NeuralEngine
A Game Engine with embeded Machine Learning algorithms based on Gaussian Processes.
NeuralEngine::MachineLearning::ScaledConjugateGradient< Scalar, LSType > Class Template Reference

Conjugate Gradient exit codes. More...

#include <FgSCG.h>

Inheritance diagram for NeuralEngine::MachineLearning::ScaledConjugateGradient< Scalar, LSType >:
Collaboration diagram for NeuralEngine::MachineLearning::ScaledConjugateGradient< Scalar, LSType >:

Public Member Functions

 ScaledConjugateGradient (int numberOfVariables)
 Creates a new instance of the CG optimization algorithm. More...
 
 ScaledConjugateGradient (int numberOfVariables, std::function< Scalar(const af::array &, af::array &)> function)
 Creates a new instance of the CG optimization algorithm. More...
 
 ScaledConjugateGradient (NonlinearObjectiveFunction< Scalar > *function)
 Creates a new instance of the CG optimization algorithm. More...
 
- Public Member Functions inherited from NeuralEngine::MachineLearning::BaseGradientOptimizationMethod< Scalar, MoreThuente >
Scalar GetTolerance ()
 Gets the relative difference threshold to be used as stopping criteria between two iterations. Default is 0 (iterate until convergence). More...
 
void SetTolerance (Scalar tolerance)
 Sets the relative difference threshold to be used as stopping criteria between two iterations. Default is 0 (iterate until convergence). More...
 
int GetMaxIterations ()
 Gets the maximum number of iterations to be performed during optimization. Default is 0 (iterate until convergence). More...
 
void SetMaxIterations (int iter)
 Sets the maximum number of iterations to be performed during optimization. Default is 0 (iterate until convergence). More...
 
int GetIterations ()
 Gets the number of iterations performed in the last call to IOptimizationMethod.Minimize(). More...
 
- Public Member Functions inherited from NeuralEngine::MachineLearning::BaseOptimizationMethod< Scalar >
virtual int GetNumberOfVariables ()
 Gets the number of variables (free parameters) in the optimization problem. More...
 
virtual af::array GetSolution ()
 Gets the current solution found, the values of the parameters which optimizes the function. More...
 
virtual void SetSolution (af::array &x)
 Sets the current solution found, the values of the parameters which optimizes the function. More...
 
virtual Scalar GetValue ()
 Gets the output of the function at the current Solution. More...
 
virtual bool Maximize (af::array &values, int *cycle=nullptr)
 Finds the maximum value of a function. The solution vector will be made available at the Solution property. More...
 
virtual bool Minimize (af::array &values, int *cycle=nullptr)
 Finds the minimum value of a function. The solution vector will be made available at the Solution property. More...
 
virtual bool Maximize (int *cycle=nullptr)
 Finds the maximum value of a function. The solution vector will be made available at the Solution property. More...
 
virtual bool Minimize (int *cycle=nullptr)
 Finds the minimum value of a function. The solution vector will be made available at the Solution property. More...
 
void Display (bool display)
 Set to display optimization information. More...
 
virtual int GetNumberOfVariables ()=0
 Gets the number of variables (free parameters) in the optimization problem. More...
 
virtual af::array GetSolution ()=0
 Gets the current solution found, the values of the parameters which optimizes the function. More...
 
virtual void SetSolution (af::array &x)=0
 Gets a solution. More...
 
virtual Scalar GetValue ()=0
 Gets the output of the function at the current Solution. More...
 
virtual bool Minimize (int *cycle=nullptr)=0
 Finds the minimum value of a function. The solution vector will be made available at the Solution property. More...
 
virtual bool Maximize (int *cycle=nullptr)=0
 Finds the maximum value of a function. The solution vector will be made available at the Solution property. More...
 

Protected Member Functions

virtual bool Optimize (int *cycle=nullptr) override
 Implements the actual optimization algorithm. This method should try to minimize the objective function. More...
 
- Protected Member Functions inherited from NeuralEngine::MachineLearning::BaseGradientOptimizationMethod< Scalar, MoreThuente >
 BaseGradientOptimizationMethod (int numberOfVariables)
 Initializes a new instance of the BaseGradientOptimizationMethod class. More...
 
 BaseGradientOptimizationMethod (int numberOfVariables, std::function< Scalar(const af::array &, af::array &)> function)
 Initializes a new instance of the BaseGradientOptimizationMethod class. More...
 
 BaseGradientOptimizationMethod (NonlinearObjectiveFunction< Scalar > *function)
 Initializes a new instance of the BaseGradientOptimizationMethod class. More...
 
void InitLinesearch ()
 Inits linesearch. More...
 
- Protected Member Functions inherited from NeuralEngine::MachineLearning::BaseOptimizationMethod< Scalar >
void SetValue (Scalar v)
 Sets the output of the function at the current Solution. More...
 
void SetNumberOfVariables (int n)
 Sets the number of variables (free parameters) in the optimization problem. More...
 
 BaseOptimizationMethod (int numberOfVariables)
 Initializes a new instance of the BaseOptimizationMethod class. More...
 
 BaseOptimizationMethod (int numberOfVariables, std::function< Scalar(const af::array &, af::array &)> function)
 Initializes a new instance of the BaseOptimizationMethod class. More...
 
 BaseOptimizationMethod (NonlinearObjectiveFunction< Scalar > *function)
 Initializes a new instance of the BaseOptimizationMethod class. More...
 
virtual bool Optimize (int *cycle=nullptr)=0
 Implements the actual optimization algorithm. This method should try to minimize the objective function. More...
 

Additional Inherited Members

- Protected Attributes inherited from NeuralEngine::MachineLearning::BaseGradientOptimizationMethod< Scalar, MoreThuente >
int maxIterations
 
Scalar _tolerance
 
int iterations
 
ILineSearch< Scalar > * linesearch
 
- Protected Attributes inherited from NeuralEngine::MachineLearning::BaseOptimizationMethod< Scalar >
NonlinearObjectiveFunction< Scalar > * _function
 
af::array _x
 
bool _display
 
af::dtype m_dtype
 

Detailed Description

template<typename Scalar, LineSearchType LSType = MoreThuente>
class NeuralEngine::MachineLearning::ScaledConjugateGradient< Scalar, LSType >

Conjugate Gradient exit codes.

Admin, 3/27/2017.

Scaled Conjugate Gradient (SCG) optimization method.

In mathematics, the conjugate gradient method is an algorithm for the numerical solution of particular systems of linear equations, namely those whose matrix is symmetric and positive- definite. The conjugate gradient method is an iterative method, so it can be applied to sparse systems that are too large to be handled by direct methods. Such systems often arise when numerically solving partial differential equations. The nonlinear conjugate gradient method generalizes the conjugate gradient method to nonlinear optimization (Wikipedia, 2011).

The framework implementation of this method is based on the original FORTRAN source code by Jorge Nocedal (see references below). The original FORTRAN source code of CG+ (for large scale unconstrained problems) is available at http://users.eecs.northwestern.edu/~nocedal/CG+.html and had been made freely available for educational or commercial use. The original authors expect that all publications describing work using this software quote the (Gilbert and Nocedal, 1992) reference given below.

References:

See also
BroydenFletcherGoldfarbShanno, ResilientBackpropagation, BoundedBroydenFletcherGoldfarbShanno, TrustRegionNewtonMethod

Definition at line 144 of file FgSCG.h.

Constructor & Destructor Documentation

◆ ScaledConjugateGradient() [1/3]

template<typename Scalar , LineSearchType LSType = MoreThuente>
NeuralEngine::MachineLearning::ScaledConjugateGradient< Scalar, LSType >::ScaledConjugateGradient ( int  numberOfVariables)

Creates a new instance of the CG optimization algorithm.

Admin, 3/27/2017.

Parameters
numberOfVariablesThe number of free parameters in the optimization problem.

◆ ScaledConjugateGradient() [2/3]

template<typename Scalar , LineSearchType LSType = MoreThuente>
NeuralEngine::MachineLearning::ScaledConjugateGradient< Scalar, LSType >::ScaledConjugateGradient ( int  numberOfVariables,
std::function< Scalar(const af::array &, af::array &)>  function 
)

Creates a new instance of the CG optimization algorithm.

Admin, 3/27/2017.

Parameters
numberOfVariablesThe number of free parameters in the function to be optimized.
function[in,out] The function to be optimized.
gradient[in,out] The gradient of the function.

◆ ScaledConjugateGradient() [3/3]

template<typename Scalar , LineSearchType LSType = MoreThuente>
NeuralEngine::MachineLearning::ScaledConjugateGradient< Scalar, LSType >::ScaledConjugateGradient ( NonlinearObjectiveFunction< Scalar > *  function)

Creates a new instance of the CG optimization algorithm.

Admin, 3/27/2017.

Parameters
functionThe objective function and gradients whose optimum values should be found.

Member Function Documentation

◆ Optimize()

template<typename Scalar , LineSearchType LSType = MoreThuente>
virtual bool NeuralEngine::MachineLearning::ScaledConjugateGradient< Scalar, LSType >::Optimize ( int cycle = nullptr)
overrideprotectedvirtual

Implements the actual optimization algorithm. This method should try to minimize the objective function.

Hmetal T, 11.04.2017.

Returns
true if it succeeds, false if it fails.

Implements NeuralEngine::MachineLearning::BaseOptimizationMethod< Scalar >.


The documentation for this class was generated from the following file: