionDerivativeResults>	SequentialQuadraticProgrammingOptimizer.computeNextStep(double[] variableValues, Matrix hessian, ParameterOptimizerIterationResults previousIterationResults, ITrackCalculationProgress progressTracker) Computes the next optimization step that this parameter optimizer should take.
MultivariableFunctionSolverStepResult<OptimizerMultivariableFunctionResults,OptimizerMultivariableFunctionDerivativeResults>	ActiveSetSequentialQuadraticProgrammingOptimizer.computeNextStep(double[] variableValues, Matrix hessian, ParameterOptimizerIterationResults previousIterationResults, ITrackCalculationProgress progressTracker) Computes the next optimization step that this parameter optimizer should take.
MultivariableFunctionEvaluationAndDerivativeResults<OptimizerMultivariableFunctionResults,OptimizerMultivariableFunctionDerivativeResults>	OptimizerMultivariableFunction.evaluate(double[] variables, int order, boolean multithreaded, ITrackCalculationProgress progressTracker) Evaluates the function and the gradients.

Methods in agi.foundation.numericalmethods with parameters of type OptimizerMultivariableFunctionResults

Modifier and Type	Method and Description
void	TargetedSegmentListOptimizerFunction.applyResults(OptimizerMultivariableFunctionResults results) For OptimizerMultivariableFunctions that have state, there may be times when that state should be manually set on a function (sometimes for performance reasons, when the function will be called multiple times and it should start from where it left off).
void	OptimizerMultivariableFunction.applyResults(OptimizerMultivariableFunctionResults results) For OptimizerMultivariableFunctions that have state, there may be times when that state should be manually set on a function (sometimes for performance reasons, when the function will be called multiple times and it should start from where it left off).
static NumericallyComputedOptimizerFunctionDerivativeResults	OptimizerMultivariableFunction.computeGradientsNumerically(OptimizerMultivariableFunction function, double[] variables, double[] perturbationValues, FiniteDifferenceMethod differenceMethod, boolean multithreaded, OptimizerMultivariableFunctionResults precomputedValueResults, ITrackCalculationProgress progressTracker) Computes the gradients of an OptimizerMultivariableFunction numerically.
abstract boolean	SequentialQuadraticProgrammingOptimizer.convergenceCheck(OptimizerMultivariableFunctionResults currentResults, OptimizerMultivariableFunctionResults previousResults) Determines whether the optimizer converged or not.
boolean	ActiveSetSequentialQuadraticProgrammingOptimizer.convergenceCheck(OptimizerMultivariableFunctionResults currentResults, OptimizerMultivariableFunctionResults previousResults) Determines whether the optimizer converged or not.
OptimizerMultivariableFunctionDerivativeResults	OptimizerMultivariableFunction.evaluateDerivative(double[] variables, boolean multithreaded, OptimizerMultivariableFunctionResults valueResults, ITrackCalculationProgress progressTracker) Evaluates the gradients of this function.
abstract boolean	ActiveSetSequentialQuadraticProgrammingOptimizer.ConvergenceChecker.invoke(ActiveSetSequentialQuadraticProgrammingOptimizer optimizer, OptimizerMultivariableFunctionResults currentResults, OptimizerMultivariableFunctionResults previousResults) A function that tests the convergence of the optimizer by comparing the results of the current iteration with the results of the previous iteration.
boolean	ActiveSetSequentialQuadraticProgrammingOptimizer.ConvergenceChecker.Function.invoke(ActiveSetSequentialQuadraticProgrammingOptimizer optimizer, OptimizerMultivariableFunctionResults currentResults, OptimizerMultivariableFunctionResults previousResults) A function that tests the convergence of the optimizer by comparing the results of the current iteration with the results of the previous iteration.
static void	ActiveSetSequentialQuadraticProgrammingOptimizer.solveLagrangianDerivativeEquation(OptimizerMultivariableFunctionResults unperturbedAnswer, OptimizerMultivariableFunctionDerivativeResults derivativeResults, ArrayList<InequalityConstraintSettings> activeInequalitySet, ArrayList<Double> activeInequalityErrors, ArrayList<double[]> activeInequalityGradients, List<SolverConstraintSettings> equalitySet, double[] equalityErrors, ArrayList<Double>[] lagrangeMultipliers, double[][] lagrangianDerivatives) Solves for the Lagrange multipliers and derivatives of the Lagrangian using the cost function, equality errors, active inequality errors, and the gradients of each with respect to the variables.

Constructors in agi.foundation.numericalmethods with parameters of type OptimizerMultivariableFunctionResults

Constructor and Description
NumericallyComputedOptimizerFunctionDerivativeResults(OptimizerMultivariableFunctionResults value, double[] costFunctionGradient, double[][] equalityConstraintGradients, double[][] inequalityConstraintGradients, OptimizerMultivariableFunctionResults[] perturbedResultsUsedToComputeDerivative, FiniteDifferenceMethod differenceMethod) Initializes a new instance.
NumericallyComputedOptimizerFunctionDerivativeResults(OptimizerMultivariableFunctionResults value, double[] costFunctionGradient, double[][] equalityConstraintGradients, double[][] inequalityConstraintGradients, OptimizerMultivariableFunctionResults[] perturbedResultsUsedToComputeDerivative, FiniteDifferenceMethod differenceMethod) Initializes a new instance.
OptimizerFunctionEvaluatedEventArgs(OptimizerMultivariableFunctionResults result) Initializes a new instance.
OptimizerMultivariableFunctionResults(OptimizerMultivariableFunctionResults existingInstance, CopyContext context) Initializes a new instance as a copy of an existing instance.
ParameterOptimizerIterationResults(OptimizerMultivariableFunctionResults functionResult, OptimizerMultivariableFunctionDerivativeResults derivativeResults, int iteration, SolverVariableSettings[] variableSettings, CostFunctionSettings costFunctionSettings, SolverConstraintSettings[] equalitySettings, InequalityConstraintSettings[] inequalitySettings) Initializes a new instance.