Uses of Class
net.finmath.optimizer.LevenbergMarquardt
Package
Description
This package provides classes with numerical algorithm for optimization of
an objective function and a factory to easy construction of the optimizers.
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Uses of LevenbergMarquardt in net.finmath.optimizer
Modifier and TypeMethodDescriptionLevenbergMarquardt.clone()
Create a clone of this LevenbergMarquardt optimizer.LevenbergMarquardt.getCloneWithModifiedTargetValues
(double[] newTargetVaues, double[] newWeights, boolean isUseBestParametersAsInitialParameters) Create a clone of this LevenbergMarquardt optimizer with a new vector for the target values and weights.LevenbergMarquardt.getCloneWithModifiedTargetValues
(List<Number> newTargetVaues, List<Number> newWeights, boolean isUseBestParametersAsInitialParameters) Create a clone of this LevenbergMarquardt optimizer with a new vector for the target values and weights.LevenbergMarquardt.setErrorTolerance
(double errorTolerance) Set the error tolerance.LevenbergMarquardt.setInitialParameters
(double[] initialParameters) Set the initial parameters for the solver.LevenbergMarquardt.setLambda
(double lambda) Set the parameter λ used in the Tikhonov-like regularization of the Hessian matrix, that is the \( \lambda \) in \( H + \lambda \diag H \).LevenbergMarquardt.setMaxIteration
(int maxIteration) Set the maximum number of iterations to be performed until the solver gives up.LevenbergMarquardt.setParameterSteps
(double[] parameterSteps) Set the parameter step for the solver.LevenbergMarquardt.setTargetValues
(double[] targetValues) Set the target values for the solver.LevenbergMarquardt.setWeights
(double[] weights) Set the weight for the objective function.