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//Copyright © 2009 Jose Antonio De Santiago-Castillo
//E-mail:JAntonioDeSantiago@gmail.com
//Web: www.DotNumerics.com
//
#endregion
using System;
using System.Collections.Generic;
using System.Text;
using DotNumerics.Optimization.LBFGSB;
namespace DotNumerics.Optimization
{
/// <summary>
/// Class used to minimizes a function of several variables by using Limited memory Broyden–Fletcher–Goldfarb–Shanno (L-BFGS) method. This class can be used for unconstrained and bounded constrained minimization.
/// </summary>
public class L_BFGS_B : xMinimizationBase
{
#region Fields
LBFGSBDriver _Driver = new LBFGSBDriver();
/// <summary>
/// Accuracy factor
/// </summary>
private double _AccuracyFactor = 1E7;
#endregion
#region Constructor
/// <summary>
/// Initializes a new instance of the L_BFG_S class.
/// </summary>
public L_BFGS_B()
{
}
#endregion
#region Properties
/// <summary>
/// Accuracy factor. The iteration will stop when (f^k - f^{k+1})/max{|f^k|,|f^{k+1}|,1} is least than AccuracyFactor*epsmch
/// where epsmch is the machine precision. Typical values for AccuracyFactor: 1E12 for low accuracy; 1E7 for moderate accuracy;
/// 1E1 for extremely high accuracy.
/// </summary>
public double AccuracyFactor
{
get { return _AccuracyFactor; }
set { _AccuracyFactor = value; }
}
#endregion
#region Public methods
/// <summary>
/// Computes the minimum point of a function of several variables.
/// </summary>
/// <param name="function">The function to minimize.</param>
/// <param name="gradient">A delegate that computes the gradient.</param>
/// <param name="initialGuess">Array of size N containing the initial guess. N is the number of variables.</param>
/// <returns>Array containing the solution.</returns>
public double[] ComputeMin(OptMultivariateFunction function, OptMultivariateGradient gradient, double[] initialGuess)
{
if (initialGuess == null) return new double[0];
if (initialGuess.Length == 0) return new double[0];
OptVariable[] variables = this.GetVariables(initialGuess);
int maxFunc = this._MaxFunEvaluations;
double[] minimum= this._Driver.ComputeMin(function, gradient, variables, this._Tolerance, this._AccuracyFactor, ref maxFunc);
this._FunEvaluations = maxFunc;
return minimum;
}
/// <summary>
/// Computes the minimum point of a function of several variables.
/// </summary>
/// <param name="function">The function to minimize.</param>
/// <param name="gradient">A delegate that computes the gradient.</param>
/// <param name="variables">Array of size N containing the varaibles.</param>
/// <returns>Array containing the solution.</returns>
public double[] ComputeMin(OptMultivariateFunction function, OptMultivariateGradient gradient, OptVariable[] variables)
{
if (variables == null) return new double[0];
if (variables.Length == 0) return new double[0];
int maxFunc = this._MaxFunEvaluations;
double[] minimum = this._Driver.ComputeMin(function, gradient, variables, this._Tolerance, this._AccuracyFactor, ref maxFunc);
this._FunEvaluations = maxFunc;
return minimum;
}
/// <summary>
/// Computes the minimum point of a function of several variables.
/// </summary>
/// <param name="function">The function to minimize.</param>
/// <param name="gradient">A delegate that computes the gradient.</param>
/// <param name="variables">Array of size N containing the varaibles.</param>
/// <returns>Array containing the solution.</returns>
public double[] ComputeMin(OptMultivariateFunction function, OptMultivariateGradient gradient, OptBoundVariable[] variables)
{
if (variables == null) return new double[0];
if (variables.Length == 0) return new double[0];
int maxFunc = this._MaxFunEvaluations;
double[] minimum = this._Driver.ComputeMin(function, gradient, variables, this._Tolerance, this._AccuracyFactor, ref maxFunc);
this._FunEvaluations = maxFunc;
return minimum;
}
#endregion
#region private Methods
private OptVariable[] GetVariables(double[] variablesArray)
{
OptVariable[] vars = new OptVariable[variablesArray.Length];
for (int i = 0; i < variablesArray.Length; i++)
{
vars[i] = new OptVariable(variablesArray[i]);
}
return vars;
}
#endregion
}
}
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