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/*
* This file is part of choco-solver, http://choco-solver.org/
*
* Copyright (c) 2023, IMT Atlantique. All rights reserved.
*
* Licensed under the BSD 4-clause license.
*
* See LICENSE file in the project root for full license information.
*/
package org.chocosolver.solver;
import org.chocosolver.memory.IEnvironment;
import org.chocosolver.solver.constraints.Constraint;
import org.chocosolver.solver.exception.ContradictionException;
import org.chocosolver.solver.exception.InvalidSolutionException;
import org.chocosolver.solver.exception.SolverException;
import org.chocosolver.solver.learn.AbstractEventObserver;
import org.chocosolver.solver.objective.IBoundsManager;
import org.chocosolver.solver.objective.IObjectiveManager;
import org.chocosolver.solver.objective.ObjectiveFactory;
import org.chocosolver.solver.propagation.PropagationEngine;
import org.chocosolver.solver.search.SearchState;
import org.chocosolver.solver.search.limits.ICounter;
import org.chocosolver.solver.search.loop.Reporting;
import org.chocosolver.solver.search.loop.learn.Learn;
import org.chocosolver.solver.search.loop.learn.LearnNothing;
import org.chocosolver.solver.search.loop.monitors.ISearchMonitor;
import org.chocosolver.solver.search.loop.monitors.SearchMonitorList;
import org.chocosolver.solver.search.loop.move.Move;
import org.chocosolver.solver.search.loop.move.MoveBinaryDFS;
import org.chocosolver.solver.search.loop.move.MoveSeq;
import org.chocosolver.solver.search.loop.propagate.Propagate;
import org.chocosolver.solver.search.measure.IMeasures;
import org.chocosolver.solver.search.measure.MeasuresRecorder;
import org.chocosolver.solver.search.restart.AbstractRestart;
import org.chocosolver.solver.search.strategy.Search;
import org.chocosolver.solver.search.strategy.decision.Decision;
import org.chocosolver.solver.search.strategy.decision.DecisionPath;
import org.chocosolver.solver.search.strategy.strategy.AbstractStrategy;
import org.chocosolver.solver.search.strategy.strategy.WarmStart;
import org.chocosolver.solver.trace.IOutputFactory;
import org.chocosolver.solver.variables.IntVar;
import org.chocosolver.solver.variables.Task;
import org.chocosolver.solver.variables.Variable;
import org.chocosolver.util.ESat;
import org.chocosolver.util.criteria.Criterion;
import org.chocosolver.util.logger.ANSILogger;
import org.chocosolver.util.logger.Logger;
import java.util.*;
import static org.chocosolver.solver.Solver.Action.*;
import static org.chocosolver.solver.constraints.Constraint.Status.FREE;
import static org.chocosolver.util.ESat.*;
/**
* This class is inspired from :
*
* Inspired from "Unifying search algorithms for CSP" N. Jussien and O. Lhomme, Technical report 02-3-INFO, EMN
*
*
* It declares a search loop made of three components:
*
* -
* Propagate: it aims at propagating information throughout the constraint network when a decision is made,
*
* -
* Learn: it aims at ensuring that the search mechanism will avoid (as much as possible) to get back to states that have been explored and proved to be solution-less,
*
* -
* Move: aims at, unlike other ones, not pruning the search space but rather exploring it.
*
*
*
*
* Created by cprudhom on 01/09/15.
* Project: choco.
*
* @author Charles Prud'homme
* @since 01/09/15.
*/
public class Solver implements ISolver, IMeasures, IOutputFactory {
/**
* Define the possible actions of SearchLoop
*/
public enum Action {
/**
* Initialization step
*/
initialize,
/**
* propagation step
*/
propagate,
/**
* fixpoint step
*/
fixpoint,
/**
* extension step
*/
extend,
/**
* validation step
*/
validate,
/**
* reparation step
*/
repair
}
////////////////////////////////////////////////////////////////////////////////////////////////////////////////////
/////////////////////////////////////// PRIVATE FIELDS //////////////////////////////////////////////////////
////////////////////////////////////////////////////////////////////////////////////////////////////////////////////
/**
* The learning component of this search loop
*/
protected Learn L;
/**
* The moving component of this search loop
*/
protected Move M;
/**
* The declaring model
*/
protected Model mModel;
/**
* The objective manager declare
*/
@SuppressWarnings({"WeakerAccess", "rawtypes"})
protected IObjectiveManager objectivemanager;
/**
* The next action to execute in the search loop
*/
protected Action action;
/**
* The measure recorder to keep up to date
*/
@SuppressWarnings("WeakerAccess")
protected MeasuresRecorder mMeasures;
/**
* The current decision
*/
@SuppressWarnings("WeakerAccess")
protected DecisionPath dpath;
/**
* Index of the initial world, before initialization.
* May be different from 0 if some external backups have been made.
*/
private int rootWorldIndex = 0;
/**
* Index of the world where the search starts, after initialization.
*/
private int searchWorldIndex = 0;
/**
* List of stopping criteria.
* When at least one is satisfied, the search loop ends.
*/
protected List criteria;
/**
* Indicates if the default search loop is in use (set to true in that case).
*/
private boolean defaultSearch = false;
/**
* Indicates if a complementary search strategy should be added (set to true in that case).
*/
private boolean completeSearch = false;
/**
* An events observer
*/
private AbstractEventObserver eventObserver;
/**
* List of search monitors attached to this search loop
*/
@SuppressWarnings("WeakerAccess")
protected SearchMonitorList searchMonitors;
/**
* The propagation engine to use
*/
protected PropagationEngine engine;
/**
* Internal unique contradiction exception, used on propagation failures
*/
protected final ContradictionException exception;
/**
* Problem feasbility:
* - UNDEFINED if unknown,
* - TRUE if satisfiable,
* - FALSE if unsatisfiable
*/
protected ESat feasible = ESat.UNDEFINED;
/**
* Counter that indicates how many world should be rolled back when backtracking
*/
private int jumpTo;
/**
* Set to true to stop the search loop
**/
protected boolean stop;
/**
* Set to true when no more reparation can be achieved, ie entire search tree explored.
*/
private boolean canBeRepaired = true;
/**
* The restarting strategy
*/
private AbstractRestart restarter;
/**
* This object is accessible lazily
*/
private Solution lastSol = null;
/**
* Store hints on partial solution, to better start the search
*/
private WarmStart warmStart = null;
/**
* Default logger
*/
private Logger logger = new ANSILogger();
////////////////////////////////////////////////////////////////////////////////////////////////////////////////////
/////////////////////////////////////// CONSTRUCTOR //////////////////////////////////////////////////////
////////////////////////////////////////////////////////////////////////////////////////////////////////////////////
/**
* Create a resolver based for the model aModel.
*
* @param aModel the target model
*/
protected Solver(Model aModel) {
mModel = aModel;
engine = new PropagationEngine(mModel);
exception = new ContradictionException();
eventObserver = AbstractEventObserver.SILENT_OBSERVER;
objectivemanager = ObjectiveFactory.SAT();
dpath = new DecisionPath(aModel.getEnvironment());
action = initialize;
mMeasures = new MeasuresRecorder(mModel.getName());
criteria = new ArrayList<>();
mMeasures.setSearchState(SearchState.NEW);
mMeasures.setBoundsManager(objectivemanager);
searchMonitors = new SearchMonitorList();
setMove(new MoveBinaryDFS());
setNoLearning();
restarter = AbstractRestart.NO_RESTART;
}
public void throwsException(ICause c, Variable v, String s) throws ContradictionException {
throw exception.set(c, v, s);
}
public ContradictionException getContradictionException() {
return exception;
}
////////////////////////////////////////////////////////////////////////////////////////////////////////////////////
/////////////////////////////////////// SEARCH LOOP //////////////////////////////////////////////////////
////////////////////////////////////////////////////////////////////////////////////////////////////////////////////
/**
* Executes the resolver as it is configured.
*
* Default configuration:
* - SATISFACTION : Computes a feasible solution. Use while(solve()) to enumerate all solutions.
* - OPTIMISATION : Computes a feasible solution, wrt to the objective defined. Use while(solve()) to find the optimal solution.
* Indeed, each new solution improves the objective. If no new solution is found (and no stop criterion encountered),
* the last one is guaranteed to be the optimal one.
*
* @return if at least one new solution has been found.
*/
public boolean solve() {
mMeasures.setSearchState(SearchState.RUNNING);
// prepare
boolean satPb = getModel().getResolutionPolicy() == ResolutionPolicy.SATISFACTION;
if (getModel().getObjective() == null && !satPb) {
throw new SolverException("No objective variable has been defined whereas policy implies optimization");
}
stop = !canBeRepaired;
if (action == initialize) {
searchMonitors.beforeInitialize();
boolean ok = initialize();
searchMonitors.afterInitialize(ok);
}
// solve
boolean newSolutionFound = searchLoop();
// close
searchMonitors.beforeClose();
closeSearch();
searchMonitors.afterClose();
// restoration
return newSolutionFound;
}
/**
* Executes the search loop
*
* @return true if ends on a solution, false otherwise
*/
@SuppressWarnings("WeakerAccess")
public boolean searchLoop() {
boolean solution = false;
boolean left = true;
Thread th = Thread.currentThread();
while (!stop) {
stop = isStopCriterionMet();
if (stop || th.isInterrupted()) {
if (stop) {
mMeasures.setSearchState(SearchState.STOPPED);
} else {
mMeasures.setSearchState(SearchState.KILLED);
}
}
switch (action) {
case initialize:
throw new UnsupportedOperationException("should not initialize during search loop");
case propagate:
propagate(left);
break;
case fixpoint:
fixpoint();
break;
case extend:
left = true;
extend();
break;
case repair:
left = false;
repair();
break;
case validate:
stop = solution = validate();
break;
default:
throw new SolverException("Invalid Solver loop action " + action);
}
}
return solution;
}
////////////////////////////////////////////////////////////////////////////////////////////////////////////////////
/////////////////////////////////////// MAIN METHODS //////////////////////////////////////////////////////
////////////////////////////////////////////////////////////////////////////////////////////////////////////////////
/**
* Preparation of the search:
* - start time recording,
* - store root world
* - push a back up world,
* - run the initial propagation,
* - initialize the Move and the search strategy
*/
protected boolean initialize() {
boolean ok = true;
if (mModel.getSettings().checkDeclaredConstraints()) {
//noinspection unchecked
Set instances = (Set) mModel.getHook("cinstances");
if (instances != null) {
Optional undeclared = instances
.stream()
.filter(c -> (c.getStatus() == FREE))
.findFirst();
if (undeclared.isPresent()) {
logger.white().println(
"At least one constraint is free, i.e., neither posted or reified. ).");
instances
.stream()
.filter(c -> c.getStatus() == FREE)
.limit(mModel.getSettings().printAllUndeclaredConstraints() ? Integer.MAX_VALUE
: 1)
.forEach(c -> logger.white().printf(String.format("%s is free\n", c)));
}
}
}
engine.initialize();
getMeasures().setReadingTimeCount(System.nanoTime() - mModel.getCreationTime());
// end note
mMeasures.startStopwatch();
rootWorldIndex = mModel.getEnvironment().getWorldIndex();
// Indicates which decision was previously applied before selecting the move.
// Always sets to ROOT for the first move
M.setTopDecisionPosition(0);
mModel.getEnvironment().worldPush(); // store state before initial propagation; w = 0 -> 1
try {
if (mModel.getHook(Model.TASK_SET_HOOK_NAME) != null) {
//noinspection unchecked
ArrayList tset = (ArrayList) mModel.getHook(Model.TASK_SET_HOOK_NAME);
for (int i = 0; i < tset.size(); i++) {
tset.get(i).ensureBoundConsistency();
}
}
mMeasures.incFixpointCount();
doPropagate();
action = extend;
mModel.getEnvironment().worldPush(); // store state after initial propagation; w = 1 -> 2
searchWorldIndex = mModel.getEnvironment().getWorldIndex(); // w = 2
mModel.getEnvironment().worldPush(); // store another time for restart purpose: w = 2 -> 3
} catch (ContradictionException ce) {
engine.flush();
mMeasures.incFailCount();
searchMonitors.onContradiction(ce);
L.record(this);
L.forget(this);
mModel.getEnvironment().worldPop();
stop = true;
ok = false;
}
// call to HeuristicVal.update(Action.initial_propagation)
if (M.getChildMoves().size() <= 1 && M.getStrategy() == null) {
if (getModel().getSettings().warnUser()) {
logger.white().println("No search strategies defined.");
logger.white().println("Set to default ones.");
}
defaultSearch = true;
setSearch(mModel.getSettings().makeDefaultSearch(mModel));
}
if (completeSearch && !defaultSearch) {
AbstractStrategy declared = M.getStrategy();
AbstractStrategy complete = mModel.getSettings().makeDefaultSearch(mModel);
setSearch(declared, complete);
}
if (warmStart != null) {
AbstractStrategy declared = M.getStrategy();
warmStart.setStrategy(declared);
setSearch(warmStart);
}
restarter.init();
if (!M.init()) { // the initialisation of the Move and strategy can detect inconsistency
mModel.getEnvironment().worldPop();
feasible = FALSE;
engine.flush();
getMeasures().incFailCount();
ok = stop = true;
}
criteria.stream().filter(c -> c instanceof ICounter).forEach(c -> ((ICounter) c).init());
return ok;
}
/**
* Basic propagation:
*
* - First, prepare the decision (to ensure good behavior of the
* {@link org.chocosolver.solver.search.loop.move.Move#repair(Solver)} call)
* - then, a first propagation ensures that, if learning is on,
* the unit no-good learnt on failure filters,
*
- the cut is posted before applying the decision to ensure good nogood,
* and a second propagation ensures the cut is taken into account
* - the decision is applied (if learning is on and the decision is refuted,
* it is bypassed by the learnt unit nogood),
* - finally, a fix point is reached.
*
* @throws ContradictionException if failure occurs during propagation
*/
private void doPropagate() throws ContradictionException {
//WARNING: keep the order as is (read javadoc for more details)
dpath.buildNext();
objectivemanager.postDynamicCut();
engine.propagate();
dpath.apply();
engine.propagate();
}
/**
* Search loop propagation phase. This needs to be distinguished from {@link #propagate()}
*
* @param left true if we are branching on the left false otherwise
*/
protected void propagate(boolean left) {
searchMonitors.beforeDownBranch(left);
try {
mMeasures.incFixpointCount();
doPropagate();
action = extend;
} catch (ContradictionException ce) {
engine.flush();
mMeasures.incFailCount();
jumpTo = 1;
action = repair;
searchMonitors.onContradiction(ce);
}
searchMonitors.afterDownBranch(left);
}
private void fixpoint() {
try {
mMeasures.incFixpointCount();
objectivemanager.postDynamicCut();
engine.propagate();
action = propagate;
} catch (ContradictionException ce) {
engine.flush();
// mMeasures.incFailCount();
jumpTo = 1;
action = repair;
searchMonitors.onContradiction(ce);
}
}
/**
* Search loop extend phase
*/
protected void extend() {
searchMonitors.beforeOpenNode();
mMeasures.incNodeCount();
action = propagate;
if (restarter.mustRestart(this)) {
this.restart();
} else if (!M.extend(this)) {
action = validate;
}
searchMonitors.afterOpenNode();
}
/**
* Search loop repair phase
*/
protected void repair() {
if (L.record(this)) {
// this is done before the reparation,
// since restart is a move which can stop the search if the cut fails
action = fixpoint;
} else {
// this is done before the reparation,
// since restart is a move which can stop the search if the cut fails
action = propagate;
}
searchMonitors.beforeUpBranch();
if (restarter.mustRestart(this)) {
canBeRepaired = true;
this.restart();
} else {
canBeRepaired = M.repair(this);
}
searchMonitors.afterUpBranch();
if (!canBeRepaired) {
stop = true;
} else {
L.forget(this);
}
}
/**
* Search loop validate phase
*
* @return true if a solution is found
*/
private boolean validate() {
if (!getModel().getSettings().checkModel(this)) {
throw new InvalidSolutionException("The current solution does not satisfy the checker." +
"Either (a) the search strategy is not complete or " +
"(b) the model is not constrained enough or " +
"(c) a constraint's checker (\"isSatisfied()\") is not correct or " +
"(d) some constraints' filtering algorithm (\"propagate(...)\") is not correct.\n" +
Reporting.fullReport(mModel),
mModel);
}
feasible = TRUE;
mMeasures.incSolutionCount();
if (mModel.getResolutionPolicy() == ResolutionPolicy.SATISFACTION && mMeasures.getSolutionCount() == 1) {
mMeasures.updateTimeToBestSolution();
} else if (mModel.getResolutionPolicy() != ResolutionPolicy.SATISFACTION) {
boolean bestSolutionHasBeenUpdated = objectivemanager.updateBestSolution();
if (bestSolutionHasBeenUpdated) {
mMeasures.updateTimeToBestSolution();
}
}
searchMonitors.onSolution();
jumpTo = 1;
action = repair;
return true;
}
/**
* Close the search:
* - set satisfaction
* - update statistics
*/
private void closeSearch() {
if (mMeasures.getSearchState() == SearchState.RUNNING) {
mMeasures.setSearchState(SearchState.TERMINATED);
}
feasible = FALSE;
if (mMeasures.getSolutionCount() > 0) {
feasible = TRUE;
if (objectivemanager.isOptimization()) {
mMeasures.setObjectiveOptimal(!isStopCriterionMet());
}
} else if (isStopCriterionMet()) {
mMeasures.setObjectiveOptimal(false);
feasible = UNDEFINED;
}
}
/**
*
* Resetting a solver to the state just before running the last resolution instruction.
* That is, {@link Learn}, {@link Move} and {@link Search} are kept as declared.
* {@link ISearchMonitor} are also kept plugged to the search loop.
*
*
* For hard reset, see {@link #hardReset()}.
*
* In details, calling this method will:
*
* - backtrack to {@link #rootWorldIndex}
* - set {@link #searchWorldIndex} to 0
* - set {@link #action} to {@link Action#initialize}
* - reset {@link #mMeasures}
* - flush {@link #engine}
* - synchronize {@link #dpath} to erase out-dated decisions, presumably all of them
* - reset bounds of {@link #objectivemanager} (calling {@link IObjectiveManager#resetBestBounds()}
* - remove all stop criteria {@link #removeAllStopCriteria()}
* - set {@link #feasible} to {@link ESat#UNDEFINED}
*
*
* @see #hardReset()
*/
public void reset() {
if (rootWorldIndex > -1) {
mModel.getEnvironment().worldPopUntil(rootWorldIndex);
}
searchWorldIndex = 0;
action = initialize;
mMeasures.reset();
engine.reset();
dpath.synchronize();
objectivemanager.resetBestBounds();
removeAllStopCriteria();
feasible = UNDEFINED;
jumpTo = 0;
stop = false;
canBeRepaired = true;
}
/**
*
* Resetting a solver to its creation state.
*
*
*
* For soft reset, see {@link #reset()}.
*
*
* In details, calling this method will, first call {@link #reset()} and then:
*
* - replace {@link #M} by {@link MoveBinaryDFS}
* - call {@link Solver#setNoLearning()}
* - remove warm start hints
* - clear {@link #searchMonitors}, that forget any declared one
* - call {@link Model#removeMinisat()}
*
*
*
* @see #reset()
*/
public void hardReset() {
reset();
this.M.removeStrategy();
setMove(new MoveBinaryDFS());
setNoLearning();
//no need to unplug, done by searchMonitors.reset()
this.lastSol = null;
if (this.warmStart != null) {
this.warmStart.clearHints();
this.warmStart = null;
}
searchMonitors.reset();
defaultSearch = false;
completeSearch = false;
mModel.removeMinisat();
}
/**
* Propagates constraints and related events through the constraint network until a fix point is find,
* or a contradiction is detected.
*
* @throws ContradictionException inconsistency is detected, the problem has no solution with the current set of domains and constraints.
* @implNote The propagation engine is ensured to be empty (no pending events) after this method.
* Indeed, if no contradiction occurs, a fix point is reached.
* Otherwise, a call to {@link PropagationEngine#flush()} is made.
*/
public void propagate() throws ContradictionException {
if (!engine.isInitialized()) {
engine.initialize();
}
if (mModel.getHook(Model.TASK_SET_HOOK_NAME) != null) {
//noinspection unchecked
ArrayList tset = (ArrayList) mModel.getHook(Model.TASK_SET_HOOK_NAME);
for (int i = 0; i < tset.size(); i++) {
tset.get(i).ensureBoundConsistency();
}
}
try {
engine.propagate();
} finally {
engine.flush();
}
}
/**
* Return the minimum conflicting set from a conflicting set that is causing contradiction.
*
* @param conflictingSet the super-set of constraints causing contradiction
* @return minimumConflictingSet of constraints (the root cause of contradiction)
* @throws SolverException when MCS is called during solving
*/
public List findMinimumConflictingSet(List conflictingSet) {
if (isSolving()) {
throw new SolverException("Minimum Conflicting Set (MCS) can't be executed during solving");
}
return new QuickXPlain(getModel()).findMinimumConflictingSet(conflictingSet);
}
/**
* Sets the following action in the search to be a restart instruction.
* Note that the restart may not be immediate
*/
public void restart() {
searchMonitors.beforeRestart();
restoreRootNode();
mModel.getEnvironment().worldPush();
getMeasures().incRestartCount();
try {
objectivemanager.postDynamicCut();
mMeasures.incFixpointCount();
doPropagate();
action = extend;
} catch (ContradictionException e) {
// trivial inconsistency is detected, due to the cut
stop = true;
}
searchMonitors.afterRestart();
}
/**
* Retrieves the state of the root node (after the initial propagation)
* Has an immediate effect
*/
private void restoreRootNode() {
IEnvironment environment = mModel.getEnvironment();
while (environment.getWorldIndex() > searchWorldIndex) {
getMeasures().incBackTrackCount();
environment.worldPop();
}
dpath.synchronize();
}
/**
*
* Move forward in the search space by adding a new decision.
* A call to this method will :
*
* - add dec to the decision path
* - push a back-up copy of internal states
* - propagate
*
*
* Steps 1. and 2. are ignored when dec is null.
*
* In case of success, a call {@link #moveForward(Decision)} is possible.
* Otherwise, a call {@link #moveBackward()} is required to keep on exploring the search space.
* If no such call is done, the state maybe inconsistent with the decision path.
*
*
* Example of usage: looking for all solutions of a problem.
*
* {@code
* // Declare model, variables and constraints, then
* Decision dec = null;
* boolean search = true;
* while(search) {
* if (solver.moveForward(dec)) {
* dec = strategy.getDecision();
* if (dec == null) {
* // here a solution is found
* }else {
* continue;
* }
* }
* search = solver.moveBackward();
* dec = strategy.getDecision();
* }
* }
*
* @param decision decision to add, can be null.
* @return true if extension is successful, false otherwise.
* @see #moveBackward()
* @see #getDecisionPath()
* @see AbstractStrategy#getDecision()
*/
public boolean moveForward(Decision decision) {
if (!engine.isInitialized()) {
engine.initialize();
}
if (this.getEnvironment().getWorldIndex() == 0) {
this.getEnvironment().worldPush();
}
boolean success = true;
if (decision != null) { // null means there is no more decision
this.getDecisionPath().pushDecision(decision);
this.getEnvironment().worldPush();
this.getDecisionPath().buildNext();
}
try {
this.getDecisionPath().apply();
this.getObjectiveManager().postDynamicCut();
this.getEngine().propagate();
} catch (ContradictionException cex) {
engine.flush();
success = false;
}
return success;
}
/**
*
* Move backward in the search space.
* A call to this method will :
*
* - pop the last copy of internal states
* - refute the last decision of the decision path
* - propagate
*
* If step 2. is not possible or step 3. throws a failure,
* the last decision of the decision path is popped and the three-step loop is applied
* until a successful refutation or emptying decision path.
*
* In case of success, a call {@link #moveForward(Decision)} is possible.
*
*
* Example of usage: looking for all solutions of a problem.
*
* {@code
* // Declare model, variables and constraints, then
* Decision dec = null;
* boolean search = true;
* while(search) {
* if (solver.moveForward(dec)) {
* dec = strategy.getDecision();
* if (dec == null) {
* // here a solution is found
* }else {
* continue;
* }
* }
* search = solver.moveBackward();
* dec = strategy.getDecision();
* }
* }
*
* @return true in case of success, false otherwise
* @see #moveForward(Decision)
* @see #getDecisionPath()
*/
public boolean moveBackward() {
this.getEnvironment().worldPop();
boolean success = false;
Decision head = dpath.getLastDecision();
while (!success && head.getPosition() > 0) {
if (head.hasNext()) {
this.getEnvironment().worldPush();
this.getDecisionPath().buildNext();
try {
this.getDecisionPath().apply();
this.getObjectiveManager().postDynamicCut();
this.getEngine().propagate();
success = true;
} catch (ContradictionException cex) {
engine.flush();
}
} else {
dpath.synchronize();
this.getEnvironment().worldPop();
}
head = dpath.getLastDecision();
}
return success;
}
/**
* Solving is executing if the search state is different from NEW, that is,
* if it has started to branch decisions.
* A double check for execution is done looking if the environment trailing
* has started as well.
*
* @return isSolving if the solver is executing searching or branching
*/
public boolean isSolving() {
boolean isSearching = getSearchState() != SearchState.NEW;
boolean isTrailing = getEnvironment().getWorldIndex() > rootWorldIndex;
return isSearching || isTrailing;
}
////////////////////////////////////////////////////////////////////////////////////////////////////////////////////
/////////////////////////////////////// GETTERS //////////////////////////////////////////////////////
////////////////////////////////////////////////////////////////////////////////////////////////////////////////////
/**
* @return the model of this resolver
*/
public Model getModel() {
return mModel;
}
/**
* @return the current learn.
*/
public Learn getLearner() {
return L;
}
/**
* @return the current move.
*/
public Move getMove() {
return M;
}
/**
* @deprecated
*/
@Deprecated
public Propagate getPropagate() {
return null;
}
/**
* @return the backtracking environment used for this solver
*/
public IEnvironment getEnvironment() {
return getModel().getEnvironment();
}
/**
* @return the current decision path
*/
public DecisionPath getDecisionPath() {
return dpath;
}
/**
* @param kind of variables the search strategy deals with
* @return the current search strategy in use
*/
public AbstractStrategy getSearch() {
if (M.getChildMoves().size() > 1 && mModel.getSettings().warnUser()) {
logger.bold().println(
"This search loop is based on a sequential Move, the returned strategy may not reflect the reality.");
}
return M.getStrategy();
}
/**
* @param type of the objective variable
* @return the currently used objective manager
*/
@SuppressWarnings("unchecked")
public IObjectiveManager getObjectiveManager() {
return objectivemanager;
}
/**
* Indicates if the default search strategy is used
*
* @return false if a specific search strategy is used
*/
public boolean isDefaultSearchUsed() {
return defaultSearch;
}
/**
* Indicates if the search strategy is completed with one over all variables
*
* @return false if no strategy over all variables complete the declared one
*/
public boolean isSearchCompleted() {
return completeSearch;
}
/**
* @return true if the search loops ends unexpectedly (externally killed, for instance).
*/
@SuppressWarnings("unused")
public boolean hasEndedUnexpectedly() {
return mMeasures.getSearchState() == SearchState.KILLED;
}
/**
* @return true if the search loops encountered at least one of the stop criteria declared.
*/
public boolean isStopCriterionMet() {
boolean ismet = false;
for (int i = 0; i < criteria.size() && !ismet; i++) {
ismet = criteria.get(i).isMet();
}
return ismet;
}
/**
* @return the index of the world where the search starts, after initialization.
*/
public int getSearchWorldIndex() {
return searchWorldIndex;
}
/**
* Returns a reference to the measures recorder.
* This enables to get, for instance, the number of solutions found, time count, etc.
*
* @return this model's measure recorder
*/
public MeasuresRecorder getMeasures() {
//TODO Should the user have write-permission on the solver measures ?
return mMeasures;
}
/**
* Return the events observer plugged into {@code this}.
*
* @return this events observer
*/
public AbstractEventObserver getEventObserver() {
return eventObserver;
}
/**
* @return the propagation engine used in {@code this}.
*/
public PropagationEngine getEngine() {
return engine;
}
/**
* Returns information on the feasibility of the current problem defined by the solver.
*
* Possible back values are:
*
- {@link ESat#TRUE}: a solution has been found,
*
- {@link ESat#FALSE}: the CSP has been proven to have no solution,
*
- {@link ESat#UNDEFINED}: no solution has been found so far (within given limits)
* without proving the unfeasibility, though.
*
* @return an {@link ESat}.
*/
public ESat isFeasible() {
return feasible;
}
/**
* Return the current state of the CSP.
*
* Given the current domains, it can return a value among:
*
- {@link ESat#TRUE}: all constraints of the CSP are satisfied for sure,
*
- {@link ESat#FALSE}: at least one constraint of the CSP is not satisfied.
*
- {@link ESat#UNDEFINED}: neither satisfiability nor unsatisfiability could be proven so far.
*
* Presumably, not all variables are instantiated.
*
* @return ESat.TRUE if all constraints of the problem are satisfied,
* ESat.FLASE if at least one constraint is not satisfied,
* ESat.UNDEFINED neither satisfiability nor unsatisfiability could be proven so far.
*/
public ESat isSatisfied() {
int OK = 0;
for (Constraint c : mModel.getCstrs()) {
if (c.isEnabled()) {
ESat satC = c.isSatisfied();
if (FALSE == satC) {
if (getModel().getSettings().warnUser()) {
logger.bold().red().printf("FAILURE >> %s (%s)%n", c, satC);
}
return FALSE;
} else if (TRUE == satC) {
OK++;
}
} else {
OK++;
}
}
if (OK == mModel.getCstrs().length) {
return TRUE;
} else {
return UNDEFINED;
}
}
/**
* @return how many worlds should be rolled back when backtracking (usually 1)
*/
public int getJumpTo() {
return jumpTo;
}
/**
* @return true when learning algorithm is not plugged in
*/
public boolean isLearnOff() {
return L instanceof LearnNothing;
}
////////////////////////////////////////////////////////////////////////////////////////////////////////////////////
/////////////////////////////////////// SETTERS //////////////////////////////////////////////////////
////////////////////////////////////////////////////////////////////////////////////////////////////////////////////
/**
* Replaces the current learn with {@code l}
*
* @param l the new learn to apply
*/
public void setLearner(Learn l) {
this.L = l;
}
/**
* Replaces the current move with {@code m}
*
* @param m the new move to apply
*/
public void setMove(Move... m) {
if (m == null) {
this.M = null;
} else if (m.length == 1) {
this.M = m[0];
} else {
this.M = new MoveSeq(getModel(), m);
}
}
/**
* @deprecated
*/
@Deprecated
public void setPropagate(Propagate p) {
}
/**
* Add or complete a restart policy.
*
* @param restarter restarter policy
* @implNote There can be multiple restart policies, stored in as linked list.
* @see #clearRestarter()
*/
public void addRestarter(AbstractRestart restarter) {
restarter.setNext(this.restarter);
this.restarter = restarter;
}
/**
* @return the current declared restart policy or {@link AbstractRestart#NO_RESTART}
*/
public AbstractRestart getRestarter() {
return this.restarter;
}
/**
* Clear the declared restart strategy.
* Consequently, no restarting will occur.
*
* @implNote replace the declared restart policy by {@link AbstractRestart#NO_RESTART}
*/
public void clearRestarter() {
this.restarter = AbstractRestart.NO_RESTART;
}
/**
* Declares an objective manager to use.
*
* @param om the objective manager to use instead of the declared one (if any).
*/
public void setObjectiveManager(IObjectiveManager om) {
this.objectivemanager = om;
mMeasures.setBoundsManager(om);
}
/**
* Override the default search strategies to use in {@code this}.
* In case many strategies are given, they will be called in sequence:
* The first strategy in parameter is first called to compute a decision, if possible.
* If it cannot provide a new decision, the second strategy is called ...
* and so on, until the last strategy.
*
*
* @param strategies the search strategies to use.
*/
public void setSearch(AbstractStrategy... strategies) {
if (strategies == null || strategies.length == 0) {
throw new UnsupportedOperationException("no search strategy has been specified");
}
if (M.getChildMoves().size() > 1) {
throw new UnsupportedOperationException("The Move declared is composed of many Moves.\n" +
"A strategy must be attached to each of them independently, and it cannot be achieved calling this method." +
"An iteration over it child moves is needed: this.getMove().getChildMoves().");
} else {
//noinspection unchecked
M.setStrategy(strategies.length == 1 ? strategies[0] : Search.sequencer(strategies));
}
}
/**
* Overrides the explanation engine.
*
* @param explainer the explanation to use
*/
public void setEventObserver(AbstractEventObserver explainer) {
this.eventObserver = explainer;
}
/**
* Attaches a propagation engine {@code this}.
* It overrides the previously defined one, only
* if no propagation was done yet.
* Indeed, some incremental propagators may have set up their internal structure,
* which cannot be set up twice safely.
*
* If propagation was done calling {@link #solve()},
* calling {@link #reset()} enables to set the propagation engine anew.
*
* If propagation was done "manually" (calling {@link #propagate()}, then nothing can be done.
*
* @param propagationEngine a propagation strategy
* @throws SolverException is already initialized.
*/
public void setEngine(PropagationEngine propagationEngine) {
if (!engine.isInitialized()
|| getEnvironment().getWorldIndex() == rootWorldIndex) {
this.engine = propagationEngine;
} else {
throw new SolverException("Illegal propagation engine modification.");
}
}
/**
* Completes (or not) the declared search strategy with one over all variables
*
* @param isComplete set to true to complete the current search strategy
*/
@SuppressWarnings("WeakerAccess")
public void makeCompleteStrategy(boolean isComplete) {
this.completeSearch = isComplete;
}
/**
* Declare a warm start strategy that consists of a set of variables and a set of values.
* It allows to define either a solution or at least a partial solution in order to drive the search toward
* a solution.
*
Such a (partial) solution serves only once.
*
Note that a variable can appears more than once.
*/
public void addHint(IntVar var, int val) {
if (warmStart == null) {
warmStart = new WarmStart(this);
}
warmStart.addHint(var, val);
}
/**
* Remove declare hints
*/
public void removeHints() {
setSearch(warmStart.getStrategy());
warmStart.clearHints();
warmStart = null;
}
/**
* Adds a stop criterion, which, when met, stops the search loop.
* There can be multiple stop criteria, a logical OR is then applied.
* The stop criteria are declared to the search loop just before launching the search,
* the previously defined ones are erased.
*
* There is no check if there are any duplicates.
*
*
* Examples:
*
* With a built-in counter, stop after 20 seconds:
*
* SMF.limitTime(solver, "20s");
*
* With lambda, stop when 10 nodes are visited:
*
* () -> solver.getNodeCount() >= 10
*
*
* @param criterion one or many stop criterion to add.
* @see #removeStopCriterion(Criterion...)
* @see #removeAllStopCriteria()
*/
public void addStopCriterion(Criterion... criterion) {
if (criterion != null) {
Collections.addAll(criteria, criterion);
}
}
/**
* Removes one or many stop criterion from the one to declare to the search loop.
*
* @param criterion criterion to remove
*/
public void removeStopCriterion(Criterion... criterion) {
if (criterion != null) {
for (Criterion c : criterion) {
criteria.remove(c);
}
}
}
/**
* Empties the list of stop criteria declared.
* This is automatically called on {@link #reset()}.
*/
@SuppressWarnings("WeakerAccess")
public void removeAllStopCriteria() {
this.criteria.clear();
}
/**
* @return the list of search monitors plugged in this resolver
*/
public SearchMonitorList getSearchMonitors() {
return searchMonitors;
}
/**
* Put a search monitor to react on search events (solutions, decisions, fails, ...).
* Any search monitor is actually plugged just before the search starts.
*
* There is no check if there are any duplicates.
* A search monitor added during while the resolution has started will not be taken into account.
*
* @param sm a search monitor to be plugged in the solver
*/
public void plugMonitor(ISearchMonitor sm) {
searchMonitors.add(sm);
}
/**
* Removes a search monitors from the ones to plug when the search will start.
*
* @param sm a search monitor to be unplugged in the solver
*/
public void unplugMonitor(ISearchMonitor sm) {
searchMonitors.remove(sm);
}
/**
* Empties the list of search monitors.
*/
@SuppressWarnings("WeakerAccess")
public void unplugAllSearchMonitors() {
searchMonitors.reset();
}
/**
* Sets how many worlds to rollback when backtracking
*
* @param jto how many worlds to rollback when backtracking
*/
public void setJumpTo(int jto) {
this.jumpTo = jto;
}
/**
* The first call to this method will create a new solution based on all variables
* of the model and attach it to this.
* Next calls return the solution instance.
*
* @return a global solution.
*/
public Solution defaultSolution() {
if (lastSol == null) {
lastSol = new Solution(this.getModel());
this.attach(lastSol);
}
return lastSol;
}
////////////////////////////////////////////////////////////////////////////////////////////////////////////////////
/////////////////////////////////////// FACTORY //////////////////////////////////////////////////////
////////////////////////////////////////////////////////////////////////////////////////////////////////////////////
@Override
public Solver ref() {
return this;
}
////////////////////////////////////////////////////////////////////////////////////////////////////////////////////
/////////////////////////////////////// MEASURES //////////////////////////////////////////////////////
////////////////////////////////////////////////////////////////////////////////////////////////////////////////////
@Override
public String getModelName() {
return getMeasures().getModelName();
}
@Override
public long getTimestamp() {
return getMeasures().getTimestamp();
}
@Override
public float getTimeCount() {
return getMeasures().getTimeCount();
}
@Override
public long getTimeCountInNanoSeconds() {
return getMeasures().getTimeCountInNanoSeconds();
}
@Override
public long getTimeToBestSolutionInNanoSeconds() {
return getMeasures().getTimeToBestSolutionInNanoSeconds();
}
@Override
public long getReadingTimeCountInNanoSeconds() {
return getMeasures().getReadingTimeCountInNanoSeconds();
}
@Override
public float getReadingTimeCount() {
return getMeasures().getReadingTimeCount();
}
@Override
public long getNodeCount() {
return getMeasures().getNodeCount();
}
@Override
public long getBackTrackCount() {
return getMeasures().getBackTrackCount();
}
@Override
public long getBackjumpCount() {
return getMeasures().getBackjumpCount();
}
@Override
public long getFailCount() {
return getMeasures().getFailCount();
}
@Override
public long getFixpointCount() {
return getMeasures().getFixpointCount();
}
@Override
public long getRestartCount() {
return getMeasures().getRestartCount();
}
@Override
public long getSolutionCount() {
return getMeasures().getSolutionCount();
}
@Override
public long getDecisionCount() {
return getMeasures().getDecisionCount();
}
@Override
public long getMaxDepth() {
return getMeasures().getMaxDepth();
}
@Override
public long getCurrentDepth() {
return getDecisionPath().size();
}
@Override
public boolean hasObjective() {
return getMeasures().hasObjective();
}
@Override
public boolean isObjectiveOptimal() {
return getMeasures().isObjectiveOptimal();
}
@Override
public Number getBestSolutionValue() {
return getMeasures().getBestSolutionValue();
}
@Override
public SearchState getSearchState() {
return getMeasures().getSearchState();
}
/**
* @return the currently used objective manager
*/
@Override
public IBoundsManager getBoundsManager() {
assert getMeasures().getBoundsManager() == objectivemanager;
return getMeasures().getBoundsManager();
}
////////////////////////////////////////////////////////////////////////////////////////////////////////////////////
/////////////////////////////////////// OUTPUT ////////////////////////////////////////////////////////
////////////////////////////////////////////////////////////////////////////////////////////////////////////////////
/**
* Return the current used logger.
* By default, logger prints to {@link System#out}.
* Any trace from choco-solver are redirected to this logger.
*
* @return the current logger.
* @see #logWithANSI(boolean)
*/
public Logger log() {
return logger;
}
/**
* Defines whether (when {@code ansi} is set to {@code true}) or not
* ANSI tags are added to any trace from choco-solver.
*
* @param ansi {@code true} to enable colors
*/
public void logWithANSI(boolean ansi) {
logger = ansi ? new ANSILogger(logger) : new Logger(logger);
}
}