Class Scine::Utils::EigenvectorFollowing

class EigenvectorFollowing : public Scine::Utils::Optimizer

An implementation of an Eigenvector following optimization algorithm.

This algorithm is intended to find the maximum along one single eigenvector and the minimum along all other eigenvectors of a given system/Hessian.

Public Functions

EigenvectorFollowing()

Default constructor.

template<class UpdateFunction>
int optimize(Eigen::VectorXd &parameters, UpdateFunction &&function, GradientBasedCheck &check)

The main routine of the optimizer that carries out the actual optimization.

Return

int Returns the number of optimization cycles carried out until the conclusion of the optimization function.

Template Parameters

  • UpdateFunction: A lambda function with a void return value, and the arguments:

    1. const Eigen::VectorXd& parameters

    2. double& value

    3. Eigen::VectorXd& gradients

Parameters

  • parameters: The parameters to be optimized.

  • function: The function to be evaluated in order to get values and gradients for a given set of parameters.

  • check: The ConvergenceCheck to be used in order to determine when the optimization is finished or should stop for other reasons.

virtual void addSettingsDescriptors(UniversalSettings::DescriptorCollection &collection) const

Adds all relevant options to the given UniversalSettings::DescriptorCollection thus expanding it to include the optimizers’s options.

Parameters

  • collection: The DescriptorCollection to which new fields shall be added.

virtual void applySettings(const Settings &settings)

Updates theoptimizers’s options with those values given in the Settings.

Parameters

  • settings: The settings to update the option of the steepest descent with.

Public Members

double trustRadius = 0.5

The maximum RMS of a taken step.