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/// ------------------------------------------------------
/// SwarmOps - Numeric and heuristic optimization for C#
/// Copyright (C) 2003-2011 Magnus Erik Hvass Pedersen.
/// Please see the file license.txt for license details.
/// SwarmOps on the internet: http://www.Hvass-Labs.org/
/// ------------------------------------------------------
using System.Diagnostics;
namespace SwarmOps.Optimizers
{
/// <summary>
/// Gradient Descent (GD), follows the gradient of
/// the problem in small steps.
/// </summary>
public class GD : Optimizer
{
#region Constructors.
/// <summary>
/// Construct the object.
/// </summary>
public GD()
: base()
{
}
/// <summary>
/// Construct the object.
/// </summary>
/// <param name="problem">Problem to optimize.</param>
public GD(Problem problem)
: base(problem)
{
}
#endregion
#region Get control parameters.
/// <summary>
/// Get parameter, Stepsize.
/// </summary>
/// <param name="parameters">Optimizer parameters.</param>
public double GetStepsize(double[] parameters)
{
return parameters[0];
}
#endregion
#region Base-class overrides, Problem.
/// <summary>
/// Name of the optimizer.
/// </summary>
public override string Name
{
get { return "GD"; }
}
/// <summary>
/// Number of control parameters for optimizer.
/// </summary>
public override int Dimensionality
{
get { return 1; }
}
string[] _parameterName = { "Stepsize" };
/// <summary>
/// Control parameter names.
/// </summary>
public override string[] ParameterName
{
get { return _parameterName ; }
}
static readonly double[] _defaultParameters = { 0.05 };
/// <summary>
/// Default control parameters.
/// </summary>
public override double[] DefaultParameters
{
get { return _defaultParameters; }
}
static readonly double[] _lowerBound = { 0 };
/// <summary>
/// Lower search-space boundary for control parameters.
/// </summary>
public override double[] LowerBound
{
get { return _lowerBound; }
}
static readonly double[] _upperBound = { 2.0 };
/// <summary>
/// Upper search-space boundary for control parameters.
/// </summary>
public override double[] UpperBound
{
get { return _upperBound; }
}
#endregion
#region Base-class overrides, Optimizer.
/// <summary>
/// Perform one optimization run and return the best found solution.
/// </summary>
/// <param name="parameters">Control parameters for the optimizer.</param>
public override Result Optimize(double[] parameters)
{
Debug.Assert(parameters != null && parameters.Length == Dimensionality);
// Signal beginning of optimization run.
Problem.BeginOptimizationRun();
// Retrieve parameter specific to GD method.
double stepsize = GetStepsize(parameters);
// Get problem-context.
double[] lowerBound = Problem.LowerBound;
double[] upperBound = Problem.UpperBound;
double[] lowerInit = Problem.LowerInit;
double[] upperInit = Problem.UpperInit;
int n = Problem.Dimensionality;
// Allocate agent position and search-range.
double[] x = new double[n]; // Current position.
double[] v = new double[n]; // Gradient/velocity.
double[] g = new double[n]; // Best-found position.
// Initialize agent-position in search-space.
Tools.InitializeUniform(ref x, lowerInit, upperInit);
// Enforce constraints and evaluate feasibility.
bool feasible = Problem.EnforceConstraints(ref x);
// Compute fitness of initial position.
// This counts as an iteration below.
double fitness = Problem.Fitness(x, feasible);
// This is the best-found position.
x.CopyTo(g, 0);
// Trace fitness of best found solution.
Trace(0, fitness, feasible);
int i;
for (i = 1; Problem.Continue(i, fitness, feasible); i++)
{
// Compute gradient.
int gradientIterations = Problem.Gradient(x, ref v);
// Compute norm of gradient-vector.
double gradientNorm = Tools.Norm(v);
// Compute current stepsize.
double normalizedStepsize = stepsize / gradientNorm;
// Move in direction of steepest descent.
for (int j = 0; j < n; j++)
{
x[j] -= normalizedStepsize * v[j];
}
// Enforce constraints and evaluate feasibility.
bool newFeasible = Problem.EnforceConstraints(ref x);
// Compute fitness if feasibility (constraint satisfaction) is same or better.
if (Tools.BetterFeasible(feasible, newFeasible))
{
// Compute new fitness.
double newFitness = Problem.Fitness(x, fitness, feasible, newFeasible);
// Update best position and fitness found in this run.
if (Tools.BetterFeasibleFitness(feasible, newFeasible, fitness, newFitness))
{
// Update this run's best known position.
x.CopyTo(g, 0);
// Update this run's best know fitness.
fitness = newFitness;
}
}
// Trace fitness of best found solution.
Trace(i, fitness, feasible);
// Add iterations for gradient computation.
// This is incompatible with FitnessTrace.
//i += gradientIterations;
}
// Signal end of optimization run.
Problem.EndOptimizationRun();
// Return best-found solution and fitness.
return new Result(g, fitness, feasible, i);
}
#endregion
}
} |