uminstep.hh

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// conoptstep.H
// created by ADP 5/27/04

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#ifndef __ALG_UMINSTEP_H_
#define __ALG_UMINSTEP_H_

#include "lnsrch.hh"
#include "alg.hh"
#include "functional.hh"

namespace RVLUmin {

  using namespace RVLAlg;
  using RVL::Functional;
  using RVL::Vector;
  using RVL::Vector;
  using RVL::FunctionalEvaluation;

  template<typename Scalar>
  class UMinDir: public Terminator {
  public:
    UMinDir() {}
    UMinDir(UMinDir<Scalar> const &) {}
    virtual ~UMinDir() {}

    // note that Terminator::query must be defined by instantiable subclass 

    virtual void calcDir(Vector<Scalar> & dir,
             FunctionalEvaluation<Scalar> & fx) = 0;

    virtual void updateDir(LineSearchAlg<Scalar> const & ls) = 0;

    virtual void resetDir() = 0;

    virtual ostream & write(ostream & str) const = 0;
  };

  template<class Scalar>
  class UMinStepLS: public Algorithm, public Terminator {
  private:
    
    FunctionalEvaluation<Scalar> & fx; 
    UMinDir<Scalar> & dc;
    LineSearchAlg<Scalar> & ls;
    bool ans;
    ostream & str;

    virtual void calcStep(Vector<Scalar> & dir) {
      try {
    //Line Search
    bool tried_steepest_descent = false;
    // set up and run line search
    ls.initialize(fx,dir);
        ls.run();
    bool res = ls.query();
    if (!res) { 
      dc.updateDir(ls);
      ans = ans || dc.query(); 
    }
    // line search failed, try steepest descent
    else {
      if (!tried_steepest_descent) {
        str<<"UMinStep: attempting steepest descent restart\n";
        // This resets the evaluation point
        Vector<Scalar> & x = fx.getPoint();
        x.copy(ls.getBasePoint());
        dir.copy(ls.getBaseGradient());
        dir.negate();
        dc.resetDir();
        // this costs a redundant gradient computation!!
        ls.initialize(fx,dir);
        ls.run();
        if (!ls.query()) { 
          dc.updateDir(ls);
          ans= ans || dc.query();
        }
        else {
          ans = true;
        }
        tried_steepest_descent=true;
      }
      else {
        ans = true;
      }
    }

      } catch(RVLException & e) {
    e << "\ncalled from UMinStepLS::calcStep()\n";
    throw e;
      }
    }

  public:

    UMinStepLS(FunctionalEvaluation<Scalar> & _fx, 
           UMinDir<Scalar> & _dc,
           LineSearchAlg<Scalar> & _ls,
           ostream & _str = cout)
      : fx(_fx), dc(_dc), ls(_ls), ans(false), str(_str) {}

    UMinStepLS(const UMinStepLS<Scalar> & cos)
      : fx(cos.fx), dc(cos.dc), ls(cos.ls), ans(cos.ans), str(cos.str) {}

    virtual ~UMinStepLS() {}

    Vector<Scalar> const & getBasePoint() { return ls.getBasePoint(); }

    Vector<Scalar> const & getBaseGradient() { return ls.getBaseGradient(); }

    FunctionalEvaluation<Scalar> & getFunctionalEvaluation() { return fx; }
    
    void run() {
      try {
    Vector<Scalar> dir(fx.getDomain(), true);
    dc.calcDir(dir,fx);
    calcStep(dir);
      } 
      catch(RVLException & e) {
    e << "called from UMinStepLS::run()\n";
    throw e;
      } 
      catch( std::exception & e) {
    RVLException es;
    es << "Exception caught in UMinStepLS::run() with error message";
    es << e.what(); 
    throw e;
      }
    }

    bool query() { return ans; }

  };

}


#endif

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