Conjugate Gradient exit codes. More...
#include <FgSCG.h>
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... | |
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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... | |
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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... | |
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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... | |
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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 | |
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int | maxIterations |
Scalar | _tolerance |
int | iterations |
ILineSearch< Scalar > * | linesearch |
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NonlinearObjectiveFunction< Scalar > * | _function |
af::array | _x |
bool | _display |
af::dtype | m_dtype |
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:
NeuralEngine::MachineLearning::ScaledConjugateGradient< Scalar, LSType >::ScaledConjugateGradient | ( | int | numberOfVariables | ) |
Creates a new instance of the CG optimization algorithm.
Admin, 3/27/2017.
numberOfVariables | The number of free parameters in the optimization problem. |
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.
numberOfVariables | The 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. |
NeuralEngine::MachineLearning::ScaledConjugateGradient< Scalar, LSType >::ScaledConjugateGradient | ( | NonlinearObjectiveFunction< Scalar > * | function | ) |
Creates a new instance of the CG optimization algorithm.
Admin, 3/27/2017.
function | The objective function and gradients whose optimum values should be found. |
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overrideprotectedvirtual |
Implements the actual optimization algorithm. This method should try to minimize the objective function.
Hmetal T, 11.04.2017.
Implements NeuralEngine::MachineLearning::BaseOptimizationMethod< Scalar >.