internal static OptBoundVariable[] GetClon(OptBoundVariable[] variables)
{
OptBoundVariable[] cloned = new OptBoundVariable[variables.Length];
for (int i = 0; i < variables.Length; i++)
{
cloned[i] = variables[i].GetClon();
}
return(cloned);
}
private OptSimplexBoundVariable[] GetVariables(OptBoundVariable[] variablesArray)
{
OptSimplexBoundVariable[] vars = new OptSimplexBoundVariable[variablesArray.Length];
for (int i = 0; i < variablesArray.Length; i++)
{
OptBoundVariable var = variablesArray[i];
vars[i] = new OptSimplexBoundVariable(var.Name, var.InitialGuess, var.Fixed, var.LowerBound, var.UpperBound);
}
return(vars);
}
private OptSimplexBoundVariable[] GetVariables(OptBoundVariable[] variablesArray)
{
OptSimplexBoundVariable[] vars = new OptSimplexBoundVariable[variablesArray.Length];
for (int i = 0; i < variablesArray.Length; i++)
{
OptBoundVariable var = variablesArray[i];
vars[i] = new OptSimplexBoundVariable(var.Name, var.InitialGuess, var.Fixed, var.LowerBound, var.UpperBound);
}
return vars;
}
/// <summary>
/// Computes the minimum point of a function of several variables.
/// </summary>
/// <param name="function">The function to minimize.</param>
/// <param name="variables">Array of size N containing the varaibles.</param>
/// <param name="initialStep">The initial change of the variables.</param>
/// <returns>Array containing the solution.</returns>
public double[] ComputeMin(OptMultivariateFunction function, OptBoundVariable[] variables, float initialStep)
{
if (variables == null) return new double[0];
if (variables.Length == 0) return new double[0];
OptSimplexBoundVariable[] simplexVariables = this.GetVariables(variables);
int maxFunc = this._MaxFunEvaluations;
return this.GetMinimum(function, simplexVariables, initialStep, this._Tolerance, ref maxFunc);
}
/// <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;
this._TNDriver.ETA = this._SearchSeverity;
this._TNDriver.STEPMX = this._MaximunStep;
double[] minimum = this._TNDriver.ComputeMin(function, gradient, variables, this._Tolerance, this._Accuracy, ref maxFunc);
this._FunEvaluations = maxFunc;
return minimum;
}
internal OptBoundVariable GetClon()
{
OptBoundVariable clon = new OptBoundVariable(this._Name, this._InitialGuess, this._Fixed, this._LowerBound, this._UpperBound);
return(clon);
}
/// <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;
}
internal OptBoundVariable GetClon()
{
OptBoundVariable clon = new OptBoundVariable(this._Name, this._InitialGuess, this._Fixed, this._LowerBound, this._UpperBound);
return clon;
}
internal static OptBoundVariable[] GetClon(OptBoundVariable[] variables)
{
OptBoundVariable[] cloned = new OptBoundVariable[variables.Length];
for (int i = 0; i < variables.Length; i++)
{
cloned[i] = variables[i].GetClon();
}
return cloned;
}
