net.sf.cpsolver.ifs.perturbations
Interface PerturbationsCounter<V extends Variable<V,T>,T extends Value<V,T>>

All Known Implementing Classes:
DefaultPerturbationsCounter, UniversalPerturbationsCounter

public interface PerturbationsCounter<V extends Variable<V,T>,T extends Value<V,T>>

Counter of perturbation penalty (minimal perturbation problem).

Many real-life problems are dynamic, with changes in the problem definition occurring after a solution to the initial formulation has been reached. A minimal perturbation problem incorporates these changes, along with the initial solution, as a new problem whose solution must be as close as possible to the initial solution. The iterative forward search algorithm is also made to solve minimal perturbation problems.

To define the minimal perturbation problem, we will consider an initial (original) problem, its solution, a new problem, and some distance function which allows us to compare solutions of the initial and the new problem. Subsequently we look for a solution of the new problem with minimal distance from the initial solution. This distance is expressed by this PerturbationCounter

Version:
IFS 1.2 (Iterative Forward Search)
Copyright (C) 2006 - 2010 Tomáš Müller
muller@unitime.org
http://muller.unitime.org

This library is free software; you can redistribute it and/or modify it under the terms of the GNU Lesser General Public License as published by the Free Software Foundation; either version 3 of the License, or (at your option) any later version.

This library is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU Lesser General Public License for more details.

You should have received a copy of the GNU Lesser General Public License along with this library; if not see http://www.gnu.org/licenses/.
See Also:
Solver, Solution, Variable

Method Summary
 void getInfo(Map<String,String> info, Model<V,T> model)
          Some (perturbation) information about the solution might be returned here.
 void getInfo(Map<String,String> info, Model<V,T> model, Collection<V> variables)
          Some (perturbation) information about the solution might be returned here (only include variables from the given set).
 double getPerturbationPenalty(Model<V,T> model)
          Returns perturbation penalty, i.e., the distance between current solution and the solution of the initial problem (see Variable.getInitialAssignment()).
 double getPerturbationPenalty(Model<V,T> model, Collection<V> variables)
          Returns perturbation penalty, i.e., the distance between current solution and the solution of the initial (only include variables from the given set) problem (see Variable.getInitialAssignment()).
 double getPerturbationPenalty(Model<V,T> model, T selectedValue, Collection<T> conflicts)
          Returns perturbation penalty of the solution which become from the current solution when given conflicting values are unassigned and the selected value is assigned.
 void init(Solver<V,T> solver)
          Initialization
 

Method Detail

init

void init(Solver<V,T> solver)
Initialization


getPerturbationPenalty

double getPerturbationPenalty(Model<V,T> model)
Returns perturbation penalty, i.e., the distance between current solution and the solution of the initial problem (see Variable.getInitialAssignment()).

Parameters:
model - current model

getPerturbationPenalty

double getPerturbationPenalty(Model<V,T> model,
                              Collection<V> variables)
Returns perturbation penalty, i.e., the distance between current solution and the solution of the initial (only include variables from the given set) problem (see Variable.getInitialAssignment()).

Parameters:
model - current model

getPerturbationPenalty

double getPerturbationPenalty(Model<V,T> model,
                              T selectedValue,
                              Collection<T> conflicts)
Returns perturbation penalty of the solution which become from the current solution when given conflicting values are unassigned and the selected value is assigned. Since this penalty is used for comparison of different candidate values in the value selection criterion, it is fully acceptable to just return a difference between current and the altered solution (which might be easied for computation that the whole perturbation penalty).

Parameters:
model - current model
selectedValue - value to be selected in the next iteration
conflicts - conflicting values to be unassigned in the next iteration

getInfo

void getInfo(Map<String,String> info,
             Model<V,T> model)
Some (perturbation) information about the solution might be returned here.

Parameters:
info - resultant info table
model - current model

getInfo

void getInfo(Map<String,String> info,
             Model<V,T> model,
             Collection<V> variables)
Some (perturbation) information about the solution might be returned here (only include variables from the given set).

Parameters:
info - resultant info table
model - current model