Uses of Class
org.drip.function.definition.RdToR1

Packages that use RdToR1

Package	Description
org.drip.function.definition
org.drip.function.rdtor1
org.drip.function.rdtor1descent
org.drip.function.rdtor1solver
org.drip.learning.kernel
org.drip.learning.regularization
org.drip.learning.rxtor1
org.drip.learning.svm
org.drip.measure.continuousjoint
org.drip.optimization.constrained
org.drip.portfolioconstruction.allocator
org.drip.sequence.custom
org.drip.sequence.functional
org.drip.spaces.functionclass
org.drip.spaces.metric
org.drip.spaces.rxtor1

Uses of RdToR1 in org.drip.function.definition

Methods in org.drip.function.definition that return RdToR1

Modifier and Type	Method and Description
RdToR1	RdToR1.gradientModulusFunction() Generate the Gradient Modulus Function

Uses of RdToR1 in org.drip.function.rdtor1

Subclasses of RdToR1 in org.drip.function.rdtor1

Modifier and Type	Class and Description
class	AffineBoundMultivariate AffineBoundMultivariate implements a Bounded Planar Linear R^d To R^1 Function.
class	AffineMultivariate AffineMultivariate implements a Planar Linear R^d To R^1 Function using a Multivariate Vector.
class	CovarianceEllipsoidMultivariate CovarianceEllipsoidMultivariate implements an R^d To R^1 Co-variance Estimate of the specified Distribution.
class	LagrangianMultivariate LagrangianMultivariate implements an R^d To R^1 Multivariate Function along with the specified Set of Equality Constraints.
class	RiskObjectiveUtilityMultivariate RiskObjectiveUtilityMultivariate implements the Risk Objective R^d To R^1 Multivariate Function used in Portfolio Allocation.

Methods in org.drip.function.rdtor1 that return RdToR1

Modifier and Type	Method and Description
RdToR1[]	LagrangianMultivariate.constraintFunctions() Retrieve the Array of the Constraint R^d To R^1 Function Instances
RdToR1	LagrangianMultivariate.objectiveFunction() Retrieve the Objective R^d To R^1 Function Instance

Constructors in org.drip.function.rdtor1 with parameters of type RdToR1

Constructor and Description
LagrangianMultivariate(RdToR1 RdToR1Objective, RdToR1[] aRdToR1EqualityConstraint) LagrangianMultivariate Constructor
LagrangianMultivariate(RdToR1 RdToR1Objective, RdToR1[] aRdToR1EqualityConstraint) LagrangianMultivariate Constructor

Uses of RdToR1 in org.drip.function.rdtor1descent

Methods in org.drip.function.rdtor1descent with parameters of type RdToR1

Modifier and Type	Method and Description
LineEvolutionVerifierMetrics	WolfeEvolutionVerifier.metrics(UnitVector uvTargetDirection, double[] adblCurrentVariate, RdToR1 funcRdToR1, double dblStepLength)
abstract LineEvolutionVerifierMetrics	LineEvolutionVerifier.metrics(UnitVector uvTargetDirection, double[] adblCurrentVariate, RdToR1 funcRdToR1, double dblStepLength) Generate the Verifier Metrics for the Specified Inputs
LineEvolutionVerifierMetrics	CurvatureEvolutionVerifier.metrics(UnitVector uvTargetDirection, double[] adblCurrentVariate, RdToR1 funcRdToR1, double dblStepLength)
LineEvolutionVerifierMetrics	ArmijoEvolutionVerifier.metrics(UnitVector uvTargetDirection, double[] adblCurrentVariate, RdToR1 funcRdToR1, double dblStepLength)
boolean	LineEvolutionVerifier.verify(UnitVector uvTargetDirection, double[] adblCurrentVariate, RdToR1 funcRdToR1, double dblStepLength) Verify if the specified Inputs satisfy the Criterion

Uses of RdToR1 in org.drip.function.rdtor1solver

Methods in org.drip.function.rdtor1solver that return RdToR1

Modifier and Type	Method and Description
RdToR1[]	InteriorFixedPointFinder.inequalityConstraints() Retrieve the Array of Inequality Constraints
RdToR1[]	BarrierFixedPointFinder.inequalityConstraints() Retrieve the Array of Inequality Constraints
RdToR1	FixedRdFinder.objectiveFunction() Retrieve the Objective Function
RdToR1	BarrierFixedPointFinder.objectiveFunction() Retrieve the Objective Function

Constructors in org.drip.function.rdtor1solver with parameters of type RdToR1

Constructor and Description
BarrierFixedPointFinder(RdToR1 rdToR1ObjectiveFunction, RdToR1[] aRdToR1InequalityConstraint, InteriorPointBarrierControl ipbc, LineStepEvolutionControl lsec) BarrierFixedPointFinder Constructor
BarrierFixedPointFinder(RdToR1 rdToR1ObjectiveFunction, RdToR1[] aRdToR1InequalityConstraint, InteriorPointBarrierControl ipbc, LineStepEvolutionControl lsec) BarrierFixedPointFinder Constructor
InteriorFixedPointFinder(RdToR1 rdToR1ObjectiveFunction, RdToR1[] aRdToR1InequalityConstraint, LineStepEvolutionControl lsec, ConvergenceControl cc, double dblBarrierStrength) InteriorFixedPointFinder Constructor
InteriorFixedPointFinder(RdToR1 rdToR1ObjectiveFunction, RdToR1[] aRdToR1InequalityConstraint, LineStepEvolutionControl lsec, ConvergenceControl cc, double dblBarrierStrength) InteriorFixedPointFinder Constructor
NewtonFixedPointFinder(RdToR1 rdToR1ObjectiveFunction, LineStepEvolutionControl lsec, ConvergenceControl cc) NewtonFixedPointFinder Constructor

Uses of RdToR1 in org.drip.learning.kernel

Methods in org.drip.learning.kernel that return RdToR1

Modifier and Type	Method and Description
RdToR1	IntegralOperator.kernelOperatorFunction() Retrieve the R^d To R^1 Kernel Operator Function

Constructors in org.drip.learning.kernel with parameters of type RdToR1

Constructor and Description
IntegralOperator(SymmetricRdToNormedR1Kernel kernel, RdToR1 funcRdToR1, R1Normed r1OperatorOutput) IntegralOperator Constructor

Uses of RdToR1 in org.drip.learning.regularization

Methods in org.drip.learning.regularization that return RdToR1

Modifier and Type	Method and Description
RdToR1	RegularizationFunction.rdTor1() Retrieve the R^d To R^1 Regularization Function

Methods in org.drip.learning.regularization with parameters of type RdToR1

Modifier and Type	Method and Description
static RegularizerRdToR1	RegularizerBuilder.RdCombinatorialToR1Continuous(RdToR1 funcRegularizerRdToR1, NormedRdCombinatorialToR1Continuous funcSpaceRdToR1, double dblLambda) Construct an Instance of R^d Combinatorial To R^1 Continuous Regularizer
static RegularizerRdToR1	RegularizerBuilder.RdContinuousToR1Continuous(RdToR1 funcRegularizerRdToR1, NormedRdContinuousToR1Continuous funcSpaceRdToR1, double dblLambda) Construct an Instance of R^d Continuous To R^1 Continuous Regularizer
double	RegularizerRdToR1.structuralLoss(RdToR1 funcRdToR1, double[][] aadblX) Compute the Regularization Sample Structural Loss
double	RegularizerRdContinuousToR1Continuous.structuralLoss(RdToR1 funcRdToR1, double[][] aadblX)
double	RegularizerRdCombinatorialToR1Continuous.structuralLoss(RdToR1 funcRdToR1, double[][] aadblX)
double	RegularizerRdToR1.structuralRisk(RdR1 distRdR1, RdToR1 funcRdToR1, double[][] aadblX, double[] adblY) Compute the Regularization Sample Structural Loss
double	RegularizerRdContinuousToR1Continuous.structuralRisk(RdR1 distRdR1, RdToR1 funcRdToR1, double[][] aadblX, double[] adblY)
double	RegularizerRdCombinatorialToR1Continuous.structuralRisk(RdR1 distRdR1, RdToR1 funcRdToR1, double[][] aadblX, double[] adblY)
static RegularizerRdToR1	RegularizerBuilder.ToRdContinuous(RdToR1 funcRegularizerRdToR1, RdNormed rdInput, R1Continuous r1ContinuousOutput, double dblLambda) Construct an Instance of R^d Combinatorial/Continuous To R^1 Continuous Regularizer

Constructors in org.drip.learning.regularization with parameters of type RdToR1

Constructor and Description
RegularizationFunction(R1ToR1 regR1ToR1, RdToR1 regRdToR1, double dblLambda) RegularizationFunction Constructor
RegularizerRdCombinatorialToR1Continuous(RdToR1 funcRegularizerRdToR1, RdCombinatorialBanach rdCombinatorialInput, R1Continuous r1ContinuousOutput, double dblLambda) RegularizerRdCombinatorialToR1Continuous Function Space Constructor
RegularizerRdContinuousToR1Continuous(RdToR1 funcRdToR1, RdContinuousBanach rdContinuousInput, R1Continuous r1ContinuousOutput, double dblLambda) RegularizerRdContinuousToR1Continuous Function Space Constructor

Uses of RdToR1 in org.drip.learning.rxtor1

Subclasses of RdToR1 in org.drip.learning.rxtor1

Modifier and Type	Class and Description
class	EmpiricalPenaltySupremumEstimator EmpiricalPenaltySupremumEstimator contains the Implementation of the Empirical Penalty Supremum Estimator dependent on Multivariate Random Variables where the Multivariate Function is a Linear Combination of Bounded Univariate Functions acting on each Random Variate.

Methods in org.drip.learning.rxtor1 that return RdToR1

Modifier and Type	Method and Description
RdToR1	EmpiricalPenaltySupremumEstimator.supremumRdToR1(double[][] aadblX) Retrieve the Supremum R^d To R^1 Function Instance for the specified Variate Sequence

Methods in org.drip.learning.rxtor1 with parameters of type RdToR1

Modifier and Type	Method and Description
double	LpLossLearner.empiricalLoss(RdToR1 funcLearnerRdToR1, GeneralizedValidatedVector gvviX, GeneralizedValidatedVector gvviY)
double	LipschitzLossLearner.empiricalLoss(RdToR1 funcLearnerRdToR1, GeneralizedValidatedVector gvviX, GeneralizedValidatedVector gvviY)
double	L1LossLearner.empiricalLoss(RdToR1 funcLearnerRdToR1, GeneralizedValidatedVector gvviX, GeneralizedValidatedVector gvviY)
double	EmpiricalLearningMetricEstimator.empiricalLoss(RdToR1 funcLearnerRdToR1, GeneralizedValidatedVector gvviX, GeneralizedValidatedVector gvviY) Compute the Empirical Sample Loss
double	LpLossLearner.empiricalRisk(RdR1 distRdR1, RdToR1 funcLearnerRdToR1, GeneralizedValidatedVector gvviX, GeneralizedValidatedVector gvviY)
double	LipschitzLossLearner.empiricalRisk(RdR1 distRdR1, RdToR1 funcLearnerRdToR1, GeneralizedValidatedVector gvviX, GeneralizedValidatedVector gvviY)
double	L1LossLearner.empiricalRisk(RdR1 distRdR1, RdToR1 funcLearnerRdToR1, GeneralizedValidatedVector gvviX, GeneralizedValidatedVector gvviY)
double	EmpiricalLearningMetricEstimator.empiricalRisk(RdR1 distRdR1, RdToR1 funcLearnerRdToR1, GeneralizedValidatedVector gvviX, GeneralizedValidatedVector gvviY) Compute the Empirical Sample Risk
double	GeneralizedLearner.regularizedLoss(RdToR1 funcLearnerRdToR1, GeneralizedValidatedVector gvviX, GeneralizedValidatedVector gvviY)
double	EmpiricalLearningMetricEstimator.regularizedLoss(RdToR1 funcLearnerRdToR1, GeneralizedValidatedVector gvviX, GeneralizedValidatedVector gvviY) Compute the Regularized Sample Loss (Empirical + Structural)
double	GeneralizedLearner.regularizedRisk(RdR1 distRdR1, RdToR1 funcLearnerRdToR1, GeneralizedValidatedVector gvviX, GeneralizedValidatedVector gvviY)
double	EmpiricalLearningMetricEstimator.regularizedRisk(RdR1 distRdR1, RdToR1 funcLearnerRdToR1, GeneralizedValidatedVector gvviX, GeneralizedValidatedVector gvviY) Compute the Regularized Sample Risk (Empirical + Structural)
double	GeneralizedLearner.structuralLoss(RdToR1 funcLearnerRdToR1, GeneralizedValidatedVector gvvi)
double	EmpiricalLearningMetricEstimator.structuralLoss(RdToR1 funcLearnerRdToR1, GeneralizedValidatedVector gvvi) Compute the Structural Sample Loss
double	GeneralizedLearner.structuralRisk(RdR1 distRdR1, RdToR1 funcLearnerRdToR1, GeneralizedValidatedVector gvviX, GeneralizedValidatedVector gvviY)
double	EmpiricalLearningMetricEstimator.structuralRisk(RdR1 distRdR1, RdToR1 funcLearnerRdToR1, GeneralizedValidatedVector gvviX, GeneralizedValidatedVector gvviY) Compute the Structural Sample Risk

Uses of RdToR1 in org.drip.learning.svm

Subclasses of RdToR1 in org.drip.learning.svm

Modifier and Type	Class and Description
class	KernelRdDecisionFunction KernelRdDecisionFunction implements the Kernel-based R^d Decision Function-Based SVM Functionality for Classification and Regression.
class	LinearRdDecisionFunction LinearRdDecisionFunction implements the Linear R^d Decision Function-Based SVM Functionality for Classification and Regression.
class	RdDecisionFunction RdDecisionFunction exposes the R^d Decision-Function Based SVM Functionality for Classification and Regression.

Uses of RdToR1 in org.drip.measure.continuousjoint

Methods in org.drip.measure.continuousjoint that return RdToR1

Modifier and Type	Method and Description
RdToR1	R1Multivariate.densityRdToR1() Convert the Multivariate Density into an RdToR1 Functions Instance

Methods in org.drip.measure.continuousjoint with parameters of type RdToR1

Modifier and Type	Method and Description
double	R1Multivariate.expectation(RdToR1 funcRdToR1) Compute the Expectation of the Specified R^d To R^1 Function Instance

Uses of RdToR1 in org.drip.optimization.constrained

Subclasses of RdToR1 in org.drip.optimization.constrained

Modifier and Type	Class and Description
class	OptimizationFramework OptimizationFramework holds the Non Linear Objective Function and the Collection of Equality and the Inequality Constraints that correspond to the Optimization Setup.

Methods in org.drip.optimization.constrained that return RdToR1

Modifier and Type	Method and Description
RdToR1[]	OptimizationFramework.activeConstraints(double[] adblVariate) Retrieve the Array of Active Constraints
RdToR1[]	OptimizationFramework.equalityConstraint() Retrieve the Array of R^d To R^1 Equality Constraint Functions
RdToR1[]	OptimizationFramework.inequalityConstraint() Retrieve the Array of R^d To R^1 Inequality Constraint Functions
RdToR1	OptimizationFramework.objectiveFunction() Retrieve the R^d To R^1 Objective Function

Constructors in org.drip.optimization.constrained with parameters of type RdToR1

Constructor and Description
OptimizationFramework(RdToR1 rdToR1Objective, RdToR1[] aRdToR1EqualityConstraint, RdToR1[] aRdToR1InequalityConstraint) OptimizationFramework Constructor
OptimizationFramework(RdToR1 rdToR1Objective, RdToR1[] aRdToR1EqualityConstraint, RdToR1[] aRdToR1InequalityConstraint) OptimizationFramework Constructor
OptimizationFramework(RdToR1 rdToR1Objective, RdToR1[] aRdToR1EqualityConstraint, RdToR1[] aRdToR1InequalityConstraint) OptimizationFramework Constructor

Uses of RdToR1 in org.drip.portfolioconstruction.allocator

Methods in org.drip.portfolioconstruction.allocator that return RdToR1

Modifier and Type	Method and Description
RdToR1[]	PortfolioConstructionParameters.equalityConstraintRdToR1(AssetUniverseStatisticalProperties ausp) Retrieve the Equality Constraint R^d To R^1 Corresponding to the Specified Constraint Type
RdToR1	PortfolioConstructionParameters.fullyInvestedConstraint() Retrieve the Fully Invested Equality Constraint
RdToR1	PortfolioConstructionParameters.returnsConstraint(AssetUniverseStatisticalProperties ausp) Retrieve the Mandatory Returns Constraint
RdToR1	CustomRiskUtilitySettings.riskObjectiveUtility(java.lang.String[] astrAssetID, AssetUniverseStatisticalProperties ausp) Retrieve the Custom Risk Objective Utility Multivariate

Uses of RdToR1 in org.drip.sequence.custom

Subclasses of RdToR1 in org.drip.sequence.custom

Modifier and Type	Class and Description
class	GlivenkoCantelliFunctionSupremum GlivenkoCantelliFunctionSupremum contains the Implementation of the Supremum Class Objective Function dependent on Multivariate Random Variables where the Multivariate Function is a Linear Combination of Bounded Univariate Functions acting on each Random Variate.
class	GlivenkoCantelliUniformDeviation GlivenkoCantelliUniformDeviation contains the Implementation of the Bounded Objective Function dependent on Multivariate Random Variables where the Multivariate Function is a Linear Combination of Bounded Univariate Functions acting on each Random Variate.
class	KernelDensityEstimationL1 KernelDensityEstimationL1 implements the L1 Error Scheme Estimation for a Multivariate Kernel Density Estimator with Focus on establishing targeted Variate-Specific and Agnostic Bounds.
class	LongestCommonSubsequence LongestCommonSubsequence contains Variance Bounds on the Critical Measures of the Longest Common Subsequence between two Strings.
class	OrientedPercolationFirstPassage OrientedPercolationFirstPassage contains Variance Bounds on the Critical Measures of the Standard Problem of First Passage Time in Oriented Percolation.

Uses of RdToR1 in org.drip.sequence.functional

Subclasses of RdToR1 in org.drip.sequence.functional

Modifier and Type	Class and Description
class	BoundedMultivariateRandom BoundedMultivariateRandom contains the Implementation of the Bounded Objective Function dependent on Multivariate Random Variables.
class	FlatMultivariateRandom FlatMultivariateRandom contains the Implementation of the Flat Objective Function dependent on Multivariate Random Variables.
class	MultivariateRandom MultivariateRandom contains the implementation of the objective Function dependent on Multivariate Random Variables.

Methods in org.drip.sequence.functional that return RdToR1

Modifier and Type	Method and Description
RdToR1	EfronSteinMetrics.function() Retrieve the Multivariate Objective Function

Uses of RdToR1 in org.drip.spaces.functionclass

Methods in org.drip.spaces.functionclass that return RdToR1

Modifier and Type	Method and Description
RdToR1[]	NormedRdToNormedR1Finite.functionRdToR1Set() Retrieve the Finite Class of R^d To R^1 Functions

Uses of RdToR1 in org.drip.spaces.metric

Methods in org.drip.spaces.metric with parameters of type RdToR1

Modifier and Type	Method and Description
double	RdNormed.borelMeasureSpaceExpectation(RdToR1 funcRdToR1) Compute the Borel Measure Expectation for the specified R^d To R^1 Function over the full Input Space
double	RdContinuousBanach.borelMeasureSpaceExpectation(RdToR1 funcRdToR1)
double	RdCombinatorialBanach.borelMeasureSpaceExpectation(RdToR1 funcRdToR1)

Uses of RdToR1 in org.drip.spaces.rxtor1

Methods in org.drip.spaces.rxtor1 that return RdToR1

Modifier and Type	Method and Description
RdToR1	NormedRdToNormedR1.function() Retrieve the Underlying RdToR1 Function

Constructors in org.drip.spaces.rxtor1 with parameters of type RdToR1

Constructor and Description
NormedRdCombinatorialToR1Continuous(RdCombinatorialBanach rdCombinatorialInput, R1Continuous r1ContinuousOutput, RdToR1 funcRdToR1) NormedRdCombinatorialToR1Continuous Function Space Constructor
NormedRdContinuousToR1Continuous(RdContinuousBanach rdContinuousInput, R1Continuous r1ContinuousOutput, RdToR1 funcRdToR1) NormedRdContinuousToR1Continuous Function Space Constructor