Uses of Interface
org.hipparchus.optim.nonlinear.vector.leastsquares.LeastSquaresProblem.Evaluation

Packages that use LeastSquaresProblem.Evaluation

Package	Description
org.hipparchus.optim.nonlinear.vector.leastsquares	This package provides algorithms that minimize the residuals between observations and model values.

Uses of LeastSquaresProblem.Evaluation in org.hipparchus.optim.nonlinear.vector.leastsquares

Subinterfaces of LeastSquaresProblem.Evaluation in org.hipparchus.optim.nonlinear.vector.leastsquares

Modifier and Type	Interface and Description
static interface	LeastSquaresOptimizer.Optimum The optimum found by the optimizer.

Classes in org.hipparchus.optim.nonlinear.vector.leastsquares that implement LeastSquaresProblem.Evaluation

Modifier and Type	Class and Description
class	AbstractEvaluation An implementation of LeastSquaresProblem.Evaluation that is designed for extension.

Methods in org.hipparchus.optim.nonlinear.vector.leastsquares that return LeastSquaresProblem.Evaluation

Modifier and Type	Method and Description
LeastSquaresProblem.Evaluation	LeastSquaresProblem.evaluate(RealVector point) Evaluate the model at the specified point.
LeastSquaresProblem.Evaluation	LeastSquaresAdapter.evaluate(RealVector point) Evaluate the model at the specified point.
LeastSquaresProblem.Evaluation	SequentialGaussNewtonOptimizer.getOldEvaluation() Get the previous evaluation used by the optimizer.

Methods in org.hipparchus.optim.nonlinear.vector.leastsquares that return types with arguments of type LeastSquaresProblem.Evaluation

Modifier and Type	Method and Description
static ConvergenceChecker<LeastSquaresProblem.Evaluation>	LeastSquaresFactory.evaluationChecker(ConvergenceChecker<PointVectorValuePair> checker) View a convergence checker specified for a PointVectorValuePair as one specified for an LeastSquaresProblem.Evaluation.
ConvergenceChecker<LeastSquaresProblem.Evaluation>	LeastSquaresAdapter.getConvergenceChecker() Gets the convergence checker.

Methods in org.hipparchus.optim.nonlinear.vector.leastsquares with parameters of type LeastSquaresProblem.Evaluation

Modifier and Type	Method and Description
boolean	EvaluationRmsChecker.converged(int iteration, LeastSquaresProblem.Evaluation previous, LeastSquaresProblem.Evaluation current) Check if the optimization algorithm has converged.
static LeastSquaresOptimizer.Optimum	LeastSquaresOptimizer.Optimum.of(LeastSquaresProblem.Evaluation value, int evaluations, int iterations) Create a new optimum from an evaluation and the values of the counters.
SequentialGaussNewtonOptimizer	SequentialGaussNewtonOptimizer.withEvaluation(LeastSquaresProblem.Evaluation previousEvaluation) Configure the previous evaluation used by the optimizer.

Method parameters in org.hipparchus.optim.nonlinear.vector.leastsquares with type arguments of type LeastSquaresProblem.Evaluation

Modifier and Type	Method and Description
LeastSquaresBuilder	LeastSquaresBuilder.checker(ConvergenceChecker<LeastSquaresProblem.Evaluation> newChecker) Configure the convergence checker.
static LeastSquaresProblem	LeastSquaresFactory.create(MultivariateJacobianFunction model, RealVector observed, RealVector start, ConvergenceChecker<LeastSquaresProblem.Evaluation> checker, int maxEvaluations, int maxIterations) Create a LeastSquaresProblem from the given elements.
static LeastSquaresProblem	LeastSquaresFactory.create(MultivariateJacobianFunction model, RealVector observed, RealVector start, RealMatrix weight, ConvergenceChecker<LeastSquaresProblem.Evaluation> checker, int maxEvaluations, int maxIterations) Create a LeastSquaresProblem from the given elements.
static LeastSquaresProblem	LeastSquaresFactory.create(MultivariateJacobianFunction model, RealVector observed, RealVector start, RealMatrix weight, ConvergenceChecker<LeastSquaresProblem.Evaluation> checker, int maxEvaluations, int maxIterations, boolean lazyEvaluation, ParameterValidator paramValidator) Create a LeastSquaresProblem from the given elements.
static LeastSquaresProblem	LeastSquaresFactory.create(MultivariateVectorFunction model, MultivariateMatrixFunction jacobian, double[] observed, double[] start, RealMatrix weight, ConvergenceChecker<LeastSquaresProblem.Evaluation> checker, int maxEvaluations, int maxIterations) Create a LeastSquaresProblem from the given elements.

Constructors in org.hipparchus.optim.nonlinear.vector.leastsquares with parameters of type LeastSquaresProblem.Evaluation

Constructor and Description
SequentialGaussNewtonOptimizer(MatrixDecomposer decomposer, boolean formNormalEquations, LeastSquaresProblem.Evaluation evaluation) Create a sequential Gauss Newton optimizer that uses the given matrix decomposition algorithm to solve the normal equations.