/// <summary>Solves the problem using OWLQN.</summary>
/// <remarks>
/// Solves the problem using OWLQN. The solution
/// is stored in the
/// <c>lambda</c>
/// array of
/// <c>prob</c>
/// . Note that the
/// likelihood function will be a penalized L2 likelihood function unless you
/// have turned this off via setting the priorSigmaS to 0.0.
/// </remarks>
/// <param name="weight">
/// Controls the sparseness/regularization of the L1 solution.
/// The bigger the number the sparser the solution. Weights between
/// 0.01 and 1.0 typically give good performance.
/// </param>
public virtual void SolveL1(double weight)
{
CGRunner.LikelihoodFunction df = new CGRunner.LikelihoodFunction(prob, tol, useGaussianPrior, priorSigmaS, sigmaSquareds);
IMinimizer <IDiffFunction> owl = ReflectionLoading.LoadByReflection("edu.stanford.nlp.optimization.OWLQNMinimizer", weight);
prob.lambda = owl.Minimize(df, tol, new double[df.DomainDimension()]);
PrintOptimizationResults(df, null);
}
/// <summary>Solves the problem using conjugate gradient (CG).</summary>
/// <remarks>
/// Solves the problem using conjugate gradient (CG). The solution
/// is stored in the
/// <c>lambda</c>
/// array of
/// <c>prob</c>
/// .
/// </remarks>
public virtual void SolveCG()
{
CGRunner.LikelihoodFunction df = new CGRunner.LikelihoodFunction(prob, tol, useGaussianPrior, priorSigmaS, sigmaSquareds);
CGRunner.MonitorFunction monitor = new CGRunner.MonitorFunction(prob, df, filename);
IMinimizer <IDiffFunction> cgm = new CGMinimizer(monitor);
// all parameters are started at 0.0
prob.lambda = cgm.Minimize(df, tol, new double[df.DomainDimension()]);
PrintOptimizationResults(df, monitor);
}
public int DomainDimension()
{
return(lf.DomainDimension());
}
