Uses of Interface
org.hipparchus.optim.nonlinear.vector.leastsquares.LeastSquaresProblem.Evaluation
Packages that use LeastSquaresProblem.Evaluation
Package
Description
This package provides algorithms that minimize the residuals
between observations and model values.
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Uses of LeastSquaresProblem.Evaluation in org.hipparchus.optim.nonlinear.vector.leastsquares
Subinterfaces of LeastSquaresProblem.Evaluation in org.hipparchus.optim.nonlinear.vector.leastsquaresModifier and TypeInterfaceDescriptionstatic interface
The optimum found by the optimizer.Classes in org.hipparchus.optim.nonlinear.vector.leastsquares that implement LeastSquaresProblem.EvaluationModifier and TypeClassDescriptionclass
An implementation ofLeastSquaresProblem.Evaluation
that is designed for extension.Methods in org.hipparchus.optim.nonlinear.vector.leastsquares that return LeastSquaresProblem.EvaluationModifier and TypeMethodDescriptionLeastSquaresAdapter.evaluate
(RealVector point) Evaluate the model at the specified point.LeastSquaresProblem.evaluate
(RealVector point) Evaluate the model at the specified point.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.EvaluationModifier and TypeMethodDescriptionLeastSquaresFactory.evaluationChecker
(ConvergenceChecker<PointVectorValuePair> checker) View a convergence checker specified for aPointVectorValuePair
as one specified for anLeastSquaresProblem.Evaluation
.LeastSquaresAdapter.getConvergenceChecker()
Gets the convergence checker.Methods in org.hipparchus.optim.nonlinear.vector.leastsquares with parameters of type LeastSquaresProblem.EvaluationModifier and TypeMethodDescriptionboolean
EvaluationRmsChecker.converged
(int iteration, LeastSquaresProblem.Evaluation previous, LeastSquaresProblem.Evaluation current) Check if the optimization algorithm has converged.LeastSquaresOptimizer.Optimum.of
(LeastSquaresProblem.Evaluation value, int evaluations, int iterations) Create a new optimum from an evaluation and the values of the counters.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.EvaluationModifier and TypeMethodDescriptionLeastSquaresBuilder.checker
(ConvergenceChecker<LeastSquaresProblem.Evaluation> newChecker) Configure the convergence checker.static LeastSquaresProblem
LeastSquaresFactory.create
(MultivariateVectorFunction model, MultivariateMatrixFunction jacobian, double[] observed, double[] start, RealMatrix weight, ConvergenceChecker<LeastSquaresProblem.Evaluation> checker, int maxEvaluations, int maxIterations) Create aLeastSquaresProblem
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 aLeastSquaresProblem
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 aLeastSquaresProblem
from the given elements.static LeastSquaresProblem
LeastSquaresFactory.create
(MultivariateJacobianFunction model, RealVector observed, RealVector start, ConvergenceChecker<LeastSquaresProblem.Evaluation> checker, int maxEvaluations, int maxIterations) Create aLeastSquaresProblem
from the given elements.Constructors in org.hipparchus.optim.nonlinear.vector.leastsquares with parameters of type LeastSquaresProblem.EvaluationModifierConstructorDescriptionSequentialGaussNewtonOptimizer
(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.