Package opennlp.tools.ml.maxent.quasinewton

Class QNMinimizer

  • public class QNMinimizer
extends Object
    Implementation of 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);

    • Constructor Detail

      • QNMinimizer

        public QNMinimizer()

      • QNMinimizer

        public QNMinimizer​(double l1Cost,
                   double l2Cost)

      • QNMinimizer

        public QNMinimizer​(double l1Cost,
                   double l2Cost,
                   int iterations)

      • QNMinimizer

        public QNMinimizer​(double l1Cost,
                   double l2Cost,
                   int iterations,
                   int m,
                   int maxFctEval)

      • QNMinimizer

        public QNMinimizer​(double l1Cost,
                   double l2Cost,
                   int iterations,
                   int m,
                   int maxFctEval,
                   boolean verbose)
        Constructor
        Parameters:
        l1Cost - L1-regularization cost
        l2Cost - L2-regularization cost
        iterations - maximum number of iterations
        m - number of Hessian updates to store
        maxFctEval - maximum number of function evaluations
        verbose - verbose output

    • Method Detail

      • minimize

        public double[] minimize​(Function function)
        Find the parameters that minimize the objective function
        Parameters:
        function - objective function
        Returns:
        minimizing parameters