001package org.cpsolver.ifs.heuristics;
002
003import org.cpsolver.ifs.model.Value;
004import org.cpsolver.ifs.model.Variable;
005import org.cpsolver.ifs.solution.Solution;
006import org.cpsolver.ifs.solver.Solver;
007
008/**
009 * Variable selection criterion. <br>
010 * <br>
011 * The IFS algorithm requires a function that selects a variable to be
012 * (re)assigned during the current iteration step. This problem is equivalent to
013 * a variable selection criterion in constraint programming. There are several
014 * guidelines for selecting a variable. In local search, the variable
015 * participating in the largest number of violations is usually selected first.
016 * In backtracking-based algorithms, the first-fail principle is often used,
017 * i.e., a variable whose instantiation is most complicated is selected first.
018 * This could be the variable involved in the largest set of constraints or the
019 * variable with the smallest domain, etc. <br>
020 * <br>
021 * We can split the variable selection criterion into two cases. If some
022 * variables remain unassigned, the "worst" variable among them is selected,
023 * i.e., first-fail principle is applied. This may, for example, be the variable
024 * with the smallest domain or with the highest number of hard and/or soft
025 * constraints. <br>
026 * <br>
027 * The second case occurs when all variables are assigned. Because the algorithm
028 * does not need to stop when a complete feasible solution is found, the
029 * variable selection criterion for such case has to be considered as well. Here
030 * all variables are assigned but the solution is not good enough, e.g., in the
031 * sense of violated soft constraints. We choose a variable whose change of a
032 * value can introduce the best improvement of the solution. It may, for
033 * example, be a variable whose value violates the highest number of soft
034 * constraints. <br>
035 * <br>
036 * It is possible for the solution to become incomplete again after such an
037 * iteration because a value which is not consistent with all hard constraints
038 * can be selected in the value selection criterion. This can also be taken into
039 * account in the variable selection heuristics.
040 * 
041 * @see Solver
042 * 
043 * @author  Tomáš Müller
044 * @version IFS 1.3 (Iterative Forward Search)<br>
045 *          Copyright (C) 2006 - 2014 Tomáš Müller<br>
046 *          <a href="mailto:muller@unitime.org">muller@unitime.org</a><br>
047 *          <a href="http://muller.unitime.org">http://muller.unitime.org</a><br>
048 * <br>
049 *          This library is free software; you can redistribute it and/or modify
050 *          it under the terms of the GNU Lesser General Public License as
051 *          published by the Free Software Foundation; either version 3 of the
052 *          License, or (at your option) any later version. <br>
053 * <br>
054 *          This library is distributed in the hope that it will be useful, but
055 *          WITHOUT ANY WARRANTY; without even the implied warranty of
056 *          MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU
057 *          Lesser General Public License for more details. <br>
058 * <br>
059 *          You should have received a copy of the GNU Lesser General Public
060 *          License along with this library; if not see <a href='http://www.gnu.org/licenses'>http://www.gnu.org/licenses</a>.
061 *
062 * @param <V> Variable
063 * @param <T> Value
064 **/
065public interface VariableSelection<V extends Variable<V, T>, T extends Value<V, T>> {
066    /** Initialization 
067     * @param solver current solver
068     **/
069    public void init(Solver<V, T> solver);
070
071    /**
072     * Variable selection
073     * 
074     * @param solution
075     *            current solution
076     * @return selected variable
077     */
078    public V selectVariable(Solution<V, T> solution);
079}