Package opennlp.tools.ml.maxent.quasinewton

Class QNMinimizer

java.lang.Object
opennlp.tools.ml.maxent.quasinewton.QNMinimizer
public class QNMinimizer extends Object
Implementation of the Limited memory Broyden-Fletcher-Goldfarb-Shanno algorithm (L-BFGS) which supports L1-, L2-regularization and Elastic Net for solving convex optimization problems.

Usage example: 

  // Quadratic function f(x) = (x-1)^2 + 10
  // f obtains its minimum value 10 at x = 1
  Function f = new Function() {

    @Override
    public int getDimension() {
      return 1;
    }

    @Override
    public double valueAt(double[] x) {
      return StrictMath.pow(x[0]-1, 2) + 10;
    }

    @Override
    public double[] gradientAt(double[] x) {
      return new double[] { 2*(x[0]-1) };
    }

  };

  QNMinimizer minimizer = new QNMinimizer();
  double[] x = minimizer.minimize(f);
  double min = f.valueAt(x);
See Also:

  • Field Details

    • CONVERGE_TOLERANCE

      public static final double CONVERGE_TOLERANCE
      See Also:

    • REL_GRAD_NORM_TOL

      public static final double REL_GRAD_NORM_TOL
      See Also:

    • INITIAL_STEP_SIZE

      public static final double INITIAL_STEP_SIZE
      The initial step size: 1.0.
      See Also:

    • MIN_STEP_SIZE

      public static final double MIN_STEP_SIZE
      The minimum step size: 1e-10.
      See Also:

    • L1COST_DEFAULT

      public static final double L1COST_DEFAULT
      The default L1-cost value is 0.0d.
      See Also:

    • L2COST_DEFAULT

      public static final double L2COST_DEFAULT
      The default L2-cost value is 0.0d.
      See Also:

    • NUM_ITERATIONS_DEFAULT

      public static final int NUM_ITERATIONS_DEFAULT
      By default the number of iterations is 100.
      See Also:

    • M_DEFAULT

      public static final int M_DEFAULT
      The default number of Hessian updates to store is 15.
      See Also:

    • MAX_FCT_EVAL_DEFAULT

      public static final int MAX_FCT_EVAL_DEFAULT
      The default maximum number of function evaluations is 30,000.
      See Also:

  • Constructor Details

    • QNMinimizer

      public QNMinimizer()
      Initializes a QNMinimizer with default parameters (see: L1COST_DEFAULT and L2COST_DEFAULT).

    • QNMinimizer

      public QNMinimizer(double l1Cost, double l2Cost)
      Initializes a QNMinimizer.
      Parameters:
      l1Cost - The L1-regularization cost. Must be equal to or greater than 0.
      l2Cost - The L2-regularization cost. Must be equal to or greater than 0.
      Throws:
      IllegalArgumentException - Thrown if parameters were invalid.

    • QNMinimizer

      public QNMinimizer(double l1Cost, double l2Cost, int iterations)
      Initializes a QNMinimizer with L1 and L2 parameters.
      Parameters:
      l1Cost - The L1-regularization cost. Must be equal to or greater than 0.
      l2Cost - The L2-regularization cost. Must be equal to or greater than 0.
      iterations - The maximum number of iterations. Must be greater than 0.
      Throws:
      IllegalArgumentException - Thrown if parameters were invalid.

    • QNMinimizer

      public QNMinimizer(double l1Cost, double l2Cost, int iterations, int m, int maxFctEval)
      Initializes a QNMinimizer.
      Parameters:
      l1Cost - The L1-regularization cost. Must be equal to or greater than 0.
      l2Cost - The L2-regularization cost. Must be equal to or greater than 0.
      iterations - The maximum number of iterations. Must be greater than 0.
      m - The number of Hessian updates to store. Must be greater than 0.
      maxFctEval - The maximum number of function evaluations. Must be greater than 0.
      Throws:
      IllegalArgumentException - Thrown if parameters were invalid.

  • Method Details

    • getEvaluator

      public QNMinimizer.Evaluator getEvaluator()

    • setEvaluator

      public void setEvaluator(QNMinimizer.Evaluator evaluator)

    • minimize

      public double[] minimize(Function function)
      Finds the parameters that minimize the objective function.
      Parameters:
      function - The objective Function.
      Returns:
      The minimizing parameters.