Uses of Interface
org.hipparchus.optim.nonlinear.vector.leastsquares.LeastSquaresProblem.Evaluation
Package
Description
This package provides algorithms that minimize the residuals
between observations and model values.
-
Uses of LeastSquaresProblem.Evaluation in org.hipparchus.optim.nonlinear.vector.leastsquares
Modifier and TypeInterfaceDescriptionstatic interface
The optimum found by the optimizer.Modifier and TypeClassDescriptionclass
An implementation ofLeastSquaresProblem.Evaluation
that is designed for extension.Modifier 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.Modifier 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.Modifier 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.Modifier 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.ModifierConstructorDescriptionSequentialGaussNewtonOptimizer
(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.