com.exigen.ie.constrainer.GoalFloatFastMinimize Maven / Gradle / Ivy
package com.exigen.ie.constrainer;
import com.exigen.ie.constrainer.impl.ConstraintFloatExpLessValue;
import com.exigen.ie.tools.Log;
///////////////////////////////////////////////////////////////////////////////
/*
* Copyright Exigen Group 1998, 1999, 2000
* 320 Amboy Ave., Metuchen, NJ, 08840, USA, www.exigengroup.com
*
* The copyright to the computer program(s) herein
* is the property of Exigen Group, USA. All rights reserved.
* The program(s) may be used and/or copied only with
* the written permission of Exigen Group
* or in accordance with the terms and conditions
* stipulated in the agreement/contract under which
* the program(s) have been supplied.
*/
///////////////////////////////////////////////////////////////////////////////
/**
* An implementation of a {@link Goal} that finds a solution which minimizes
* a constrained integer variable called the "cost".
*
* This goal uses the search goal provided by the caller, and expects
* that this goal instantiates the cost every time when a solution is found.
*
* To search for a solution which provides the minimal cost,
* GoalFloatFastMinimize finds a solution, posts the constraint that the
* cost variable should be less then the found cost and continues the search.
*
* In contrast with GoalFloatMinimize, when a solution is found GoalFloatFastMinimize
* does not restore all decision variables, but continues the search just with
* a more strict constraint to the cost variable.
*
* When there are no solutions anymore, the latest found solution is the optimal one.
*
* By default, GoalFloatFastMinimize calculates the optimal solution twice.
*
* If the caller's search goal saves every found solution itself, the caller may
* specify the mode "goal_saves_solution" to prevent the duplicate calculation
* at the end of the search.
*
* @see GoalFloatMinimize
*/
public class GoalFloatFastMinimize extends GoalImpl
{
private FloatExp _cost;
private double _best_cost_min;
private double _best_cost_max;
private Goal _goal_find_solution;
private ConstraintFloatExpLessValue _constraint_limit_cost;
private int _number_of_solutions;
private boolean _trace;
private Object _client_tracer;
private boolean _goal_saves_solution;
private ChoicePointLabel _rootLabel;
private double _cost_step;
/**
* Constructor with a given generation goal, cost expression, trace flag,
* and save_solution flag.
*
* Use "goal" to minimize "cost".
*
* If ("trace" = true), after each successful iteration, solution# and the
* cost value will be printed.
*
* If ("goal_saves_solution" = true), the optimal solution (if any)
* would not be calculated twice: it means the "goal" should save
* the value of decision variables and the cost.
*/
public GoalFloatFastMinimize(Goal goal, FloatExp cost, double costStep)
{
super(cost.constrainer(),"");
_goal_find_solution = goal;
_cost = cost;
_constraint_limit_cost = new ConstraintFloatExpLessValue(_cost, _cost.max());
_number_of_solutions = 0;
_trace = false;
_client_tracer = null;
_goal_saves_solution = false;
_rootLabel = constrainer().createChoicePointLabel();
_cost_step = Math.abs(costStep);
}
public void traceFlag(boolean flag)
{
_trace = flag;
}
public void clientTracer(Object tracer)
{
_client_tracer = tracer;
}
public void savesSolutionFlag(boolean flag)
{
_goal_saves_solution = flag;
}
/**
* The implementation of the search algorithm.
*
* It sets labeled choice point from the goals FindAndImprove and AnalyzeAndSet.
*
* FindAndImprove is a loop that finds an optimal solution (if any solution exists).
* FindAndImprove always fails (the _cost can not be improved infinitely).
*
* AnalyzeAndSet do further job:
*
* - Checks if any solution was found and fails if not.
*
- If _goal_save_solution is true then it finishes.
*
- Otherwise the constraint _cost==_best_cost is posted and _goal_find_solution
* is executed to instantiate the optimal solution.
*
*/
public Goal execute() throws Failure
{
return new GoalOr(new FindAndImprove(constrainer()),
new AnalyzeAndSet(constrainer()),
_rootLabel );
}
/**
* Returns a String representation of this goal.
*/
public String toString()
{
return "Use "+_goal_find_solution.name()+ " to minimize("+_cost.name()+")";
}
/**
* Activates _constraint_limit_cost and organizes the optimization loop
* using _goal_find_solution and GoalBacktrack.
*/
class FindAndImprove extends GoalImpl
{
FindAndImprove(Constrainer C)
{
super(C,"FindAndImprove");
}
public Goal execute() throws Failure
{
// activate _constraint_limit_cost
_constraint_limit_cost.resetValue(_cost.max());
_constraint_limit_cost.post();
return new GoalAnd( _goal_find_solution,
new GoalBacktrack(constrainer()) );
}
};
/**
* This goal is executed after an optimal solution is found or no solution exists.
* The constrainer state is as it was before GoalFloatFastMinimize.execute().
*/
class AnalyzeAndSet extends GoalImpl
{
AnalyzeAndSet(Constrainer C)
{
super(C,"AnalyzeAndSet");
}
public Goal execute() throws Failure
{
// check if the any solution was found
if (_number_of_solutions <= 0)
constrainer().fail();
//Debug.on();Debug.print("Found solution with the cost = "+_best_cost);Debug.off();
if(_goal_saves_solution)
return null;
// post the constraint '_cost == _best_cost'
double eps = 1e-6; //???
double search_cost_min = _best_cost_min - eps;
double search_cost_max = _best_cost_max + eps;
_cost.moreOrEqual(search_cost_min).post();
_cost.lessOrEqual(search_cost_max).post();
// find the optimal solution
return _goal_find_solution;
}
}
/**
* This goal backtracks while _cost GE _best_cost.
*
* Then it sets _best_cost as a new value in _constraint_limit_cost.
* In this case the search continues with the new limit on the cost.
*
* This goal fails if backtrack can not satisfy _cost LT _best_cost.
* This means that the is no better solution.
* In this case GoalAnalyzeAndSet will be executed as an alternate
* goal for the "_rootLabel" choice point.
*/
class GoalBacktrack extends GoalImpl
{
public GoalBacktrack(Constrainer c)
{
super(c,"GoalBacktrack");
}
/**
* Called when a solution (first or next) is found.
* Saves the current value of the cost, increments _number_of_solutions,
* and performs tracing.
*/
void fixFoundSolution()
{
_best_cost_min = _cost.min();
_best_cost_max = _cost.max();
_number_of_solutions++;
if (_trace)
{
Log.info("Solution "+_number_of_solutions+": cost"+_cost.domainToString());
}
if (_client_tracer != null)
{
Log.info(_client_tracer.toString());
}
}
/**
* violated() for the constraint: _cost <= _best_cost - _cost_step
*/
boolean violated()
{
return _cost.min() > _best_cost_min - _cost_step;
}
public Goal execute() throws Failure
{
fixFoundSolution();
// backtrack while _cost >= _best_cost
ChoicePointLabel lbl = constrainer().currentChoicePointLabel();
while( ((lbl==null )||(!lbl.equals(_rootLabel))) && violated() )
{
if(!constrainer().backtrack())
throw new RuntimeException("Internal error in "+this);
if (_trace)
{
Log.info(" Backtrack: cost"+_cost.domainToString()+" best_cost_min="+_best_cost_min);
}
}
// fail if still violated
if(violated())
constrainer().fail("GoalBacktrack");
// set _best_cost as a new limit in _constraint_limit_cost
_constraint_limit_cost.resetValue(_best_cost_min - _cost_step);
return null;
}
} // GoalBacktrack
} // ~GoalFloatFastMinimize