Class Scine::Utils::GeometryOptimizerBase

class GeometryOptimizerBase

The base class for all Geometry optimizers.

The purpose of the geometry optimizers is to wrap technical details needed for the actual optimization into a class that is easily used and has a small set of data needed in its constructor, is mainly configured via its settings, and then exposes the optimization through a simple function accepting a geometry (AtomCollection)

The main purpose of this base class is to hide the template parameter(s) of the derived class(es).

Subclassed by Scine::Utils::GeometryOptimizer< OptimizerType >

Public Functions

GeometryOptimizerBase()

Default constructor.

virtual ~GeometryOptimizerBase()

Virtual default destructor.

virtual int optimize(AtomCollection &atoms) = 0

The main functionality of the geometry optimizer.

This function wraps the optimize functions of the underlying optimizer.

Return

int The final number of optimization cycles carried out.

Parameters

virtual void setSettings(const Settings &settings) = 0

Function to apply the given settings to underlying classes.

Parameters

  • settings: The new settings.

virtual Settings getSettings() const = 0

Get the public settings as a Utils::Settings object.

Return

Settings A settings object with the current settings.

virtual void addObserver(std::function<void(const int&, const double&, const Eigen::VectorXd&)> function) = 0

Add an observer function that will be triggered in each iteration.

Parameters

  • function: A function to be executed in every loop of the optimization. The function will have access to the current cycle count, the current value and to a const reference of the current parameters.

virtual void clearObservers() = 0

Clear all existing observer functions.

For optimization problems with very fast evaluations of the underlying function the removal of all observers can increase performance as the observers are given as std::functions and can not be added via templates.

Public Members

bool transformCoordinates = true

Switch to transform the coordinates from Cartesian into an internal space.

The optimization will be carried out in the internal coordinate space possibly accellerating convergence.