Index (Hipparchus::Optimization 3.1 API)

A B C D E F G H I J K L M N O P Q R S T U V W
All Classes and Interfaces|All Packages|Constant Field Values|Serialized Form

A

AbstractConvergenceChecker - Class in org.hipparchus.optim: Base class for all convergence checker implementations.
AbstractConvergenceChecker(double, double) - Constructor for class org.hipparchus.optim.AbstractConvergenceChecker: Build an instance with a specified thresholds.
AbstractEvaluation - Class in org.hipparchus.optim.nonlinear.vector.leastsquares: An implementation of LeastSquaresProblem.Evaluation that is designed for extension.
AbstractEvaluation(int) - Constructor for class org.hipparchus.optim.nonlinear.vector.leastsquares.AbstractEvaluation: Constructor.
AbstractOptimizationProblem - Class in org.hipparchus.optim: Base class for implementing optimization problems.
AbstractOptimizationProblem(int, int, ConvergenceChecker) - Constructor for class org.hipparchus.optim.AbstractOptimizationProblem: Create an AbstractOptimizationProblem from the given data.
AbstractSimplex - Class in org.hipparchus.optim.nonlinear.scalar.noderiv: This class implements the simplex concept.
AbstractSimplex(double[]) - Constructor for class org.hipparchus.optim.nonlinear.scalar.noderiv.AbstractSimplex: The start configuration for simplex is built from a box parallel to the canonical axes of the space.
AbstractSimplex(double[][]) - Constructor for class org.hipparchus.optim.nonlinear.scalar.noderiv.AbstractSimplex: The real initial simplex will be set up by moving the reference simplex such that its first point is located at the start point of the optimization.
AbstractSimplex(int) - Constructor for class org.hipparchus.optim.nonlinear.scalar.noderiv.AbstractSimplex: Build a unit hypercube simplex.
AbstractSimplex(int, double) - Constructor for class org.hipparchus.optim.nonlinear.scalar.noderiv.AbstractSimplex: Build a hypercube simplex with the given side length.
AbstractSQPOptimizer - Class in org.hipparchus.optim.nonlinear.vector.constrained: Abstract class for Sequential Quadratic Programming solvers
AbstractSQPOptimizer() - Constructor for class org.hipparchus.optim.nonlinear.vector.constrained.AbstractSQPOptimizer: Simple constructor.
ADMMQPConvergenceChecker - Class in org.hipparchus.optim.nonlinear.vector.constrained: Convergence Checker for ADMM QP Optimizer.
ADMMQPKKT - Class in org.hipparchus.optim.nonlinear.vector.constrained: Alternative Direction Method of Multipliers Solver.
ADMMQPModifiedRuizEquilibrium - Class in org.hipparchus.optim.nonlinear.vector.constrained: TBD.
ADMMQPModifiedRuizEquilibrium(RealMatrix, RealMatrix, RealVector) - Constructor for class org.hipparchus.optim.nonlinear.vector.constrained.ADMMQPModifiedRuizEquilibrium: Simple constructor
ADMMQPOptimizer - Class in org.hipparchus.optim.nonlinear.vector.constrained: Alternating Direction Method of Multipliers Quadratic Programming Optimizer.
ADMMQPOptimizer() - Constructor for class org.hipparchus.optim.nonlinear.vector.constrained.ADMMQPOptimizer: Simple constructor.
ADMMQPOption - Class in org.hipparchus.optim.nonlinear.vector.constrained: Container for ADMMQPOptimizer settings.
ADMMQPOption() - Constructor for class org.hipparchus.optim.nonlinear.vector.constrained.ADMMQPOption: Simple constructor.
ADMMQPSolution - Class in org.hipparchus.optim.nonlinear.vector.constrained: Internal Solution for ADMM QP Optimizer.
ADMMQPSolution(RealVector, RealVector) - Constructor for class org.hipparchus.optim.nonlinear.vector.constrained.ADMMQPSolution: Simple constructor.
ADMMQPSolution(RealVector, RealVector, Double) - Constructor for class org.hipparchus.optim.nonlinear.vector.constrained.ADMMQPSolution: Simple constructor.
ADMMQPSolution(RealVector, RealVector, RealVector, RealVector) - Constructor for class org.hipparchus.optim.nonlinear.vector.constrained.ADMMQPSolution: Simple constructor.
ADMMQPSolution(RealVector, RealVector, RealVector, RealVector, double) - Constructor for class org.hipparchus.optim.nonlinear.vector.constrained.ADMMQPSolution: Simple constructor.

B

BaseMultiStartMultivariateOptimizer - Class in org.hipparchus.optim: Base class multi-start optimizer for a multivariate function.
BaseMultiStartMultivariateOptimizer(BaseMultivariateOptimizer, int, RandomVectorGenerator) - Constructor for class org.hipparchus.optim.BaseMultiStartMultivariateOptimizer: Create a multi-start optimizer from a single-start optimizer.
BaseMultivariateOptimizer - Class in org.hipparchus.optim: Base class for implementing optimizers for multivariate functions.
BaseMultivariateOptimizer(ConvergenceChecker) - Constructor for class org.hipparchus.optim.BaseMultivariateOptimizer: Simple constructor.
BaseOptimizer - Class in org.hipparchus.optim: Base class for implementing optimizers.
BaseOptimizer(ConvergenceChecker) - Constructor for class org.hipparchus.optim.BaseOptimizer: Simple constructor.
BaseOptimizer(ConvergenceChecker, int, int) - Constructor for class org.hipparchus.optim.BaseOptimizer: Simple constructor.
BLAND - Enum constant in enum org.hipparchus.optim.linear.PivotSelectionRule: The first variable with a negative coefficient in the objective function row will be chosen as entering variable.
BOBYQAOptimizer - Class in org.hipparchus.optim.nonlinear.scalar.noderiv: Powell's BOBYQA algorithm.
BOBYQAOptimizer(int) - Constructor for class org.hipparchus.optim.nonlinear.scalar.noderiv.BOBYQAOptimizer: Simple constructor.
BOBYQAOptimizer(int, double, double) - Constructor for class org.hipparchus.optim.nonlinear.scalar.noderiv.BOBYQAOptimizer: Simple constructor.
BoundedConstraint - Class in org.hipparchus.optim.nonlinear.vector.constrained: Constraint with lower and upper bounds: \(l \le f(x) \le u\).
BoundedConstraint(RealVector, RealVector) - Constructor for class org.hipparchus.optim.nonlinear.vector.constrained.BoundedConstraint: Simple constructor.
boundedToUnbounded(double[]) - Method in class org.hipparchus.optim.nonlinear.scalar.MultivariateFunctionMappingAdapter: Maps an array from bounded to unbounded.
BracketFinder - Class in org.hipparchus.optim.univariate: Provide an interval that brackets a local optimum of a function.
BracketFinder() - Constructor for class org.hipparchus.optim.univariate.BracketFinder: Constructor with default values 100, 500 (see the other constructor).
BracketFinder(double, int) - Constructor for class org.hipparchus.optim.univariate.BracketFinder: Create a bracketing interval finder.
BrentOptimizer - Class in org.hipparchus.optim.univariate: For a function defined on some interval (lo, hi), this class finds an approximation x to the point at which the function attains its minimum.
BrentOptimizer(double, double) - Constructor for class org.hipparchus.optim.univariate.BrentOptimizer: The arguments are used for implementing the original stopping criterion of Brent's algorithm.
BrentOptimizer(double, double, ConvergenceChecker<UnivariatePointValuePair>) - Constructor for class org.hipparchus.optim.univariate.BrentOptimizer: The arguments are used implement the original stopping criterion of Brent's algorithm.
build() - Method in class org.hipparchus.optim.nonlinear.vector.leastsquares.LeastSquaresBuilder: Construct a LeastSquaresProblem from the data in this builder.
build(double[]) - Method in class org.hipparchus.optim.nonlinear.scalar.noderiv.AbstractSimplex: Build an initial simplex.

C

checker(ConvergenceChecker<LeastSquaresProblem.Evaluation>) - Method in class org.hipparchus.optim.nonlinear.vector.leastsquares.LeastSquaresBuilder: Configure the convergence checker.
checkerPair(ConvergenceChecker<PointVectorValuePair>) - Method in class org.hipparchus.optim.nonlinear.vector.leastsquares.LeastSquaresBuilder: Configure the convergence checker.
clear() - Method in class org.hipparchus.optim.BaseMultiStartMultivariateOptimizer: Method that will called in order to clear all stored optima.
clear() - Method in class org.hipparchus.optim.nonlinear.scalar.MultiStartMultivariateOptimizer: Method that will called in order to clear all stored optima.
CMAESOptimizer - Class in org.hipparchus.optim.nonlinear.scalar.noderiv: An implementation of the active Covariance Matrix Adaptation Evolution Strategy (CMA-ES) for non-linear, non-convex, non-smooth, global function minimization.
CMAESOptimizer(int, double, boolean, int, int, RandomGenerator, boolean, ConvergenceChecker<PointValuePair>) - Constructor for class org.hipparchus.optim.nonlinear.scalar.noderiv.CMAESOptimizer: Simple constructor.
CMAESOptimizer.PopulationSize - Class in org.hipparchus.optim.nonlinear.scalar.noderiv: Population size.
CMAESOptimizer.Sigma - Class in org.hipparchus.optim.nonlinear.scalar.noderiv: Input sigma values.
computeJacobian(double[]) - Method in interface org.hipparchus.optim.nonlinear.vector.leastsquares.ValueAndJacobianFunction: Compute the Jacobian.
computeObjectiveGradient(double[]) - Method in class org.hipparchus.optim.nonlinear.scalar.GradientMultivariateOptimizer: Compute the gradient vector.
computeObjectiveValue(double) - Method in class org.hipparchus.optim.univariate.UnivariateOptimizer: Computes the objective function value.
computeObjectiveValue(double[]) - Method in class org.hipparchus.optim.nonlinear.scalar.MultivariateOptimizer: Computes the objective function value.
computeValue(double[]) - Method in interface org.hipparchus.optim.nonlinear.vector.leastsquares.ValueAndJacobianFunction: Compute the value.
Constraint - Interface in org.hipparchus.optim.nonlinear.vector.constrained: Generic constraint.
ConstraintOptimizer - Class in org.hipparchus.optim.nonlinear.vector.constrained: Abstract Constraint Optimizer.
ConstraintOptimizer() - Constructor for class org.hipparchus.optim.nonlinear.vector.constrained.ConstraintOptimizer: Simple constructor.
CONSTRAINTS_RANK - Enum constant in enum org.hipparchus.optim.LocalizedOptimFormats: CONSTRAINTS_RANK.
converged(double, double, double, double) - Method in class org.hipparchus.optim.nonlinear.vector.constrained.ADMMQPConvergenceChecker: Evaluate convergence.
converged(int, LagrangeSolution, LagrangeSolution) - Method in class org.hipparchus.optim.nonlinear.vector.constrained.ADMMQPConvergenceChecker: Check if the optimization algorithm has converged.
converged(int, LeastSquaresProblem.Evaluation, LeastSquaresProblem.Evaluation) - Method in class org.hipparchus.optim.nonlinear.vector.leastsquares.EvaluationRmsChecker: Check if the optimization algorithm has converged.
converged(int, PointValuePair, PointValuePair) - Method in class org.hipparchus.optim.SimpleValueChecker: Check if the optimization algorithm has converged considering the last two points.
converged(int, PointVectorValuePair, PointVectorValuePair) - Method in class org.hipparchus.optim.SimpleVectorValueChecker: Check if the optimization algorithm has converged considering the last two points.
converged(int, UnivariatePointValuePair, UnivariatePointValuePair) - Method in class org.hipparchus.optim.univariate.SimpleUnivariateValueChecker: Check if the optimization algorithm has converged considering the last two points.
converged(int, P, P) - Method in class org.hipparchus.optim.AbstractConvergenceChecker: Check if the optimization algorithm has converged.
converged(int, P, P) - Method in interface org.hipparchus.optim.ConvergenceChecker: Check if the optimization algorithm has converged.
converged(int, P, P) - Method in class org.hipparchus.optim.ConvergenceCheckerAndMultiplexer: Check if the optimization algorithm has converged.
converged(int, P, P) - Method in class org.hipparchus.optim.ConvergenceCheckerOrMultiplexer: Check if the optimization algorithm has converged.
converged(int, P, P) - Method in class org.hipparchus.optim.SimplePointChecker: Check if the optimization algorithm has converged considering the last two points.
ConvergenceChecker - Interface in org.hipparchus.optim: This interface specifies how to check if an optimization algorithm has converged.
ConvergenceCheckerAndMultiplexer - Class in org.hipparchus.optim: Multiplexer for ConvergenceChecker, checking all the checkers converged.
ConvergenceCheckerAndMultiplexer(List<ConvergenceChecker>) - Constructor for class org.hipparchus.optim.ConvergenceCheckerAndMultiplexer: Simple constructor.
ConvergenceCheckerOrMultiplexer - Class in org.hipparchus.optim: Multiplexer for ConvergenceChecker, checking one of the checkers converged.
ConvergenceCheckerOrMultiplexer(List<ConvergenceChecker>) - Constructor for class org.hipparchus.optim.ConvergenceCheckerOrMultiplexer: Simple constructor.
countEvaluations(LeastSquaresProblem, Incrementor) - Static method in class org.hipparchus.optim.nonlinear.vector.leastsquares.LeastSquaresFactory: Count the evaluations of a particular problem.
create(MultivariateVectorFunction, MultivariateMatrixFunction, double[], double[], RealMatrix, ConvergenceChecker<LeastSquaresProblem.Evaluation>, int, int) - Static method in class org.hipparchus.optim.nonlinear.vector.leastsquares.LeastSquaresFactory: Create a LeastSquaresProblem from the given elements.
create(MultivariateJacobianFunction, RealVector, RealVector, RealMatrix, ConvergenceChecker<LeastSquaresProblem.Evaluation>, int, int) - Static method in class org.hipparchus.optim.nonlinear.vector.leastsquares.LeastSquaresFactory: Create a LeastSquaresProblem from the given elements.
create(MultivariateJacobianFunction, RealVector, RealVector, RealMatrix, ConvergenceChecker<LeastSquaresProblem.Evaluation>, int, int, boolean, ParameterValidator) - Static method in class org.hipparchus.optim.nonlinear.vector.leastsquares.LeastSquaresFactory: Create a LeastSquaresProblem from the given elements.
create(MultivariateJacobianFunction, RealVector, RealVector, ConvergenceChecker<LeastSquaresProblem.Evaluation>, int, int) - Static method in class org.hipparchus.optim.nonlinear.vector.leastsquares.LeastSquaresFactory: Create a LeastSquaresProblem from the given elements.

D

DANTZIG - Enum constant in enum org.hipparchus.optim.linear.PivotSelectionRule: The classical rule, the variable with the most negative coefficient in the objective function row will be chosen as entering variable.
DEFAULT_ALPHA - Static variable in class org.hipparchus.optim.nonlinear.vector.constrained.ADMMQPOption: Default Value of Alpha filter for ADMM iteration.
DEFAULT_B - Static variable in class org.hipparchus.optim.nonlinear.vector.constrained.SQPOption: Default parameter for quadratic line search.
DEFAULT_CONV_CRITERIA - Static variable in class org.hipparchus.optim.nonlinear.vector.constrained.SQPOption: Default convergence criteria.
DEFAULT_EPS - Static variable in class org.hipparchus.optim.nonlinear.vector.constrained.ADMMQPOption: Default Absolute and Relative Tolerance for convergence.
DEFAULT_EPS_INFEASIBLE - Static variable in class org.hipparchus.optim.nonlinear.vector.constrained.ADMMQPOption: Default Absolute and Relative Tolerance for Infeasible Criteria.
DEFAULT_EPSILON - Static variable in class org.hipparchus.optim.nonlinear.vector.constrained.SQPOption: Default tolerance for convergence and active constraint.
DEFAULT_INITIAL_RADIUS - Static variable in class org.hipparchus.optim.nonlinear.scalar.noderiv.BOBYQAOptimizer: Default value for BOBYQAOptimizer.initialTrustRegionRadius: 10.0 .
DEFAULT_MAX_LINE_SEARCH_ITERATION - Static variable in class org.hipparchus.optim.nonlinear.vector.constrained.SQPOption: Default max iteration before reset hessian.
DEFAULT_MAX_RHO_ITERATION - Static variable in class org.hipparchus.optim.nonlinear.vector.constrained.ADMMQPOption: Default Max number of weight changes.
DEFAULT_MU - Static variable in class org.hipparchus.optim.nonlinear.vector.constrained.SQPOption: Default parameter for evaluation of Armijo condition for descend direction.
DEFAULT_POLISHING - Static variable in class org.hipparchus.optim.nonlinear.vector.constrained.ADMMQPOption: Default Value for enabling polishing the solution.
DEFAULT_POLISHING_ITERATION - Static variable in class org.hipparchus.optim.nonlinear.vector.constrained.ADMMQPOption: Default Value for Iteration of polishing Algorithm.
DEFAULT_QP_MAX_LOOP - Static variable in class org.hipparchus.optim.nonlinear.vector.constrained.SQPOption: Default max iteration admitted for QP subproblem.
DEFAULT_RHO - Static variable in class org.hipparchus.optim.nonlinear.vector.constrained.SQPOption: Default weight for augmented QP subproblem.
DEFAULT_RHO_MAX - Static variable in class org.hipparchus.optim.nonlinear.vector.constrained.ADMMQPOption: Default Max Value for the Weight for ADMM iteration.
DEFAULT_RHO_MIN - Static variable in class org.hipparchus.optim.nonlinear.vector.constrained.ADMMQPOption: Default Min Value for the Weight for ADMM iteration.
DEFAULT_RHO_UPDATE - Static variable in class org.hipparchus.optim.nonlinear.vector.constrained.ADMMQPOption: Default Value for adapting the weight during iterations.
DEFAULT_SCALING - Static variable in class org.hipparchus.optim.nonlinear.vector.constrained.ADMMQPOption: Default Value for Enabling Problem Scaling.
DEFAULT_SCALING_MAX_ITERATION - Static variable in class org.hipparchus.optim.nonlinear.vector.constrained.ADMMQPOption: Default Value for the Max Iteration for the scaling.
DEFAULT_SIGMA - Static variable in class org.hipparchus.optim.nonlinear.vector.constrained.ADMMQPOption: Default Value of regularization term sigma for Karush–Kuhn–Tucker solver.
DEFAULT_SIGMA_MAX - Static variable in class org.hipparchus.optim.nonlinear.vector.constrained.SQPOption: Default max value admitted for additional variable in QP subproblem.
DEFAULT_STOPPING_RADIUS - Static variable in class org.hipparchus.optim.nonlinear.scalar.noderiv.BOBYQAOptimizer: Default value for BOBYQAOptimizer.stoppingTrustRegionRadius: 1.0E-8 .
DEFAULT_USE_FUNCTION_HESSIAN - Static variable in class org.hipparchus.optim.nonlinear.vector.constrained.SQPOption: Default flag for using BFGS update formula.
dim() - Method in class org.hipparchus.optim.nonlinear.vector.constrained.LinearBoundedConstraint: Returns the dimensionality of the function domain.
dim() - Method in class org.hipparchus.optim.nonlinear.vector.constrained.LinearEqualityConstraint
dim() - Method in class org.hipparchus.optim.nonlinear.vector.constrained.LinearInequalityConstraint
dim() - Method in class org.hipparchus.optim.nonlinear.vector.constrained.QuadraticFunction: Returns the dimensionality of the function domain.
dim() - Method in class org.hipparchus.optim.nonlinear.vector.constrained.TwiceDifferentiableFunction: Returns the dimensionality of the function domain.
dim() - Method in interface org.hipparchus.optim.nonlinear.vector.constrained.VectorDifferentiableFunction: Returns the dimensionality of the function domain.
dimY() - Method in class org.hipparchus.optim.nonlinear.vector.constrained.BoundedConstraint: Returns the dimensionality of the function eval.
dimY() - Method in interface org.hipparchus.optim.nonlinear.vector.constrained.VectorDifferentiableFunction: Returns the dimensionality of the function eval.
doIteration(SimplexTableau) - Method in class org.hipparchus.optim.linear.SimplexSolver: Runs one iteration of the Simplex method on the given model.
doOptimize() - Method in class org.hipparchus.optim.BaseMultiStartMultivariateOptimizer: Performs the bulk of the optimization algorithm.
doOptimize() - Method in class org.hipparchus.optim.BaseOptimizer: Performs the bulk of the optimization algorithm.
doOptimize() - Method in class org.hipparchus.optim.linear.SimplexSolver: Performs the bulk of the optimization algorithm.
doOptimize() - Method in class org.hipparchus.optim.nonlinear.scalar.gradient.NonLinearConjugateGradientOptimizer: Performs the bulk of the optimization algorithm.
doOptimize() - Method in class org.hipparchus.optim.nonlinear.scalar.noderiv.BOBYQAOptimizer: Performs the bulk of the optimization algorithm.
doOptimize() - Method in class org.hipparchus.optim.nonlinear.scalar.noderiv.CMAESOptimizer: Performs the bulk of the optimization algorithm.
doOptimize() - Method in class org.hipparchus.optim.nonlinear.scalar.noderiv.PowellOptimizer: Performs the bulk of the optimization algorithm.
doOptimize() - Method in class org.hipparchus.optim.nonlinear.scalar.noderiv.SimplexOptimizer: Performs the bulk of the optimization algorithm.
doOptimize() - Method in class org.hipparchus.optim.nonlinear.vector.constrained.ADMMQPOptimizer: Performs the bulk of the optimization algorithm.
doOptimize() - Method in class org.hipparchus.optim.nonlinear.vector.constrained.QPOptimizer
doOptimize() - Method in class org.hipparchus.optim.nonlinear.vector.constrained.SQPOptimizerGM: Performs the bulk of the optimization algorithm.
doOptimize() - Method in class org.hipparchus.optim.nonlinear.vector.constrained.SQPOptimizerS: Performs the bulk of the optimization algorithm.
doOptimize() - Method in class org.hipparchus.optim.univariate.BrentOptimizer: Performs the bulk of the optimization algorithm.
doOptimize() - Method in class org.hipparchus.optim.univariate.MultiStartUnivariateOptimizer: Performs the bulk of the optimization algorithm.

E

EQ - Enum constant in enum org.hipparchus.optim.linear.Relationship: Equality relationship.
EQUAL_VERTICES_IN_SIMPLEX - Enum constant in enum org.hipparchus.optim.LocalizedOptimFormats: EQUAL_VERTICES_IN_SIMPLEX.
EqualityConstraint - Class in org.hipparchus.optim.nonlinear.vector.constrained: Equality Constraint.
EqualityConstraint(RealVector) - Constructor for class org.hipparchus.optim.nonlinear.vector.constrained.EqualityConstraint: Simple constructor.
equals(Object) - Method in class org.hipparchus.optim.linear.LinearConstraint
equals(Object) - Method in class org.hipparchus.optim.linear.LinearObjectiveFunction
evaluate(MultivariateFunction, Comparator<PointValuePair>) - Method in class org.hipparchus.optim.nonlinear.scalar.noderiv.AbstractSimplex: Evaluate all the non-evaluated points of the simplex.
evaluate(RealVector) - Method in class org.hipparchus.optim.nonlinear.vector.leastsquares.LeastSquaresAdapter: Evaluate the model at the specified point.
evaluate(RealVector) - Method in interface org.hipparchus.optim.nonlinear.vector.leastsquares.LeastSquaresProblem: Evaluate the model at the specified point.
evaluationChecker(ConvergenceChecker<PointVectorValuePair>) - Static method in class org.hipparchus.optim.nonlinear.vector.leastsquares.LeastSquaresFactory: View a convergence checker specified for a PointVectorValuePair as one specified for an LeastSquaresProblem.Evaluation.
EvaluationRmsChecker - Class in org.hipparchus.optim.nonlinear.vector.leastsquares: Check if an optimization has converged based on the change in computed RMS.
EvaluationRmsChecker(double) - Constructor for class org.hipparchus.optim.nonlinear.vector.leastsquares.EvaluationRmsChecker: Create a convergence checker for the RMS with the same relative and absolute tolerance.
EvaluationRmsChecker(double, double) - Constructor for class org.hipparchus.optim.nonlinear.vector.leastsquares.EvaluationRmsChecker: Create a convergence checker for the RMS with a relative and absolute tolerance.
evaluations - Variable in class org.hipparchus.optim.BaseOptimizer: Evaluations counter.

F

FLETCHER_REEVES - Enum constant in enum org.hipparchus.optim.nonlinear.scalar.gradient.NonLinearConjugateGradientOptimizer.Formula: Fletcher-Reeves formula.

G

GaussNewtonOptimizer - Class in org.hipparchus.optim.nonlinear.vector.leastsquares: Gauss-Newton least-squares solver.
GaussNewtonOptimizer() - Constructor for class org.hipparchus.optim.nonlinear.vector.leastsquares.GaussNewtonOptimizer: Creates a Gauss Newton optimizer.
GaussNewtonOptimizer(MatrixDecomposer, boolean) - Constructor for class org.hipparchus.optim.nonlinear.vector.leastsquares.GaussNewtonOptimizer: Create a Gauss Newton optimizer that uses the given matrix decomposition algorithm to solve the normal equations.
GEQ - Enum constant in enum org.hipparchus.optim.linear.Relationship: Greater than or equal relationship.
getA() - Method in class org.hipparchus.optim.nonlinear.vector.constrained.LinearEqualityConstraint: Get the matrix of linear weights.
getAbsoluteThreshold() - Method in class org.hipparchus.optim.AbstractConvergenceChecker: Get absolute threshold.
getAlpha() - Method in class org.hipparchus.optim.nonlinear.vector.constrained.ADMMQPOption: Get value of alpha filter for ADMM iteration.
getB() - Method in class org.hipparchus.optim.nonlinear.vector.constrained.SQPOption: Get parameter for quadratic line search.
getChiSquare() - Method in class org.hipparchus.optim.nonlinear.vector.leastsquares.AbstractEvaluation: Get the sum of the squares of the residuals.
getChiSquare() - Method in interface org.hipparchus.optim.nonlinear.vector.leastsquares.LeastSquaresProblem.Evaluation: Get the sum of the squares of the residuals.
getCoefficients() - Method in class org.hipparchus.optim.linear.LinearConstraint: Gets the coefficients of the constraint (left hand side).
getCoefficients() - Method in class org.hipparchus.optim.linear.LinearObjectiveFunction: Gets the coefficients of the linear equation being optimized.
getConstantTerm() - Method in class org.hipparchus.optim.linear.LinearObjectiveFunction: Gets the constant of the linear equation being optimized.
getConstraints() - Method in class org.hipparchus.optim.linear.LinearConstraintSet: Gets the set of linear constraints.
getConstraints() - Method in class org.hipparchus.optim.linear.LinearOptimizer: Get constraints.
getConvCriteria() - Method in class org.hipparchus.optim.nonlinear.vector.constrained.SQPOption: Get convergence criteria.
getConvergenceChecker() - Method in class org.hipparchus.optim.AbstractOptimizationProblem: Gets the convergence checker.
getConvergenceChecker() - Method in class org.hipparchus.optim.BaseOptimizer: Gets the convergence checker.
getConvergenceChecker() - Method in class org.hipparchus.optim.nonlinear.vector.constrained.ADMMQPOptimizer: Gets the convergence checker.
getConvergenceChecker() - Method in class org.hipparchus.optim.nonlinear.vector.leastsquares.LeastSquaresAdapter: Gets the convergence checker.
getConvergenceChecker() - Method in interface org.hipparchus.optim.OptimizationProblem: Gets the convergence checker.
getCost() - Method in class org.hipparchus.optim.nonlinear.vector.leastsquares.AbstractEvaluation: Get the cost.
getCost() - Method in interface org.hipparchus.optim.nonlinear.vector.leastsquares.LeastSquaresProblem.Evaluation: Get the cost.
getCostRelativeTolerance() - Method in class org.hipparchus.optim.nonlinear.vector.leastsquares.LevenbergMarquardtOptimizer: Gets the value of a tuning parameter.
getCovariances(double) - Method in class org.hipparchus.optim.nonlinear.vector.leastsquares.AbstractEvaluation: Get the covariance matrix of the optimized parameters.
getCovariances(double) - Method in interface org.hipparchus.optim.nonlinear.vector.leastsquares.LeastSquaresProblem.Evaluation: Get the covariance matrix of the optimized parameters.
getD() - Method in class org.hipparchus.optim.nonlinear.vector.constrained.QuadraticFunction: Get constant term.
getDecomposer() - Method in class org.hipparchus.optim.nonlinear.vector.leastsquares.GaussNewtonOptimizer: Get the matrix decomposition algorithm.
getDecomposer() - Method in class org.hipparchus.optim.nonlinear.vector.leastsquares.SequentialGaussNewtonOptimizer: Get the matrix decomposition algorithm.
getDimension() - Method in class org.hipparchus.optim.nonlinear.scalar.noderiv.AbstractSimplex: Get simplex dimension.
getEps() - Method in class org.hipparchus.optim.nonlinear.vector.constrained.ADMMQPOption: Get absolute and Relative Tolerance for convergence.
getEps() - Method in class org.hipparchus.optim.nonlinear.vector.constrained.SQPOption: Get tolerance for convergence and active constraint evaluation.
getEpsInfeasible() - Method in class org.hipparchus.optim.nonlinear.vector.constrained.ADMMQPOption: Get absolute and Relative Tolerance for infeasible criteria.
getEqConstraint() - Method in class org.hipparchus.optim.nonlinear.vector.constrained.AbstractSQPOptimizer: Getter for equality constraint.
getEvaluationCounter() - Method in class org.hipparchus.optim.AbstractOptimizationProblem: Get a independent Incrementor that counts up to the maximum number of evaluations and then throws an exception.
getEvaluationCounter() - Method in class org.hipparchus.optim.nonlinear.vector.leastsquares.LeastSquaresAdapter: Get a independent Incrementor that counts up to the maximum number of evaluations and then throws an exception.
getEvaluationCounter() - Method in interface org.hipparchus.optim.OptimizationProblem: Get a independent Incrementor that counts up to the maximum number of evaluations and then throws an exception.
getEvaluations() - Method in class org.hipparchus.optim.BaseMultiStartMultivariateOptimizer: Gets the number of evaluations of the objective function.
getEvaluations() - Method in class org.hipparchus.optim.BaseOptimizer: Gets the number of evaluations of the objective function.
getEvaluations() - Method in interface org.hipparchus.optim.nonlinear.vector.leastsquares.LeastSquaresOptimizer.Optimum: Get the number of times the model was evaluated in order to produce this optimum.
getEvaluations() - Method in class org.hipparchus.optim.univariate.BracketFinder: Get number of evaluations.
getEvaluations() - Method in class org.hipparchus.optim.univariate.MultiStartUnivariateOptimizer: Gets the number of evaluations of the objective function.
getFHi() - Method in class org.hipparchus.optim.univariate.BracketFinder: Get function value at BracketFinder.getHi().
getFLo() - Method in class org.hipparchus.optim.univariate.BracketFinder: Get function value at BracketFinder.getLo().
getFMid() - Method in class org.hipparchus.optim.univariate.BracketFinder: Get function value at BracketFinder.getMid().
getFunction() - Method in class org.hipparchus.optim.linear.LinearOptimizer: Get optimization type.
getGoalType() - Method in class org.hipparchus.optim.nonlinear.scalar.MultivariateOptimizer: Get optimization type.
getGoalType() - Method in class org.hipparchus.optim.univariate.UnivariateOptimizer: Get optimization type.
getHi() - Method in class org.hipparchus.optim.univariate.BracketFinder: Get higher bound of the bracket.
getInitialGuess() - Method in class org.hipparchus.optim.InitialGuess: Gets the initial guess.
getInitialStepBoundFactor() - Method in class org.hipparchus.optim.nonlinear.vector.leastsquares.LevenbergMarquardtOptimizer: Gets the value of a tuning parameter.
getIqConstraint() - Method in class org.hipparchus.optim.nonlinear.vector.constrained.AbstractSQPOptimizer: Getter for inequality constraint.
getIterationCounter() - Method in class org.hipparchus.optim.AbstractOptimizationProblem: Get a independent Incrementor that counts up to the maximum number of iterations and then throws an exception.
getIterationCounter() - Method in class org.hipparchus.optim.nonlinear.vector.leastsquares.LeastSquaresAdapter: Get a independent Incrementor that counts up to the maximum number of iterations and then throws an exception.
getIterationCounter() - Method in interface org.hipparchus.optim.OptimizationProblem: Get a independent Incrementor that counts up to the maximum number of iterations and then throws an exception.
getIterations() - Method in class org.hipparchus.optim.BaseOptimizer: Gets the number of iterations performed by the algorithm.
getIterations() - Method in interface org.hipparchus.optim.nonlinear.vector.leastsquares.LeastSquaresOptimizer.Optimum: Get the number of times the algorithm iterated in order to produce this optimum.
getJacobian() - Method in interface org.hipparchus.optim.nonlinear.vector.leastsquares.LeastSquaresProblem.Evaluation: Get the weighted Jacobian matrix.
getLambda() - Method in class org.hipparchus.optim.nonlinear.vector.constrained.LagrangeSolution: Returns Lambda Multiplier
getLo() - Method in class org.hipparchus.optim.univariate.BracketFinder: Get lower bound of the bracket.
getLocalizedString(Locale) - Method in enum org.hipparchus.optim.LocalizedOptimFormats
getLower() - Method in class org.hipparchus.optim.SimpleBounds: Gets the lower bounds.
getLowerBound() - Method in class org.hipparchus.optim.BaseMultivariateOptimizer: Get lower bounds.
getLowerBound() - Method in class org.hipparchus.optim.nonlinear.vector.constrained.BoundedConstraint: Get Lower Bound for value(x).
getLowerBound() - Method in interface org.hipparchus.optim.nonlinear.vector.constrained.Constraint: Get Lower Bound for value(x).
getMax() - Method in class org.hipparchus.optim.univariate.SearchInterval: Gets the upper bound.
getMax() - Method in class org.hipparchus.optim.univariate.UnivariateOptimizer: Get upper bounds.
getMaxEval() - Method in class org.hipparchus.optim.MaxEval: Gets the maximum number of evaluations.
getMaxEvaluations() - Method in class org.hipparchus.optim.BaseOptimizer: Gets the maximal number of function evaluations.
getMaxEvaluations() - Method in class org.hipparchus.optim.univariate.BracketFinder: Get maximum number of evaluations.
getMaxIter() - Method in class org.hipparchus.optim.MaxIter: Gets the maximum number of evaluations.
getMaxIterations() - Method in class org.hipparchus.optim.BaseOptimizer: Gets the maximal number of iterations.
getMaxLineSearchIteration() - Method in class org.hipparchus.optim.nonlinear.vector.constrained.SQPOption: Get max Iteration for the line search
getMaxRhoIteration() - Method in class org.hipparchus.optim.nonlinear.vector.constrained.ADMMQPOption: Get max number of weight changes.
getMid() - Method in class org.hipparchus.optim.univariate.BracketFinder: Get a point in the middle of the bracket.
getMin() - Method in class org.hipparchus.optim.univariate.SearchInterval: Gets the lower bound.
getMin() - Method in class org.hipparchus.optim.univariate.UnivariateOptimizer: Get lower bounds.
getMu() - Method in class org.hipparchus.optim.nonlinear.vector.constrained.SQPOption: Get parameter for evaluation of Armijo condition for descend direction.
getObj() - Method in class org.hipparchus.optim.nonlinear.vector.constrained.AbstractSQPOptimizer: Getter for objective function.
getObjectiveFunction() - Method in class org.hipparchus.optim.nonlinear.scalar.ObjectiveFunction: Gets the function to be optimized.
getObjectiveFunction() - Method in class org.hipparchus.optim.univariate.UnivariateObjectiveFunction: Gets the function to be optimized.
getObjectiveFunctionGradient() - Method in class org.hipparchus.optim.nonlinear.scalar.ObjectiveFunctionGradient: Gets the gradient of the function to be optimized.
getObservationSize() - Method in class org.hipparchus.optim.nonlinear.vector.leastsquares.LeastSquaresAdapter: Get the number of observations (rows in the Jacobian) in this problem.
getObservationSize() - Method in interface org.hipparchus.optim.nonlinear.vector.leastsquares.LeastSquaresProblem: Get the number of observations (rows in the Jacobian) in this problem.
getOldEvaluation() - Method in class org.hipparchus.optim.nonlinear.vector.leastsquares.SequentialGaussNewtonOptimizer: Get the previous evaluation used by the optimizer.
getOptima() - Method in class org.hipparchus.optim.BaseMultiStartMultivariateOptimizer: Gets all the optima found during the last call to optimize.
getOptima() - Method in class org.hipparchus.optim.nonlinear.scalar.MultiStartMultivariateOptimizer: Gets all the optima found during the last call to optimize.
getOptima() - Method in class org.hipparchus.optim.univariate.MultiStartUnivariateOptimizer: Gets all the optima found during the last call to optimize.
getOrthoTolerance() - Method in class org.hipparchus.optim.nonlinear.vector.leastsquares.LevenbergMarquardtOptimizer: Gets the value of a tuning parameter.
getP() - Method in class org.hipparchus.optim.nonlinear.vector.constrained.QuadraticFunction: Get square matrix of weights for quadratic terms.
getParameterRelativeTolerance() - Method in class org.hipparchus.optim.nonlinear.vector.leastsquares.LevenbergMarquardtOptimizer: Gets the value of a tuning parameter.
getParameterSize() - Method in class org.hipparchus.optim.nonlinear.vector.leastsquares.LeastSquaresAdapter: Get the number of parameters (columns in the Jacobian) in this problem.
getParameterSize() - Method in interface org.hipparchus.optim.nonlinear.vector.leastsquares.LeastSquaresProblem: Get the number of parameters (columns in the Jacobian) in this problem.
getPoint() - Method in interface org.hipparchus.optim.nonlinear.vector.leastsquares.LeastSquaresProblem.Evaluation: Get the abscissa (independent variables) of this evaluation.
getPoint() - Method in class org.hipparchus.optim.PointValuePair: Gets the point.
getPoint() - Method in class org.hipparchus.optim.PointVectorValuePair: Gets the point.
getPoint() - Method in class org.hipparchus.optim.univariate.UnivariatePointValuePair: Get the point.
getPoint(int) - Method in class org.hipparchus.optim.nonlinear.scalar.noderiv.AbstractSimplex: Get the simplex point stored at the requested index.
getPointRef() - Method in class org.hipparchus.optim.PointValuePair: Gets a reference to the point.
getPointRef() - Method in class org.hipparchus.optim.PointVectorValuePair: Gets a reference to the point.
getPoints() - Method in class org.hipparchus.optim.nonlinear.scalar.noderiv.AbstractSimplex: Get the points of the simplex.
getPolishIteration() - Method in class org.hipparchus.optim.nonlinear.vector.constrained.ADMMQPOption: Get number of iterations of polishing algorithm.
getPopulationSize() - Method in class org.hipparchus.optim.nonlinear.scalar.noderiv.CMAESOptimizer.PopulationSize: Get population size.
getQ() - Method in class org.hipparchus.optim.nonlinear.vector.constrained.QuadraticFunction: Get vector of weights for linear terms.
getQpMaxLoop() - Method in class org.hipparchus.optim.nonlinear.vector.constrained.SQPOption: Get max iteration admitted for QP subproblem evaluation.
getRankingThreshold() - Method in class org.hipparchus.optim.nonlinear.vector.leastsquares.LevenbergMarquardtOptimizer: Gets the value of a tuning parameter.
getReducedChiSquare(int) - Method in class org.hipparchus.optim.nonlinear.vector.leastsquares.AbstractEvaluation: Get the reduced chi-square.
getReducedChiSquare(int) - Method in interface org.hipparchus.optim.nonlinear.vector.leastsquares.LeastSquaresProblem.Evaluation: Get the reduced chi-square.
getRelationship() - Method in class org.hipparchus.optim.linear.LinearConstraint: Gets the relationship between left and right hand sides.
getRelativeThreshold() - Method in class org.hipparchus.optim.AbstractConvergenceChecker: Get relative threshold.
getResiduals() - Method in interface org.hipparchus.optim.nonlinear.vector.leastsquares.LeastSquaresProblem.Evaluation: Get the weighted residuals.
getRhoCons() - Method in class org.hipparchus.optim.nonlinear.vector.constrained.SQPOption: Get weight for augmented QP subproblem.
getRhoMax() - Method in class org.hipparchus.optim.nonlinear.vector.constrained.ADMMQPOption: Get max Value for the Weight for ADMM iteration.
getRhoMin() - Method in class org.hipparchus.optim.nonlinear.vector.constrained.ADMMQPOption: Get min Value for the Weight for ADMM iteration.
getRMS() - Method in class org.hipparchus.optim.nonlinear.vector.leastsquares.AbstractEvaluation: Get the normalized cost.
getRMS() - Method in interface org.hipparchus.optim.nonlinear.vector.leastsquares.LeastSquaresProblem.Evaluation: Get the normalized cost.
getScaledA() - Method in class org.hipparchus.optim.nonlinear.vector.constrained.ADMMQPModifiedRuizEquilibrium: Get scaled constraints coefficients matrix.
getScaledH() - Method in class org.hipparchus.optim.nonlinear.vector.constrained.ADMMQPModifiedRuizEquilibrium: Get scaled square matrix of weights for quadratic terms.
getScaledLUb(RealVector) - Method in class org.hipparchus.optim.nonlinear.vector.constrained.ADMMQPModifiedRuizEquilibrium: Get scaled upper bound
getScaledQ() - Method in class org.hipparchus.optim.nonlinear.vector.constrained.ADMMQPModifiedRuizEquilibrium: Get scaled vector of weights for linear terms.
getScaleMaxIteration() - Method in class org.hipparchus.optim.nonlinear.vector.constrained.ADMMQPOption: Get max iteration for the scaling.
getSettings() - Method in class org.hipparchus.optim.nonlinear.vector.constrained.AbstractSQPOptimizer: Getter for settings.
getSigma() - Method in class org.hipparchus.optim.nonlinear.scalar.noderiv.CMAESOptimizer.Sigma: Get sigma values.
getSigma() - Method in class org.hipparchus.optim.nonlinear.vector.constrained.ADMMQPOption: Get value of regularization term sigma for Karush–Kuhn–Tucker solver.
getSigma(double) - Method in class org.hipparchus.optim.nonlinear.vector.leastsquares.AbstractEvaluation: Get an estimate of the standard deviation of the parameters.
getSigma(double) - Method in interface org.hipparchus.optim.nonlinear.vector.leastsquares.LeastSquaresProblem.Evaluation: Get an estimate of the standard deviation of the parameters.
getSigmaMax() - Method in class org.hipparchus.optim.nonlinear.vector.constrained.SQPOption: Get max value admitted for the solution of the additional variable in QP subproblem.
getSize() - Method in class org.hipparchus.optim.nonlinear.scalar.noderiv.AbstractSimplex: Get simplex size.
getSolution() - Method in class org.hipparchus.optim.linear.SolutionCallback: Retrieve the best solution found so far.
getSourceString() - Method in enum org.hipparchus.optim.LocalizedOptimFormats
getStart() - Method in class org.hipparchus.optim.nonlinear.vector.leastsquares.LeastSquaresAdapter: Gets the initial guess.
getStart() - Method in interface org.hipparchus.optim.nonlinear.vector.leastsquares.LeastSquaresProblem: Gets the initial guess.
getStartPoint() - Method in class org.hipparchus.optim.BaseMultivariateOptimizer: Gets the initial guess.
getStartValue() - Method in class org.hipparchus.optim.univariate.SearchInterval: Gets the start value.
getStartValue() - Method in class org.hipparchus.optim.univariate.UnivariateOptimizer: Get initial guess.
getStatisticsDHistory() - Method in class org.hipparchus.optim.nonlinear.scalar.noderiv.CMAESOptimizer: Get history of D matrix.
getStatisticsFitnessHistory() - Method in class org.hipparchus.optim.nonlinear.scalar.noderiv.CMAESOptimizer: Get history of fitness values.
getStatisticsMeanHistory() - Method in class org.hipparchus.optim.nonlinear.scalar.noderiv.CMAESOptimizer: Get history of mean matrix.
getStatisticsSigmaHistory() - Method in class org.hipparchus.optim.nonlinear.scalar.noderiv.CMAESOptimizer: Get history of sigma values.
getUpper() - Method in class org.hipparchus.optim.SimpleBounds: Gets the upper bounds.
getUpperBound() - Method in class org.hipparchus.optim.BaseMultivariateOptimizer: Get upper bounds.
getUpperBound() - Method in class org.hipparchus.optim.nonlinear.vector.constrained.BoundedConstraint: Get Upper Bound for value(x).
getUpperBound() - Method in interface org.hipparchus.optim.nonlinear.vector.constrained.Constraint: Get Upper Bound for value(x).
getV() - Method in class org.hipparchus.optim.nonlinear.vector.constrained.ADMMQPSolution: Returns V tilde auxiliary Variable
getValue() - Method in class org.hipparchus.optim.linear.LinearConstraint: Gets the value of the constraint (right hand side).
getValue() - Method in class org.hipparchus.optim.nonlinear.vector.constrained.LagrangeSolution: Returns min(max) evaluated function at x
getValue() - Method in class org.hipparchus.optim.PointVectorValuePair: Gets the value of the objective function.
getValue() - Method in class org.hipparchus.optim.univariate.UnivariatePointValuePair: Get the value of the objective function.
getValueRef() - Method in class org.hipparchus.optim.PointVectorValuePair: Gets a reference to the value of the objective function.
getX() - Method in class org.hipparchus.optim.nonlinear.vector.constrained.LagrangeSolution: Returns X solution
getZ() - Method in class org.hipparchus.optim.nonlinear.vector.constrained.ADMMQPSolution: Returns Z auxiliary Variable
GoalType - Enum in org.hipparchus.optim.nonlinear.scalar: Goal type for an optimization problem (minimization or maximization of a scalar function.
gradient(double[]) - Method in class org.hipparchus.optim.nonlinear.vector.constrained.TwiceDifferentiableFunction: Returns the gradient of this function at (x)
gradient(double[]) - Method in interface org.hipparchus.optim.nonlinear.vector.constrained.VectorDifferentiableFunction: Returns the gradient of this function at (x)
gradient(RealVector) - Method in class org.hipparchus.optim.nonlinear.vector.constrained.QuadraticFunction: Returns the gradient of this function at (x)
gradient(RealVector) - Method in class org.hipparchus.optim.nonlinear.vector.constrained.TwiceDifferentiableFunction: Returns the gradient of this function at (x)
GradientMultivariateOptimizer - Class in org.hipparchus.optim.nonlinear.scalar: Base class for implementing optimizers for multivariate scalar differentiable functions.
GradientMultivariateOptimizer(ConvergenceChecker<PointValuePair>) - Constructor for class org.hipparchus.optim.nonlinear.scalar.GradientMultivariateOptimizer: Simple constructor.

H

hashCode() - Method in class org.hipparchus.optim.linear.LinearConstraint
hashCode() - Method in class org.hipparchus.optim.linear.LinearObjectiveFunction
hessian(double[]) - Method in class org.hipparchus.optim.nonlinear.vector.constrained.TwiceDifferentiableFunction: The Hessian of this function at (x)
hessian(RealVector) - Method in class org.hipparchus.optim.nonlinear.vector.constrained.QuadraticFunction: The Hessian of this function at (x)
hessian(RealVector) - Method in class org.hipparchus.optim.nonlinear.vector.constrained.TwiceDifferentiableFunction: The Hessian of this function at (x)

I

IdentityPreconditioner() - Constructor for class org.hipparchus.optim.nonlinear.scalar.gradient.NonLinearConjugateGradientOptimizer.IdentityPreconditioner: Empty constructor.
incrementEvaluationCount() - Method in class org.hipparchus.optim.BaseOptimizer: Increment the evaluation count.
incrementIterationCount() - Method in class org.hipparchus.optim.BaseOptimizer: Increment the iteration count.
InequalityConstraint - Class in org.hipparchus.optim.nonlinear.vector.constrained: Inequality Constraint with lower bound only: \(l \le f(x)\).
InequalityConstraint(RealVector) - Constructor for class org.hipparchus.optim.nonlinear.vector.constrained.InequalityConstraint: Simple constructor.
InitialGuess - Class in org.hipparchus.optim: Starting point (first guess) of the optimization procedure.
InitialGuess(double[]) - Constructor for class org.hipparchus.optim.InitialGuess: Simple constructor.
initialize(RealMatrix, RealMatrix, RealVector, int, RealVector, RealVector, double, double, double) - Method in class org.hipparchus.optim.nonlinear.vector.constrained.ADMMQPKKT: Initialize problem
INVALID_IMPLEMENTATION - Enum constant in enum org.hipparchus.optim.LocalizedOptimFormats: INVALID_IMPLEMENTATION.
isConverged() - Method in class org.hipparchus.optim.nonlinear.vector.constrained.ADMMQPOptimizer: Check if convergence has been reached.
isFormNormalEquations() - Method in class org.hipparchus.optim.nonlinear.vector.leastsquares.GaussNewtonOptimizer: Get if the normal equations are explicitly formed.
isFormNormalEquations() - Method in class org.hipparchus.optim.nonlinear.vector.leastsquares.SequentialGaussNewtonOptimizer: Get if the normal equations are explicitly formed.
isPolishing() - Method in class org.hipparchus.optim.nonlinear.vector.constrained.ADMMQPOption: Check if polishing is enabled.
isRestrictedToNonNegative() - Method in class org.hipparchus.optim.linear.LinearOptimizer: Check if variables are restricted to non-negative values.
isRestrictedToNonNegative() - Method in class org.hipparchus.optim.linear.NonNegativeConstraint: Indicates whether all the variables must be restricted to non-negative values.
isScaling() - Method in class org.hipparchus.optim.nonlinear.vector.constrained.ADMMQPOption: Check if scaling is enabled.
isSolutionOptimal() - Method in class org.hipparchus.optim.linear.SolutionCallback: Returns if the found solution is optimal.
iterate(MultivariateFunction, Comparator<PointValuePair>) - Method in class org.hipparchus.optim.nonlinear.scalar.noderiv.AbstractSimplex: Compute the next simplex of the algorithm.
iterate(MultivariateFunction, Comparator<PointValuePair>) - Method in class org.hipparchus.optim.nonlinear.scalar.noderiv.MultiDirectionalSimplex: Compute the next simplex of the algorithm.
iterate(MultivariateFunction, Comparator<PointValuePair>) - Method in class org.hipparchus.optim.nonlinear.scalar.noderiv.NelderMeadSimplex: Compute the next simplex of the algorithm.
iterate(RealVector...) - Method in class org.hipparchus.optim.nonlinear.vector.constrained.ADMMQPKKT: Iterate Karush–Kuhn–Tucker equation from given list of Vector
iterate(RealVector...) - Method in interface org.hipparchus.optim.nonlinear.vector.constrained.KarushKuhnTuckerSolver: Iterate Karush–Kuhn–Tucker equation from given list of Vector
iterations - Variable in class org.hipparchus.optim.BaseOptimizer: Iterations counter.

J

jacobian(RealVector) - Method in class org.hipparchus.optim.nonlinear.vector.constrained.LinearBoundedConstraint: Returns the gradient of this function at (x)
jacobian(RealVector) - Method in class org.hipparchus.optim.nonlinear.vector.constrained.LinearEqualityConstraint
jacobian(RealVector) - Method in class org.hipparchus.optim.nonlinear.vector.constrained.LinearInequalityConstraint
jacobian(RealVector) - Method in interface org.hipparchus.optim.nonlinear.vector.constrained.VectorDifferentiableFunction: Returns the gradient of this function at (x)

K

KarushKuhnTuckerSolver<T> - Interface in org.hipparchus.optim.nonlinear.vector.constrained: Karush–Kuhn–Tucker Solver.

L

LagrangeSolution - Class in org.hipparchus.optim.nonlinear.vector.constrained: Container for Lagrange t-uple.
LagrangeSolution(RealVector, RealVector, double) - Constructor for class org.hipparchus.optim.nonlinear.vector.constrained.LagrangeSolution: Simple constructor.
lagrangianGradX(RealVector, RealMatrix, RealVector, RealVector) - Method in class org.hipparchus.optim.nonlinear.vector.constrained.AbstractSQPOptimizer: Compute Lagrangian gradient for variable X
lazyEvaluation(boolean) - Method in class org.hipparchus.optim.nonlinear.vector.leastsquares.LeastSquaresBuilder: Configure whether evaluation will be lazy or not.
LeastSquaresAdapter - Class in org.hipparchus.optim.nonlinear.vector.leastsquares: An adapter that delegates to another implementation of LeastSquaresProblem.
LeastSquaresAdapter(LeastSquaresProblem) - Constructor for class org.hipparchus.optim.nonlinear.vector.leastsquares.LeastSquaresAdapter: Delegate the LeastSquaresProblem interface to the given implementation.
LeastSquaresBuilder - Class in org.hipparchus.optim.nonlinear.vector.leastsquares: A mutable builder for LeastSquaresProblems.
LeastSquaresBuilder() - Constructor for class org.hipparchus.optim.nonlinear.vector.leastsquares.LeastSquaresBuilder: Empty constructor.
LeastSquaresConverter - Class in org.hipparchus.optim.nonlinear.scalar: This class converts vectorial objective functions to scalar objective functions when the goal is to minimize them.
LeastSquaresConverter(MultivariateVectorFunction, double[]) - Constructor for class org.hipparchus.optim.nonlinear.scalar.LeastSquaresConverter: Builds a simple converter for uncorrelated residuals with identical weights.
LeastSquaresConverter(MultivariateVectorFunction, double[], double[]) - Constructor for class org.hipparchus.optim.nonlinear.scalar.LeastSquaresConverter: Builds a simple converter for uncorrelated residuals with the specified weights.
LeastSquaresConverter(MultivariateVectorFunction, double[], RealMatrix) - Constructor for class org.hipparchus.optim.nonlinear.scalar.LeastSquaresConverter: Builds a simple converter for correlated residuals with the specified weights.
LeastSquaresFactory - Class in org.hipparchus.optim.nonlinear.vector.leastsquares: A Factory for creating LeastSquaresProblems.
LeastSquaresOptimizer - Interface in org.hipparchus.optim.nonlinear.vector.leastsquares: An algorithm that can be applied to a non-linear least squares problem.
LeastSquaresOptimizer.Optimum - Interface in org.hipparchus.optim.nonlinear.vector.leastsquares: The optimum found by the optimizer.
LeastSquaresProblem - Interface in org.hipparchus.optim.nonlinear.vector.leastsquares: The data necessary to define a non-linear least squares problem.
LeastSquaresProblem.Evaluation - Interface in org.hipparchus.optim.nonlinear.vector.leastsquares: An evaluation of a LeastSquaresProblem at a particular point.
LEQ - Enum constant in enum org.hipparchus.optim.linear.Relationship: Lesser than or equal relationship.
LevenbergMarquardtOptimizer - Class in org.hipparchus.optim.nonlinear.vector.leastsquares: This class solves a least-squares problem using the Levenberg-Marquardt algorithm.
LevenbergMarquardtOptimizer() - Constructor for class org.hipparchus.optim.nonlinear.vector.leastsquares.LevenbergMarquardtOptimizer: Default constructor.
LevenbergMarquardtOptimizer(double, double, double, double, double) - Constructor for class org.hipparchus.optim.nonlinear.vector.leastsquares.LevenbergMarquardtOptimizer: Construct an instance with all parameters specified.
LinearBoundedConstraint - Class in org.hipparchus.optim.nonlinear.vector.constrained: A set of linear inequality constraints expressed as ub>Ax>lb.
LinearBoundedConstraint(double[][], double[], double[]) - Constructor for class org.hipparchus.optim.nonlinear.vector.constrained.LinearBoundedConstraint: Construct a set of linear inequality constraints from Ax < B
LinearBoundedConstraint(RealMatrix, RealVector, RealVector) - Constructor for class org.hipparchus.optim.nonlinear.vector.constrained.LinearBoundedConstraint: Construct a set of linear inequality constraints from Ax < B
LinearConstraint - Class in org.hipparchus.optim.linear: A linear constraint for a linear optimization problem.
LinearConstraint(double[], double, Relationship, double[], double) - Constructor for class org.hipparchus.optim.linear.LinearConstraint: Build a constraint involving two linear equations.
LinearConstraint(double[], Relationship, double) - Constructor for class org.hipparchus.optim.linear.LinearConstraint: Build a constraint involving a single linear equation.
LinearConstraint(RealVector, double, Relationship, RealVector, double) - Constructor for class org.hipparchus.optim.linear.LinearConstraint: Build a constraint involving two linear equations.
LinearConstraint(RealVector, Relationship, double) - Constructor for class org.hipparchus.optim.linear.LinearConstraint: Build a constraint involving a single linear equation.
LinearConstraintSet - Class in org.hipparchus.optim.linear: Class that represents a set of linear constraints.
LinearConstraintSet(Collection<LinearConstraint>) - Constructor for class org.hipparchus.optim.linear.LinearConstraintSet: Creates a set containing the given constraints.
LinearConstraintSet(LinearConstraint...) - Constructor for class org.hipparchus.optim.linear.LinearConstraintSet: Creates a set containing the given constraints.
LinearEqualityConstraint - Class in org.hipparchus.optim.nonlinear.vector.constrained: A set of linear equality constraints given as Ax = b.
LinearEqualityConstraint(double[][], double[]) - Constructor for class org.hipparchus.optim.nonlinear.vector.constrained.LinearEqualityConstraint: Construct a set of linear equality constraints ax = b.
LinearEqualityConstraint(RealMatrix, RealVector) - Constructor for class org.hipparchus.optim.nonlinear.vector.constrained.LinearEqualityConstraint: Construct a set of linear equality constraints ax = b.
LinearInequalityConstraint - Class in org.hipparchus.optim.nonlinear.vector.constrained: Set of linear inequality constraints expressed as \( A x \gt B\).
LinearInequalityConstraint(double[][], double[]) - Constructor for class org.hipparchus.optim.nonlinear.vector.constrained.LinearInequalityConstraint: Construct a set of linear inequality constraints from Ax > B
LinearInequalityConstraint(RealMatrix, RealVector) - Constructor for class org.hipparchus.optim.nonlinear.vector.constrained.LinearInequalityConstraint: Construct a set of linear inequality constraints from \( A x \gt B\).
LinearObjectiveFunction - Class in org.hipparchus.optim.linear: An objective function for a linear optimization problem.
LinearObjectiveFunction(double[], double) - Constructor for class org.hipparchus.optim.linear.LinearObjectiveFunction: Simple constructor.
LinearObjectiveFunction(RealVector, double) - Constructor for class org.hipparchus.optim.linear.LinearObjectiveFunction: Simple constructor.
LinearOptimizer - Class in org.hipparchus.optim.linear: Base class for implementing linear optimizers.
LinearOptimizer() - Constructor for class org.hipparchus.optim.linear.LinearOptimizer: Simple constructor with default settings.
LineSearch - Class in org.hipparchus.optim.nonlinear.scalar: Class for finding the minimum of the objective function along a given direction.
LineSearch(MultivariateOptimizer, double, double, double) - Constructor for class org.hipparchus.optim.nonlinear.scalar.LineSearch: The BrentOptimizer default stopping criterion uses the tolerances to check the domain (point) values, not the function values.
LocalizedOptimFormats - Enum in org.hipparchus.optim: Enumeration for localized messages formats used in exceptions messages.

M

maxDual(RealVector, RealVector) - Method in class org.hipparchus.optim.nonlinear.vector.constrained.ADMMQPConvergenceChecker: Compute dual vectors max.
MaxEval - Class in org.hipparchus.optim: Maximum number of evaluations of the function to be optimized.
MaxEval(int) - Constructor for class org.hipparchus.optim.MaxEval: Simple constructor.
maxEvaluations(int) - Method in class org.hipparchus.optim.nonlinear.vector.leastsquares.LeastSquaresBuilder: Configure the max evaluations.
MAXIMIZE - Enum constant in enum org.hipparchus.optim.nonlinear.scalar.GoalType: Maximization.
MaxIter - Class in org.hipparchus.optim: Maximum number of iterations performed by an (iterative) algorithm.
MaxIter(int) - Constructor for class org.hipparchus.optim.MaxIter: Simple constructor.
maxIterations(int) - Method in class org.hipparchus.optim.nonlinear.vector.leastsquares.LeastSquaresBuilder: Configure the max iterations.
maxPrimal(RealVector, RealVector) - Method in class org.hipparchus.optim.nonlinear.vector.constrained.ADMMQPConvergenceChecker: Compute primal vectors max.
MINIMIZE - Enum constant in enum org.hipparchus.optim.nonlinear.scalar.GoalType: Minimization.
MINIMUM_PROBLEM_DIMENSION - Static variable in class org.hipparchus.optim.nonlinear.scalar.noderiv.BOBYQAOptimizer: Minimum dimension of the problem: 2
model(MultivariateVectorFunction, MultivariateMatrixFunction) - Method in class org.hipparchus.optim.nonlinear.vector.leastsquares.LeastSquaresBuilder: Configure the model function.
model(MultivariateVectorFunction, MultivariateMatrixFunction) - Static method in class org.hipparchus.optim.nonlinear.vector.leastsquares.LeastSquaresFactory: Combine a MultivariateVectorFunction with a MultivariateMatrixFunction to produce a MultivariateJacobianFunction.
model(MultivariateJacobianFunction) - Method in class org.hipparchus.optim.nonlinear.vector.leastsquares.LeastSquaresBuilder: Configure the model function.
MultiDirectionalSimplex - Class in org.hipparchus.optim.nonlinear.scalar.noderiv: This class implements the multi-directional direct search method.
MultiDirectionalSimplex(double[]) - Constructor for class org.hipparchus.optim.nonlinear.scalar.noderiv.MultiDirectionalSimplex: Build a multi-directional simplex with default coefficients.
MultiDirectionalSimplex(double[][]) - Constructor for class org.hipparchus.optim.nonlinear.scalar.noderiv.MultiDirectionalSimplex: Build a multi-directional simplex with default coefficients.
MultiDirectionalSimplex(double[][], double, double) - Constructor for class org.hipparchus.optim.nonlinear.scalar.noderiv.MultiDirectionalSimplex: Build a multi-directional simplex with specified coefficients.
MultiDirectionalSimplex(double[], double, double) - Constructor for class org.hipparchus.optim.nonlinear.scalar.noderiv.MultiDirectionalSimplex: Build a multi-directional simplex with specified coefficients.
MultiDirectionalSimplex(int) - Constructor for class org.hipparchus.optim.nonlinear.scalar.noderiv.MultiDirectionalSimplex: Build a multi-directional simplex with default coefficients.
MultiDirectionalSimplex(int, double) - Constructor for class org.hipparchus.optim.nonlinear.scalar.noderiv.MultiDirectionalSimplex: Build a multi-directional simplex with default coefficients.
MultiDirectionalSimplex(int, double, double) - Constructor for class org.hipparchus.optim.nonlinear.scalar.noderiv.MultiDirectionalSimplex: Build a multi-directional simplex with specified coefficients.
MultiDirectionalSimplex(int, double, double, double) - Constructor for class org.hipparchus.optim.nonlinear.scalar.noderiv.MultiDirectionalSimplex: Build a multi-directional simplex with specified coefficients.
MultiStartMultivariateOptimizer - Class in org.hipparchus.optim.nonlinear.scalar: Multi-start optimizer.
MultiStartMultivariateOptimizer(MultivariateOptimizer, int, RandomVectorGenerator) - Constructor for class org.hipparchus.optim.nonlinear.scalar.MultiStartMultivariateOptimizer: Create a multi-start optimizer from a single-start optimizer.
MultiStartUnivariateOptimizer - Class in org.hipparchus.optim.univariate: Special implementation of the UnivariateOptimizer interface adding multi-start features to an existing optimizer.
MultiStartUnivariateOptimizer(UnivariateOptimizer, int, RandomGenerator) - Constructor for class org.hipparchus.optim.univariate.MultiStartUnivariateOptimizer: Create a multi-start optimizer from a single-start optimizer.
MultivariateFunctionMappingAdapter - Class in org.hipparchus.optim.nonlinear.scalar: Adapter for mapping bounded MultivariateFunction to unbounded ones.
MultivariateFunctionMappingAdapter(MultivariateFunction, double[], double[]) - Constructor for class org.hipparchus.optim.nonlinear.scalar.MultivariateFunctionMappingAdapter: Simple constructor.
MultivariateFunctionPenaltyAdapter - Class in org.hipparchus.optim.nonlinear.scalar: Adapter extending bounded MultivariateFunction to an unbouded domain using a penalty function.
MultivariateFunctionPenaltyAdapter(MultivariateFunction, double[], double[], double, double[]) - Constructor for class org.hipparchus.optim.nonlinear.scalar.MultivariateFunctionPenaltyAdapter: Simple constructor.
MultivariateJacobianFunction - Interface in org.hipparchus.optim.nonlinear.vector.leastsquares: A interface for functions that compute a vector of values and can compute their derivatives (Jacobian).
MultivariateOptimizer - Class in org.hipparchus.optim.nonlinear.scalar: Base class for a multivariate scalar function optimizer.
MultivariateOptimizer(ConvergenceChecker<PointValuePair>) - Constructor for class org.hipparchus.optim.nonlinear.scalar.MultivariateOptimizer: Simple constructor.

N

NelderMeadSimplex - Class in org.hipparchus.optim.nonlinear.scalar.noderiv: This class implements the Nelder-Mead simplex algorithm.
NelderMeadSimplex(double[]) - Constructor for class org.hipparchus.optim.nonlinear.scalar.noderiv.NelderMeadSimplex: Build a Nelder-Mead simplex with default coefficients.
NelderMeadSimplex(double[][]) - Constructor for class org.hipparchus.optim.nonlinear.scalar.noderiv.NelderMeadSimplex: Build a Nelder-Mead simplex with default coefficients.
NelderMeadSimplex(double[][], double, double, double, double) - Constructor for class org.hipparchus.optim.nonlinear.scalar.noderiv.NelderMeadSimplex: Build a Nelder-Mead simplex with specified coefficients.
NelderMeadSimplex(double[], double, double, double, double) - Constructor for class org.hipparchus.optim.nonlinear.scalar.noderiv.NelderMeadSimplex: Build a Nelder-Mead simplex with specified coefficients.
NelderMeadSimplex(int) - Constructor for class org.hipparchus.optim.nonlinear.scalar.noderiv.NelderMeadSimplex: Build a Nelder-Mead simplex with default coefficients.
NelderMeadSimplex(int, double) - Constructor for class org.hipparchus.optim.nonlinear.scalar.noderiv.NelderMeadSimplex: Build a Nelder-Mead simplex with default coefficients.
NelderMeadSimplex(int, double, double, double, double) - Constructor for class org.hipparchus.optim.nonlinear.scalar.noderiv.NelderMeadSimplex: Build a Nelder-Mead simplex with specified coefficients.
NelderMeadSimplex(int, double, double, double, double, double) - Constructor for class org.hipparchus.optim.nonlinear.scalar.noderiv.NelderMeadSimplex: Build a Nelder-Mead simplex with specified coefficients.
NO_FEASIBLE_SOLUTION - Enum constant in enum org.hipparchus.optim.LocalizedOptimFormats: NO_FEASIBLE_SOLUTION.
NonLinearConjugateGradientOptimizer - Class in org.hipparchus.optim.nonlinear.scalar.gradient: Non-linear conjugate gradient optimizer.
NonLinearConjugateGradientOptimizer(NonLinearConjugateGradientOptimizer.Formula, ConvergenceChecker<PointValuePair>) - Constructor for class org.hipparchus.optim.nonlinear.scalar.gradient.NonLinearConjugateGradientOptimizer: Constructor with default tolerances for the line search (1e-8) and preconditioner.
NonLinearConjugateGradientOptimizer(NonLinearConjugateGradientOptimizer.Formula, ConvergenceChecker<PointValuePair>, double, double, double) - Constructor for class org.hipparchus.optim.nonlinear.scalar.gradient.NonLinearConjugateGradientOptimizer: Constructor with default preconditioner.
NonLinearConjugateGradientOptimizer(NonLinearConjugateGradientOptimizer.Formula, ConvergenceChecker<PointValuePair>, double, double, double, Preconditioner) - Constructor for class org.hipparchus.optim.nonlinear.scalar.gradient.NonLinearConjugateGradientOptimizer: Simple constructor.
NonLinearConjugateGradientOptimizer.Formula - Enum in org.hipparchus.optim.nonlinear.scalar.gradient: Available choices of update formulas for the updating the parameter that is used to compute the successive conjugate search directions.
NonLinearConjugateGradientOptimizer.IdentityPreconditioner - Class in org.hipparchus.optim.nonlinear.scalar.gradient: Default identity preconditioner.
NonNegativeConstraint - Class in org.hipparchus.optim.linear: A constraint for a linear optimization problem indicating whether all variables must be restricted to non-negative values.
NonNegativeConstraint(boolean) - Constructor for class org.hipparchus.optim.linear.NonNegativeConstraint: Simple constructor.
normalize(double, int) - Method in class org.hipparchus.optim.nonlinear.vector.constrained.ADMMQPModifiedRuizEquilibrium: Normalize matrices.

O

ObjectiveFunction - Class in org.hipparchus.optim.nonlinear.scalar: Scalar function to be optimized.
ObjectiveFunction(MultivariateFunction) - Constructor for class org.hipparchus.optim.nonlinear.scalar.ObjectiveFunction: Simple constructor.
ObjectiveFunctionGradient - Class in org.hipparchus.optim.nonlinear.scalar: Gradient of the scalar function to be optimized.
ObjectiveFunctionGradient(MultivariateVectorFunction) - Constructor for class org.hipparchus.optim.nonlinear.scalar.ObjectiveFunctionGradient: Simple constructor.
of(LeastSquaresProblem.Evaluation, int, int) - Static method in interface org.hipparchus.optim.nonlinear.vector.leastsquares.LeastSquaresOptimizer.Optimum: Create a new optimum from an evaluation and the values of the counters.
oppositeRelationship() - Method in enum org.hipparchus.optim.linear.Relationship: Gets the relationship obtained when multiplying all coefficients by -1.
OptimizationData - Interface in org.hipparchus.optim: Marker interface.
OptimizationProblem - Interface in org.hipparchus.optim: Common settings for all optimization problems.
optimize() - Method in class org.hipparchus.optim.BaseOptimizer: Performs the optimization.
optimize(LeastSquaresProblem) - Method in class org.hipparchus.optim.nonlinear.vector.leastsquares.GaussNewtonOptimizer: Solve the non-linear least squares problem.
optimize(LeastSquaresProblem) - Method in interface org.hipparchus.optim.nonlinear.vector.leastsquares.LeastSquaresOptimizer: Solve the non-linear least squares problem.
optimize(LeastSquaresProblem) - Method in class org.hipparchus.optim.nonlinear.vector.leastsquares.LevenbergMarquardtOptimizer: Solve the non-linear least squares problem.
optimize(LeastSquaresProblem) - Method in class org.hipparchus.optim.nonlinear.vector.leastsquares.SequentialGaussNewtonOptimizer: Solve the non-linear least squares problem.
optimize(OptimizationData...) - Method in class org.hipparchus.optim.BaseMultiStartMultivariateOptimizer: Stores data and performs the optimization.
optimize(OptimizationData...) - Method in class org.hipparchus.optim.BaseMultivariateOptimizer: Stores data and performs the optimization.
optimize(OptimizationData...) - Method in class org.hipparchus.optim.BaseOptimizer: Stores data and performs the optimization.
optimize(OptimizationData...) - Method in class org.hipparchus.optim.linear.LinearOptimizer: Stores data and performs the optimization.
optimize(OptimizationData...) - Method in class org.hipparchus.optim.linear.SimplexSolver: Stores data and performs the optimization.
optimize(OptimizationData...) - Method in class org.hipparchus.optim.nonlinear.scalar.gradient.NonLinearConjugateGradientOptimizer: Stores data and performs the optimization.
optimize(OptimizationData...) - Method in class org.hipparchus.optim.nonlinear.scalar.GradientMultivariateOptimizer: Stores data and performs the optimization.
optimize(OptimizationData...) - Method in class org.hipparchus.optim.nonlinear.scalar.MultivariateOptimizer: Stores data and performs the optimization.
optimize(OptimizationData...) - Method in class org.hipparchus.optim.nonlinear.scalar.noderiv.CMAESOptimizer: Stores data and performs the optimization.
optimize(OptimizationData...) - Method in class org.hipparchus.optim.nonlinear.scalar.noderiv.SimplexOptimizer: Stores data and performs the optimization.
optimize(OptimizationData...) - Method in class org.hipparchus.optim.nonlinear.vector.constrained.AbstractSQPOptimizer
optimize(OptimizationData...) - Method in class org.hipparchus.optim.nonlinear.vector.constrained.ADMMQPOptimizer: Stores data and performs the optimization.
optimize(OptimizationData...) - Method in class org.hipparchus.optim.nonlinear.vector.constrained.ConstraintOptimizer: Stores data and performs the optimization.
optimize(OptimizationData...) - Method in class org.hipparchus.optim.univariate.MultiStartUnivariateOptimizer: Stores data and performs the optimization.
optimize(OptimizationData...) - Method in class org.hipparchus.optim.univariate.UnivariateOptimizer: Stores data and performs the optimization.
org.hipparchus.optim - package org.hipparchus.optim: Generally, optimizers are algorithms that will either minimize or maximize a scalar function, called the objective function.
org.hipparchus.optim.linear - package org.hipparchus.optim.linear: Optimization algorithms for linear constrained problems.
org.hipparchus.optim.nonlinear.scalar - package org.hipparchus.optim.nonlinear.scalar: Algorithms for optimizing a scalar function.
org.hipparchus.optim.nonlinear.scalar.gradient - package org.hipparchus.optim.nonlinear.scalar.gradient: This package provides optimization algorithms that require derivatives.
org.hipparchus.optim.nonlinear.scalar.noderiv - package org.hipparchus.optim.nonlinear.scalar.noderiv: This package provides optimization algorithms that do not require derivatives.
org.hipparchus.optim.nonlinear.vector.constrained - package org.hipparchus.optim.nonlinear.vector.constrained: This package provides algorithms that minimize the residuals between observations and model values.
org.hipparchus.optim.nonlinear.vector.leastsquares - package org.hipparchus.optim.nonlinear.vector.leastsquares: This package provides algorithms that minimize the residuals between observations and model values.
org.hipparchus.optim.univariate - package org.hipparchus.optim.univariate: One-dimensional optimization algorithms.
overshoot(RealVector) - Method in class org.hipparchus.optim.nonlinear.vector.constrained.BoundedConstraint: Check how much a point overshoots the constraint.
overshoot(RealVector) - Method in interface org.hipparchus.optim.nonlinear.vector.constrained.Constraint: Check how much a point overshoots the constraint.

P

parameterValidator(ParameterValidator) - Method in class org.hipparchus.optim.nonlinear.vector.leastsquares.LeastSquaresBuilder: Configure the validator of the model parameters.
ParameterValidator - Interface in org.hipparchus.optim.nonlinear.vector.leastsquares: Interface for validating a set of model parameters.
parseOptimizationData(OptimizationData...) - Method in class org.hipparchus.optim.BaseMultivariateOptimizer: Scans the list of (required and optional) optimization data that characterize the problem.
parseOptimizationData(OptimizationData...) - Method in class org.hipparchus.optim.BaseOptimizer: Scans the list of (required and optional) optimization data that characterize the problem.
parseOptimizationData(OptimizationData...) - Method in class org.hipparchus.optim.linear.LinearOptimizer: Scans the list of (required and optional) optimization data that characterize the problem.
parseOptimizationData(OptimizationData...) - Method in class org.hipparchus.optim.linear.SimplexSolver: Scans the list of (required and optional) optimization data that characterize the problem.
parseOptimizationData(OptimizationData...) - Method in class org.hipparchus.optim.nonlinear.scalar.gradient.NonLinearConjugateGradientOptimizer: Scans the list of (required and optional) optimization data that characterize the problem.
parseOptimizationData(OptimizationData...) - Method in class org.hipparchus.optim.nonlinear.scalar.GradientMultivariateOptimizer: Scans the list of (required and optional) optimization data that characterize the problem.
parseOptimizationData(OptimizationData...) - Method in class org.hipparchus.optim.nonlinear.scalar.MultivariateOptimizer: Scans the list of (required and optional) optimization data that characterize the problem.
parseOptimizationData(OptimizationData...) - Method in class org.hipparchus.optim.nonlinear.scalar.noderiv.CMAESOptimizer: Scans the list of (required and optional) optimization data that characterize the problem.
parseOptimizationData(OptimizationData...) - Method in class org.hipparchus.optim.nonlinear.scalar.noderiv.SimplexOptimizer: Scans the list of (required and optional) optimization data that characterize the problem.
parseOptimizationData(OptimizationData...) - Method in class org.hipparchus.optim.nonlinear.vector.constrained.AbstractSQPOptimizer
parseOptimizationData(OptimizationData...) - Method in class org.hipparchus.optim.nonlinear.vector.constrained.ADMMQPOptimizer: Scans the list of (required and optional) optimization data that characterize the problem.
parseOptimizationData(OptimizationData...) - Method in class org.hipparchus.optim.univariate.UnivariateOptimizer: Scans the list of (required and optional) optimization data that characterize the problem.
PivotSelectionRule - Enum in org.hipparchus.optim.linear: Pivot selection rule to the use for a Simplex solver.
PointValuePair - Class in org.hipparchus.optim: This class holds a point and the value of an objective function at that point.
PointValuePair(double[], double) - Constructor for class org.hipparchus.optim.PointValuePair: Builds a point/objective function value pair.
PointValuePair(double[], double, boolean) - Constructor for class org.hipparchus.optim.PointValuePair: Builds a point/objective function value pair.
PointVectorValuePair - Class in org.hipparchus.optim: This class holds a point and the vectorial value of an objective function at that point.
PointVectorValuePair(double[], double[]) - Constructor for class org.hipparchus.optim.PointVectorValuePair: Builds a point/objective function value pair.
PointVectorValuePair(double[], double[], boolean) - Constructor for class org.hipparchus.optim.PointVectorValuePair: Build a point/objective function value pair.
POLAK_RIBIERE - Enum constant in enum org.hipparchus.optim.nonlinear.scalar.gradient.NonLinearConjugateGradientOptimizer.Formula: Polak-Ribière formula.
PopulationSize(int) - Constructor for class org.hipparchus.optim.nonlinear.scalar.noderiv.CMAESOptimizer.PopulationSize: Simple constructor.
PowellOptimizer - Class in org.hipparchus.optim.nonlinear.scalar.noderiv: Powell's algorithm.
PowellOptimizer(double, double) - Constructor for class org.hipparchus.optim.nonlinear.scalar.noderiv.PowellOptimizer: The parameters control the default convergence checking procedure.
PowellOptimizer(double, double, double, double) - Constructor for class org.hipparchus.optim.nonlinear.scalar.noderiv.PowellOptimizer: Builds an instance with the default convergence checking procedure.
PowellOptimizer(double, double, double, double, ConvergenceChecker<PointValuePair>) - Constructor for class org.hipparchus.optim.nonlinear.scalar.noderiv.PowellOptimizer: This constructor allows to specify a user-defined convergence checker, in addition to the parameters that control the default convergence checking procedure and the line search tolerances.
PowellOptimizer(double, double, ConvergenceChecker<PointValuePair>) - Constructor for class org.hipparchus.optim.nonlinear.scalar.noderiv.PowellOptimizer: This constructor allows to specify a user-defined convergence checker, in addition to the parameters that control the default convergence checking procedure.
precondition(double[], double[]) - Method in class org.hipparchus.optim.nonlinear.scalar.gradient.NonLinearConjugateGradientOptimizer.IdentityPreconditioner: Precondition a search direction.
precondition(double[], double[]) - Method in interface org.hipparchus.optim.nonlinear.scalar.gradient.Preconditioner: Precondition a search direction.
Preconditioner - Interface in org.hipparchus.optim.nonlinear.scalar.gradient: This interface represents a preconditioner for differentiable scalar objective function optimizers.

Q

QPOptimizer - Class in org.hipparchus.optim.nonlinear.vector.constrained: Quadratic programming Optimizater.
QPOptimizer() - Constructor for class org.hipparchus.optim.nonlinear.vector.constrained.QPOptimizer
QuadraticFunction - Class in org.hipparchus.optim.nonlinear.vector.constrained: Given P, Q, d, implements \(\frac{1}{2}x^T P X + Q^T x + d\).
QuadraticFunction(double[][], double[], double) - Constructor for class org.hipparchus.optim.nonlinear.vector.constrained.QuadraticFunction: Construct quadratic function \(\frac{1}{2}x^T P X + Q^T x + d\).
QuadraticFunction(RealMatrix, RealVector, double) - Constructor for class org.hipparchus.optim.nonlinear.vector.constrained.QuadraticFunction: Construct quadratic function \(\frac{1}{2}x^T P X + Q^T x + d\).

R

Relationship - Enum in org.hipparchus.optim.linear: Types of relationships between two cells in a Solver LinearConstraint.
replaceWorstPoint(PointValuePair, Comparator<PointValuePair>) - Method in class org.hipparchus.optim.nonlinear.scalar.noderiv.AbstractSimplex: Replace the worst point of the simplex by a new point.
residualDual(RealVector, RealVector) - Method in class org.hipparchus.optim.nonlinear.vector.constrained.ADMMQPConvergenceChecker: Compute dual residual.
residualPrime(RealVector, RealVector) - Method in class org.hipparchus.optim.nonlinear.vector.constrained.ADMMQPConvergenceChecker: Compute primal residual.

S

search(double[], double[]) - Method in class org.hipparchus.optim.nonlinear.scalar.LineSearch: Finds the number alpha that optimizes f(startPoint + alpha * direction).
search(UnivariateFunction, GoalType, double, double) - Method in class org.hipparchus.optim.univariate.BracketFinder: Search new points that bracket a local optimum of the function.
SearchInterval - Class in org.hipparchus.optim.univariate: Search interval and (optional) start value.
SearchInterval(double, double) - Constructor for class org.hipparchus.optim.univariate.SearchInterval: Simple constructor.
SearchInterval(double, double, double) - Constructor for class org.hipparchus.optim.univariate.SearchInterval: Simple constructor.
SequentialGaussNewtonOptimizer - Class in org.hipparchus.optim.nonlinear.vector.leastsquares: Sequential Gauss-Newton least-squares solver.
SequentialGaussNewtonOptimizer() - Constructor for class org.hipparchus.optim.nonlinear.vector.leastsquares.SequentialGaussNewtonOptimizer: Create a sequential Gauss Newton optimizer.
SequentialGaussNewtonOptimizer(MatrixDecomposer, boolean, LeastSquaresProblem.Evaluation) - Constructor for class org.hipparchus.optim.nonlinear.vector.leastsquares.SequentialGaussNewtonOptimizer: Create a sequential Gauss Newton optimizer that uses the given matrix decomposition algorithm to solve the normal equations.
setAlpha(double) - Method in class org.hipparchus.optim.nonlinear.vector.constrained.ADMMQPOption: Set value of alpha filter for ADMM iteration.
setB(double) - Method in class org.hipparchus.optim.nonlinear.vector.constrained.SQPOption: Set parameter for quadratic line search.
setConvCriteria(int) - Method in class org.hipparchus.optim.nonlinear.vector.constrained.SQPOption: Set convergence criteria.
setEps(double) - Method in class org.hipparchus.optim.nonlinear.vector.constrained.ADMMQPOption: Set absolute and Relative Tolerance for convergence.
setEps(double) - Method in class org.hipparchus.optim.nonlinear.vector.constrained.SQPOption: Set tolerance for convergence and active constraint evaluation.
setEpsInfeasible(double) - Method in class org.hipparchus.optim.nonlinear.vector.constrained.ADMMQPOption: Set absolute and Relative Tolerance for infeasible criteria.
setMaxLineSearchIteration(int) - Method in class org.hipparchus.optim.nonlinear.vector.constrained.SQPOption: Set max Iteration for the line search
setMaxRhoIteration(int) - Method in class org.hipparchus.optim.nonlinear.vector.constrained.ADMMQPOption: Set max number of weight changes.
setMu(double) - Method in class org.hipparchus.optim.nonlinear.vector.constrained.SQPOption: Set parameter for evaluation of Armijo condition for descend direction.
setPoint(int, PointValuePair) - Method in class org.hipparchus.optim.nonlinear.scalar.noderiv.AbstractSimplex: Store a new point at location index.
setPoints(PointValuePair[]) - Method in class org.hipparchus.optim.nonlinear.scalar.noderiv.AbstractSimplex: Replace all points.
setPolishing(boolean) - Method in class org.hipparchus.optim.nonlinear.vector.constrained.ADMMQPOption: Set polishing enabling flag.
setPolishingIteration(int) - Method in class org.hipparchus.optim.nonlinear.vector.constrained.ADMMQPOption: Set number of iterations of polishing algorithm.
setQpMaxLoop(int) - Method in class org.hipparchus.optim.nonlinear.vector.constrained.SQPOption: Set max iteration admitted for QP subproblem evaluation.
setRhoCons(double) - Method in class org.hipparchus.optim.nonlinear.vector.constrained.SQPOption: Set weight for augmented QP subproblem.
setRhoMax(double) - Method in class org.hipparchus.optim.nonlinear.vector.constrained.ADMMQPOption: Set max Value for the Weight for ADMM iteration.
setRhoMin(double) - Method in class org.hipparchus.optim.nonlinear.vector.constrained.ADMMQPOption: Set min Value for the Weight for ADMM iteration.
setScaleMaxIteration(int) - Method in class org.hipparchus.optim.nonlinear.vector.constrained.ADMMQPOption: Set max iteration for the scaling.
setScaling(boolean) - Method in class org.hipparchus.optim.nonlinear.vector.constrained.ADMMQPOption: Set scaling enabling flag.
setSigma(double) - Method in class org.hipparchus.optim.nonlinear.vector.constrained.ADMMQPOption: Set value of regularization term sigma for Karush–Kuhn–Tucker solver.
setSigmaMax(double) - Method in class org.hipparchus.optim.nonlinear.vector.constrained.SQPOption: Set max value admitted for the solution of the additional variable in QP subproblem.
setUpdateRho(boolean) - Method in class org.hipparchus.optim.nonlinear.vector.constrained.ADMMQPOption: Set weight updating flag.
setUseFunHessian(boolean) - Method in class org.hipparchus.optim.nonlinear.vector.constrained.SQPOption: Enable or Disable using direct the function Hessian.
Sigma(double[]) - Constructor for class org.hipparchus.optim.nonlinear.scalar.noderiv.CMAESOptimizer.Sigma: Simple constructor.
SimpleBounds - Class in org.hipparchus.optim: Simple optimization constraints: lower and upper bounds.
SimpleBounds(double[], double[]) - Constructor for class org.hipparchus.optim.SimpleBounds: Simple constructor.
SimplePointChecker> - Class in org.hipparchus.optim: Simple implementation of the ConvergenceChecker interface using only point coordinates.
SimplePointChecker(double, double) - Constructor for class org.hipparchus.optim.SimplePointChecker: Build an instance with specified thresholds.
SimplePointChecker(double, double, int) - Constructor for class org.hipparchus.optim.SimplePointChecker: Builds an instance with specified thresholds.
SimpleUnivariateValueChecker - Class in org.hipparchus.optim.univariate: Simple implementation of the ConvergenceChecker interface that uses only objective function values.
SimpleUnivariateValueChecker(double, double) - Constructor for class org.hipparchus.optim.univariate.SimpleUnivariateValueChecker: Build an instance with specified thresholds.
SimpleUnivariateValueChecker(double, double, int) - Constructor for class org.hipparchus.optim.univariate.SimpleUnivariateValueChecker: Builds an instance with specified thresholds.
SimpleValueChecker - Class in org.hipparchus.optim: Simple implementation of the ConvergenceChecker interface using only objective function values.
SimpleValueChecker(double, double) - Constructor for class org.hipparchus.optim.SimpleValueChecker: Build an instance with specified thresholds.
SimpleValueChecker(double, double, int) - Constructor for class org.hipparchus.optim.SimpleValueChecker: Builds an instance with specified thresholds.
SimpleVectorValueChecker - Class in org.hipparchus.optim: Simple implementation of the ConvergenceChecker interface using only objective function values.
SimpleVectorValueChecker(double, double) - Constructor for class org.hipparchus.optim.SimpleVectorValueChecker: Build an instance with specified thresholds.
SimpleVectorValueChecker(double, double, int) - Constructor for class org.hipparchus.optim.SimpleVectorValueChecker: Builds an instance with specified tolerance thresholds and iteration count.
SIMPLEX_NEED_ONE_POINT - Enum constant in enum org.hipparchus.optim.LocalizedOptimFormats: SIMPLEX_NEED_ONE_POINT.
SimplexOptimizer - Class in org.hipparchus.optim.nonlinear.scalar.noderiv: This class implements simplex-based direct search optimization.
SimplexOptimizer(double, double) - Constructor for class org.hipparchus.optim.nonlinear.scalar.noderiv.SimplexOptimizer: Simple constructor.
SimplexOptimizer(ConvergenceChecker<PointValuePair>) - Constructor for class org.hipparchus.optim.nonlinear.scalar.noderiv.SimplexOptimizer: Simple constructor.
SimplexSolver - Class in org.hipparchus.optim.linear: Solves a linear problem using the "Two-Phase Simplex" method.
SimplexSolver() - Constructor for class org.hipparchus.optim.linear.SimplexSolver: Builds a simplex solver with default settings.
SimplexSolver(double) - Constructor for class org.hipparchus.optim.linear.SimplexSolver: Builds a simplex solver with a specified accepted amount of error.
SimplexSolver(double, int) - Constructor for class org.hipparchus.optim.linear.SimplexSolver: Builds a simplex solver with a specified accepted amount of error.
SimplexSolver(double, int, double) - Constructor for class org.hipparchus.optim.linear.SimplexSolver: Builds a simplex solver with a specified accepted amount of error.
SolutionCallback - Class in org.hipparchus.optim.linear: A callback object that can be provided to a linear optimizer to keep track of the best solution found.
SolutionCallback() - Constructor for class org.hipparchus.optim.linear.SolutionCallback: Empty constructor.
solve(RealVector, RealVector) - Method in class org.hipparchus.optim.nonlinear.vector.constrained.ADMMQPKKT: Solve Karush–Kuhn–Tucker equation from given right hand value.
solve(RealVector, RealVector) - Method in interface org.hipparchus.optim.nonlinear.vector.constrained.KarushKuhnTuckerSolver: Solve Karush–Kuhn–Tucker equation from given right hand value.
solvePhase1(SimplexTableau) - Method in class org.hipparchus.optim.linear.SimplexSolver: Solves Phase 1 of the Simplex method.
SQPOptimizerGM - Class in org.hipparchus.optim.nonlinear.vector.constrained: Sequential Quadratic Programming Optimizer.
SQPOptimizerGM() - Constructor for class org.hipparchus.optim.nonlinear.vector.constrained.SQPOptimizerGM
SQPOptimizerS - Class in org.hipparchus.optim.nonlinear.vector.constrained: Sequential Quadratic Programming Optimizer.
SQPOptimizerS() - Constructor for class org.hipparchus.optim.nonlinear.vector.constrained.SQPOptimizerS
SQPOption - Class in org.hipparchus.optim.nonlinear.vector.constrained: Parameter for SQP Algorithm.
SQPOption() - Constructor for class org.hipparchus.optim.nonlinear.vector.constrained.SQPOption: Simple constructor.
start(double[]) - Method in class org.hipparchus.optim.nonlinear.vector.leastsquares.LeastSquaresBuilder: Configure the initial guess.
start(RealVector) - Method in class org.hipparchus.optim.nonlinear.vector.leastsquares.LeastSquaresBuilder: Configure the initial guess.
store(PointValuePair) - Method in class org.hipparchus.optim.nonlinear.scalar.MultiStartMultivariateOptimizer: Method that will be called in order to store each found optimum.
store(P) - Method in class org.hipparchus.optim.BaseMultiStartMultivariateOptimizer: Method that will be called in order to store each found optimum.

T

target(double[]) - Method in class org.hipparchus.optim.nonlinear.vector.leastsquares.LeastSquaresBuilder: Configure the observed data.
target(RealVector) - Method in class org.hipparchus.optim.nonlinear.vector.leastsquares.LeastSquaresBuilder: Configure the observed data.
TOO_SMALL_COST_RELATIVE_TOLERANCE - Enum constant in enum org.hipparchus.optim.LocalizedOptimFormats: TOO_SMALL_COST_RELATIVE_TOLERANCE.
TOO_SMALL_ORTHOGONALITY_TOLERANCE - Enum constant in enum org.hipparchus.optim.LocalizedOptimFormats: TOO_SMALL_ORTHOGONALITY_TOLERANCE.
TOO_SMALL_PARAMETERS_RELATIVE_TOLERANCE - Enum constant in enum org.hipparchus.optim.LocalizedOptimFormats: TOO_SMALL_PARAMETERS_RELATIVE_TOLERANCE.
toString() - Method in enum org.hipparchus.optim.linear.Relationship
toString() - Method in class org.hipparchus.optim.nonlinear.vector.leastsquares.GaussNewtonOptimizer
toString() - Method in class org.hipparchus.optim.nonlinear.vector.leastsquares.SequentialGaussNewtonOptimizer
TRUST_REGION_STEP_FAILED - Enum constant in enum org.hipparchus.optim.LocalizedOptimFormats: TRUST_REGION_STEP_FAILED.
TwiceDifferentiableFunction - Class in org.hipparchus.optim.nonlinear.vector.constrained: A MultivariateFunction that also has a defined gradient and Hessian.
TwiceDifferentiableFunction() - Constructor for class org.hipparchus.optim.nonlinear.vector.constrained.TwiceDifferentiableFunction

U

UNABLE_TO_PERFORM_QR_DECOMPOSITION_ON_JACOBIAN - Enum constant in enum org.hipparchus.optim.LocalizedOptimFormats: UNABLE_TO_PERFORM_QR_DECOMPOSITION_ON_JACOBIAN.
UNABLE_TO_SOLVE_SINGULAR_PROBLEM - Enum constant in enum org.hipparchus.optim.LocalizedOptimFormats: UNABLE_TO_SOLVE_SINGULAR_PROBLEM.
unbounded(int) - Static method in class org.hipparchus.optim.SimpleBounds: Factory method that creates instance of this class that represents unbounded ranges.
UNBOUNDED_SOLUTION - Enum constant in enum org.hipparchus.optim.LocalizedOptimFormats: UNBOUNDED_SOLUTION.
unboundedToBounded(double[]) - Method in class org.hipparchus.optim.nonlinear.scalar.MultivariateFunctionMappingAdapter: Maps an array from unbounded to bounded.
UnivariateObjectiveFunction - Class in org.hipparchus.optim.univariate: Scalar function to be optimized.
UnivariateObjectiveFunction(UnivariateFunction) - Constructor for class org.hipparchus.optim.univariate.UnivariateObjectiveFunction: Simple constructor.
UnivariateOptimizer - Class in org.hipparchus.optim.univariate: Base class for a univariate scalar function optimizer.
UnivariateOptimizer(ConvergenceChecker<UnivariatePointValuePair>) - Constructor for class org.hipparchus.optim.univariate.UnivariateOptimizer: Simple constructor.
UnivariatePointValuePair - Class in org.hipparchus.optim.univariate: This class holds a point and the value of an objective function at this point.
UnivariatePointValuePair(double, double) - Constructor for class org.hipparchus.optim.univariate.UnivariatePointValuePair: Build a point/objective function value pair.
unlimited() - Static method in class org.hipparchus.optim.MaxEval: Factory method that creates instance of this class that represents a virtually unlimited number of evaluations.
unlimited() - Static method in class org.hipparchus.optim.MaxIter: Factory method that creates instance of this class that represents a virtually unlimited number of iterations.
unscaleX(RealVector) - Method in class org.hipparchus.optim.nonlinear.vector.constrained.ADMMQPModifiedRuizEquilibrium: Unscale solution vector.
unscaleY(RealVector) - Method in class org.hipparchus.optim.nonlinear.vector.constrained.ADMMQPModifiedRuizEquilibrium: Unscale Y vector.
unscaleZ(RealVector) - Method in class org.hipparchus.optim.nonlinear.vector.constrained.ADMMQPModifiedRuizEquilibrium: Unscale Z vector.
updateRho() - Method in class org.hipparchus.optim.nonlinear.vector.constrained.ADMMQPOption: Check if weight updating is enabled.
updateSigmaRho(double, int, double) - Method in class org.hipparchus.optim.nonlinear.vector.constrained.ADMMQPKKT: Update steps
useFunHessian() - Method in class org.hipparchus.optim.nonlinear.vector.constrained.SQPOption: Check if using direct the function Hessian is enabled or disabled.

V

validate(RealVector) - Method in interface org.hipparchus.optim.nonlinear.vector.leastsquares.ParameterValidator: Validates the set of parameters.
value(double[]) - Method in class org.hipparchus.optim.linear.LinearObjectiveFunction: Computes the value of the linear equation at the current point.
value(double[]) - Method in class org.hipparchus.optim.nonlinear.scalar.LeastSquaresConverter
value(double[]) - Method in class org.hipparchus.optim.nonlinear.scalar.MultivariateFunctionMappingAdapter: Compute the underlying function value from an unbounded point.
value(double[]) - Method in class org.hipparchus.optim.nonlinear.scalar.MultivariateFunctionPenaltyAdapter: Computes the underlying function value from an unbounded point.
value(double[]) - Method in class org.hipparchus.optim.nonlinear.vector.constrained.LinearBoundedConstraint: Returns the value of this function at (x)
value(double[]) - Method in class org.hipparchus.optim.nonlinear.vector.constrained.TwiceDifferentiableFunction: Returns the value of this function at (x)
value(double[]) - Method in interface org.hipparchus.optim.nonlinear.vector.constrained.VectorDifferentiableFunction: Returns the value of this function at (x)
value(RealVector) - Method in class org.hipparchus.optim.linear.LinearObjectiveFunction: Computes the value of the linear equation at the current point.
value(RealVector) - Method in class org.hipparchus.optim.nonlinear.vector.constrained.LinearBoundedConstraint: Returns the value of this function at (x)
value(RealVector) - Method in class org.hipparchus.optim.nonlinear.vector.constrained.LinearEqualityConstraint
value(RealVector) - Method in class org.hipparchus.optim.nonlinear.vector.constrained.LinearInequalityConstraint
value(RealVector) - Method in class org.hipparchus.optim.nonlinear.vector.constrained.QuadraticFunction: Returns the value of this function at (x)
value(RealVector) - Method in class org.hipparchus.optim.nonlinear.vector.constrained.TwiceDifferentiableFunction: Returns the value of this function at (x)
value(RealVector) - Method in interface org.hipparchus.optim.nonlinear.vector.constrained.VectorDifferentiableFunction: Returns the value of this function at (x)
value(RealVector) - Method in interface org.hipparchus.optim.nonlinear.vector.leastsquares.MultivariateJacobianFunction: Compute the function value and its Jacobian.
ValueAndJacobianFunction - Interface in org.hipparchus.optim.nonlinear.vector.leastsquares: A interface for functions that compute a vector of values and can compute their derivatives (Jacobian).
valueOf(String) - Static method in enum org.hipparchus.optim.linear.PivotSelectionRule: Returns the enum constant of this type with the specified name.
valueOf(String) - Static method in enum org.hipparchus.optim.linear.Relationship: Returns the enum constant of this type with the specified name.
valueOf(String) - Static method in enum org.hipparchus.optim.LocalizedOptimFormats: Returns the enum constant of this type with the specified name.
valueOf(String) - Static method in enum org.hipparchus.optim.nonlinear.scalar.GoalType: Returns the enum constant of this type with the specified name.
valueOf(String) - Static method in enum org.hipparchus.optim.nonlinear.scalar.gradient.NonLinearConjugateGradientOptimizer.Formula: Returns the enum constant of this type with the specified name.
values() - Static method in enum org.hipparchus.optim.linear.PivotSelectionRule: Returns an array containing the constants of this enum type, in the order they are declared.
values() - Static method in enum org.hipparchus.optim.linear.Relationship: Returns an array containing the constants of this enum type, in the order they are declared.
values() - Static method in enum org.hipparchus.optim.LocalizedOptimFormats: Returns an array containing the constants of this enum type, in the order they are declared.
values() - Static method in enum org.hipparchus.optim.nonlinear.scalar.GoalType: Returns an array containing the constants of this enum type, in the order they are declared.
values() - Static method in enum org.hipparchus.optim.nonlinear.scalar.gradient.NonLinearConjugateGradientOptimizer.Formula: Returns an array containing the constants of this enum type, in the order they are declared.
VectorDifferentiableFunction - Interface in org.hipparchus.optim.nonlinear.vector.constrained: A MultivariateFunction that also has a defined gradient and Hessian.

W

weight(RealMatrix) - Method in class org.hipparchus.optim.nonlinear.vector.leastsquares.LeastSquaresBuilder: Configure the weight matrix.
weightDiagonal(LeastSquaresProblem, RealVector) - Static method in class org.hipparchus.optim.nonlinear.vector.leastsquares.LeastSquaresFactory: Apply a diagonal weight matrix to the LeastSquaresProblem.
weightMatrix(LeastSquaresProblem, RealMatrix) - Static method in class org.hipparchus.optim.nonlinear.vector.leastsquares.LeastSquaresFactory: Apply a dense weight matrix to the LeastSquaresProblem.
withAPrioriData(RealVector, RealMatrix) - Method in class org.hipparchus.optim.nonlinear.vector.leastsquares.SequentialGaussNewtonOptimizer: Configure from a priori state and covariance.
withAPrioriData(RealVector, RealMatrix, double, double) - Method in class org.hipparchus.optim.nonlinear.vector.leastsquares.SequentialGaussNewtonOptimizer: Configure from a priori state and covariance.
withCostRelativeTolerance(double) - Method in class org.hipparchus.optim.nonlinear.vector.leastsquares.LevenbergMarquardtOptimizer: Build new instance with cost relative tolerance.
withDecomposer(MatrixDecomposer) - Method in class org.hipparchus.optim.nonlinear.vector.leastsquares.GaussNewtonOptimizer: Configure the matrix decomposition algorithm.
withDecomposer(MatrixDecomposer) - Method in class org.hipparchus.optim.nonlinear.vector.leastsquares.SequentialGaussNewtonOptimizer: Configure the matrix decomposition algorithm.
withEvaluation(LeastSquaresProblem.Evaluation) - Method in class org.hipparchus.optim.nonlinear.vector.leastsquares.SequentialGaussNewtonOptimizer: Configure the previous evaluation used by the optimizer.
withFormNormalEquations(boolean) - Method in class org.hipparchus.optim.nonlinear.vector.leastsquares.GaussNewtonOptimizer: Configure if the normal equations should be explicitly formed.
withFormNormalEquations(boolean) - Method in class org.hipparchus.optim.nonlinear.vector.leastsquares.SequentialGaussNewtonOptimizer: Configure if the normal equations should be explicitly formed.
withInitialStepBoundFactor(double) - Method in class org.hipparchus.optim.nonlinear.vector.leastsquares.LevenbergMarquardtOptimizer: Build new instance with initial step bound factor.
withOrthoTolerance(double) - Method in class org.hipparchus.optim.nonlinear.vector.leastsquares.LevenbergMarquardtOptimizer: Build new instance with ortho tolerance.
withParameterRelativeTolerance(double) - Method in class org.hipparchus.optim.nonlinear.vector.leastsquares.LevenbergMarquardtOptimizer: Build new instance with parameter relative tolerance.
withRankingThreshold(double) - Method in class org.hipparchus.optim.nonlinear.vector.leastsquares.LevenbergMarquardtOptimizer: Build new instance with ranking threshold.

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