/****************************************************************************
Copyright (c) 2003, Landmark Graphics and others. All rights reserved.
This program and accompanying materials are made available under the terms of
the Common Public License - v1.0, which accompanies this distribution, and is
available at http://www.eclipse.org/legal/cpl-v10.html
****************************************************************************/
package edu.mines.jtk.opt;

import edu.mines.jtk.util.Almost;
import edu.mines.jtk.util.Monitor;

import java.util.logging.Logger;

/** Minimize a simple quadratic objective function.
    Finds the x that minimizes the quadratic function 0.5 x'Hx + b'x .
    Solves Hx + b = 0  for x (the Normal Equations of a least-squares problem)
    b is the gradient for x=0, and H is the hessian.
    The algorithm is a conjugate gradient.
    A is an approximate inverse Hessian, making AH more diagonal
    and improving convergence.
    A may also include a model-dependent conditioning to converge
    earlier on eigenfunction of most importance.
    The algorithm assumes that the application of H dominates the cost.
<pre>
     x = p = u = 0;
     beta = 0;
     g = b;
     a = A g;
     q = H a;
     do {
       p = -a + beta p
       u = Hp = -q + beta u
       scalar = -p'g/p'u
       x = x + scalar p
       if (done) return x
       g = H x + b = g + scalar u
       a = A g
       q = H a
       beta = p'H A g / p'H p = p'q/p'u
    }
</pre>

Also contains a solver for a least-squares inverse of a linear
transform, using QuadraticTransform as a wrapper.

@author W.S. Harlan
*/
public class QuadraticSolver {
  private Quadratic _quadratic = null;
  private static final Logger LOG = Logger.getLogger("edu.mines.jtk.opt");

  /** Implement the Quadratic interface and pass to this constructor.
    Pass a vector b to the constructor (a copy is cloned internally).
    This solver calls Vect and Quadratic methods.
    Does not call Vect.multiplyInverseCovariance().
    @param quadratic Defines the Hessian quadratic term.
  */
  public QuadraticSolver( Quadratic quadratic) {
    _quadratic = quadratic;
  }

  /** Return a new solution after the number of conjugate gradient
      iterations.
      @param numberIterations is the number of iterations to perform.
      @param monitor If non-null, then track all progress.
      @return Optimized solution
  */
  public Vect solve(int numberIterations, Monitor monitor) {
    int iter;
    if (monitor == null) monitor = Monitor.NULL_MONITOR;
    monitor.report(0.);
    VectConst b = _quadratic.getB(); // instance 1
    monitor.report(1./(numberIterations+2.));

    double bb = b.dot(b); checkNaN(bb);
    if (Almost.FLOAT.zero(bb)) {
      LOG.fine("Gradient of quadratic is negligible.  Not solving");
      Vect result = VectUtil.cloneZero(b);
      monitor.report(1.);
      return result;
    }

    Vect g = (Vect) b;  b = null;   // reuse b
    Vect x = VectUtil.cloneZero(g); // instance 2
    Vect p = x.clone();      // instance 3
    Vect u = x.clone();      // instance 4
    double pu = 0.;
    Vect qa = g.clone();     // double use for instance 5

  SOLVE:
    for (iter=0; iter<numberIterations; iter++) {
      double beta = 0.;
      {
        Vect q = qa;
        VectUtil.copy(q, g);
        _quadratic.inverseHessian(q);
        q.postCondition();         // a = A g
        _quadratic.multiplyHessian(q); // expensive
        monitor.report((iter+2.)/(numberIterations+2.));
        if (iter > 0) {
          double pq = p.dot(q); checkNaN(pq);
          beta = Almost.FLOAT.divide(pq,pu,0);
          if (beta < -5.) beta = -5.;
          if (beta > 5.) beta = 5.;
        }
        u.add(beta, -1., q);
      }
      { // Did not save "a" before calculating beta, to avoid extra instance.
        Vect a = qa;
        VectUtil.copy(a, g);
        _quadratic.inverseHessian(a);
        a.postCondition();
        p.add(beta, -1., a);
      }
      double pg = p.dot(g); checkNaN(pg);
      pu = p.dot(u); checkNaN(pu);
      if (Almost.FLOAT.zero(pg)|| Almost.FLOAT.zero(pu)) {
        break SOLVE;
      }
      double scalar = -pg/pu;
      x.add(1., scalar, p);
      if (iter == numberIterations-1) {
        break SOLVE;
      }
      g.add(1., scalar, u);
    }
    p.dispose();
    u.dispose();
    g.dispose();
    qa.dispose();
    monitor.report(1.);
    return x;
  }

  /**
    Solve quadratic objective function for linear transform.
    Minimizes
    <pre>
      [F(m+x)-data]'N[F(m+x)-data] + (m+x)'M(m+x)
    </pre>
    if dampOnlyPerturbation is true and
    <pre>
      [F(m+x)-data]'N[F(m+x)-data] + (x)'M(x)
    </pre>
    if dampOnlyPerturbation is false.
    @param data The data to be fit.
    @param referenceModel Initialize with this model.
    @param linearTransform Describes the linear transform.
    @param dampOnlyPerturbation If true then, only damp perturbations
    to reference model. If false, then damp the reference model plus
    the perturbation.
    @param conjugateGradIterations The specified number of conjugate
    gradient iterations.
    @param monitor Report progress here, if non-null.
    @return Result of optimization
  */
  public static Vect solve (VectConst data,
                            VectConst referenceModel,
                            LinearTransform linearTransform,
                            boolean dampOnlyPerturbation,
                            int conjugateGradIterations,
                            Monitor monitor) {
    //final int linearizationIterations = 1;
    //final int lineSearchIterations = 0;
    //final double lineSearchError = 0.;
    Transform transform = new LinearTransformWrapper(linearTransform);
    VectConst perturbModel = null;
    TransformQuadratic transformQuadratic =
      new TransformQuadratic(data, referenceModel, perturbModel, transform,
                             dampOnlyPerturbation);
    QuadraticSolver quadraticSolver = new QuadraticSolver(transformQuadratic);
    Vect result = quadraticSolver.solve(conjugateGradIterations, monitor);
    transformQuadratic.dispose();
    result.add(1, 1, referenceModel);
    result.constrain();
    return result;
  }

  /** Abort if NaN's appear.
      @param value Abort if this value is a NaN.
   */
  private static void checkNaN(double value) {
    if (value*0 != 0) {
      throw new IllegalStateException("Value is a NaN");
    }
  }
}

@@ -5515,136 +5515,8 @@
 ) %7B%0A
-    //final int linearizationIterations = 1;%0A    //final int lineSearchIterations = 0;%0A    //final double lineSearchError = 0.;%0A
     

Remove dead code