Class Scine::Utils::SteepestDescent

class SteepestDescent : public Scine::Utils::Optimizer

An implementation of a steepest descent optimization algorithm.

The steepest descent algorithm is on of the most simple gradient based optimization algorithms. In each step the parameters are adjusted by subtracting the scaled negative gradient. The gradient is usually scaled by a factor that is smaller than 1.0 .

Public Functions

SteepestDescent()

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 steepest descent options.

Parameters

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

virtual void applySettings(const Settings &settings)

Updates the steepest descent’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 factor = 0.1

The scaling factor alpha to be used in the steepest descent algorithm.

The parameters \(\{x_i\}\) are generated as:

\[ x_{i,n+1} = x_i - \alpha * g_i \]