// 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;

  /** Abstract interface for computation of search directions, in
      support of descent methods.  Since computation can succeed or
      fail, must have character of terminator.
  */

  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 

    /** Returns search direction in mutable first argument */
    virtual void calcDir(Vector<Scalar> & dir,
			 FunctionalEvaluation<Scalar> & fx) = 0;

    /** Use data generated during line search to update any internals */
    virtual void updateDir(LineSearchAlg<Scalar> const & ls) = 0;

    /** Reinitialize direction computation */
    virtual void resetDir() = 0;

    /** verbose output stream */
    virtual ostream & write(ostream & str) const = 0;
  };

  /** Base class for Unconstrained Minimization step algorithms
      with globalization via line search.
.
      Provides the added functionality of maintaining the
      functional evaluations and states correctly when
      set, and providing external access to such states.
      The functional evaluation may be set to avoid recomputing
      result in the calling object.

      On call,
      x0 = base point for search
      fx = functional evaluation at x0
        
      On return,
      x  = minimizer estimate
      fx = functional evaluation at x

      The state is always the point x.
  */
  template<class Scalar>
  class UMinStepLS: public Algorithm, public Terminator {
  private:
    
    FunctionalEvaluation<Scalar> & fx; 
    UMinDir<Scalar> & dc;
    LineSearchAlg<Scalar> & ls;
    bool ans;
    ostream & str;

    /** Find the step length in this search direction,
	and updates x.
	Default implementation always sets the step to 1.
	
	Returning a non-positive step length is 
	an algorithmic failure, and will cause the run()
	method to return false.
    */

    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:

    /** Initialize with the functional and the starting point for the 
	optimization.
    */
    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() {}

    /** Return a reference to the base point for the step. */
    Vector<Scalar> const & getBasePoint() { return ls.getBasePoint(); }

    /** Return a reference to the gradient at the bast point. */
    Vector<Scalar> const & getBaseGradient() { return ls.getBaseGradient(); }

    /** Return a reference to the functional evaluation at the trial point */
    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