Java IloRange类代码示例

import ilog.concert.IloRange; //导入依赖的package包/类
@Override
public List<ConditionalConstraint> getConstraintList() {
	//There's some overhead here for avoiding duplicates
	Set<ConditionalConstraint> ccSet = new HashSet<ConditionalConstraint>();
	List<ConditionalConstraint> ccList = new ArrayList<ConditionalConstraint>();
	
	for (IloRange range : getRangeList()) {
		
		ConditionalConstraint cc = m_rangeMap.get( range );
		
		if (cc != null) {
			
			if (!ccSet.contains( cc )) {
		
				ccList.add( cc );
				ccSet.add( cc );
			}
		}
	}
	
	return ccList;
}
 

import ilog.concert.IloRange; //导入依赖的package包/类
/**
 * returns reactions that show conflict in current lp matrix
 * 
 * @param lp
 *            current calculated model
 * @return list of conflict reactions
 * @throws IloException
 */
protected ArrayList<String> getConflictReactions() throws IloException {
	ArrayList<String> conflict = new ArrayList<String>();
	IloRange[] x = m_lp.getRanges();
	double[] p = new double[x.length];
	for (int i = 0; i < p.length; i++) {
		p[i] = 1;
	}
	m_cplex.refineConflict(x, p);
	ConflictStatus[] cs = m_cplex.getConflict(x);
	for (int i = 0; i < cs.length; i++) {
		if (cs[i] == ConflictStatus.Member) {
			String name = x[i].getName();
			if (name.startsWith(ThermoChecker.RX_PREFIX) || name.startsWith(ThermoChecker.RX_REV_PREFIX)) {
				conflict.add(name);
			}
		}
	}
	return conflict;
}
 

import ilog.concert.IloRange; //导入依赖的package包/类
private List<IloRange> convertToIloConstraints(
    Iterable<Cutset<V, E>> cutsets) throws IloException {
  List<IloRange> ans = Lists.newArrayList();
  double best = 0;
  Cutset<V, E> bestCut = null;
  IloRange bestConstraint = null;
  for (Cutset<V, E> cut : cutsets) {
    IloRange constraint = makeCutsetConstraint(cut);
    if (cut.getViolation() > best) {
      bestCut = cut;
      best = cut.getViolation();
      bestConstraint = constraint;
    }
    ans.add(constraint);
  }
  // System.out.println(bestCut == null ? "0" : bestCut.getViolation());
  // return bestCut == null ? Lists.<IloConstraint>newArrayList():
  // Lists.<IloConstraint>newArrayList(bestConstraint);
  return ans;
}
 

import ilog.concert.IloRange; //导入依赖的package包/类
@Override
protected void main() throws IloException {
  if (!this.isAfterCutLoop()) {
    return;
  }
  int time = numUserCutCallback++;
  if (time % 500 == 0 && time != 0) {
    System.err.println("completed about: " + time
        + " user cut callbacks.  MIP gap: " + this.getMIPRelativeGap());
  }
  UserCutGenerator userCutGen = polytope.makeUserCutGenerator(this);
  List<IloRange> violatedConstraints = userCutGen.quickUserCut();
  if (!solverOptions.contains(SolverOption.disableFullUserCut)
      && violatedConstraints.size() == 0 && this.getNnodes64() == 0) {
    violatedConstraints = userCutGen.fullUserCut();
  }
  for (IloRange constraint : violatedConstraints) {
    this.add(constraint);
  }
}
 

import ilog.concert.IloRange; //导入依赖的package包/类
@Override
protected void main() throws IloException {
	int time = numLazyConstraintCallback++;
	if(time %500 == 0 && time != 0){
		System.err.println("completed about: " + time + " lazy cut callbacks.  Integrality gap: " + this.getMIPRelativeGap());
	}
	
	List<IloRange> violatedConstraints = Lists.newArrayList();
	violatedConstraints.addAll(phaseOneProblem.lazyConstraint(this));
	for(CycleChainPackingPolytope<V,E> phaseTwoProbem: phaseTwoProblems){
		violatedConstraints.addAll(phaseTwoProbem.lazyConstraint(this));
	}
	for(IloRange constraint: violatedConstraints){
		this.add(constraint);
	}
}
 

import ilog.concert.IloRange; //导入依赖的package包/类
/**
 * @param column
 * @param name
 * @return Index of the newly added column
 * @throws CPException
 */
@Override
public int addColumn(double[] column, String name) throws CPException {
	
	try {
		
		IloObjective obj = m_model.getObjective();
		IloColumn col = m_model.column( obj, column[0] );
		int index = 1;
		
		for (IloRange range : m_ranges) {
			//Install the new column at every constraint
			col = col.and( m_model.column( range, column[index++] ) );
		}
		
		addVar( m_model.numVar(col, 0.0, 1.0, name));
		m_objCoeffs = ArrayUtils.add(m_objCoeffs, column[0]);//TODO This must be damn fucking slow!
		
		return getColumnNumber() - 1 + getFirstColumnIndex();
		
	} catch( IloException e ) {

		m_logger.fatal( e );
		
		throw new CPException(e);
	}
}
 

import ilog.concert.IloRange; //导入依赖的package包/类
protected void addRange(IloNumVar[] vars, double[] coeffs, double lb, double ub, String name) throws IloException {
	
	IloNumExpr expr = m_model.scalProd( coeffs, vars );
	IloRange range = NumberUtils.equal( lb, ub, PRECISION_THRESHOLD )
		? m_model.addEq( expr, lb, name )
		: m_model.addRange( lb, expr, ub, name );

	addRange( range );		
}
 

import ilog.concert.IloRange; //导入依赖的package包/类
protected void removeRange(int index) throws IloException {
	
	IloRange range = m_ranges.get( index );
	
	m_model.delete( range );
	m_ranges.remove( index );
	m_nameRangeMap.remove( range.getName() );
}
 

import ilog.concert.IloRange; //导入依赖的package包/类
@Override
protected void removeRange(IloRange range) throws IloException {
	
	if (range != null) {
		
		m_rangeMap.remove( range );
		super.removeRange( range );
	}
}
 

import ilog.concert.IloRange; //导入依赖的package包/类
/**
 * Creates a new <code>HeuristicConcentration</code>
 * 
 * @param instance
 */
public HeuristicConcentration(IVRPInstance instance, ISolutionFactory soluitonFactory) {
    mInstance = instance;
    mSolutionFactory = soluitonFactory;
    mCoverCtrs = new IloRange[Utilities.getMaxId(instance.getRequests()) + 1];

}
 

import ilog.concert.IloRange; //导入依赖的package包/类
public void ChangeCoefficientsInSubModel(double[] shadowPrices, PartialSolution partialSolutions, 
        double bound, double gap) throws IloException{
    double[] tmp = new double[problemInstanceSingleDevice.getNumbSlots()];
    Helpers.InitializeTo(tmp, 1.0 / problemInstanceSingleDevice.getNumbSlots(), 0, problemInstanceSingleDevice.getNumbSlots());
     
    if(ReducedCost != null) patSolver.remove(ReducedCost);
    ReducedCost = patSolver.addMaximize(patSolver.diff(patSolver.scalProd(x, shadowPrices), patSolver.scalProd(x, tmp)));
   
    //setting bound on the criterion value
    if(bound < 0){
       patSolver.setParam(IloCplex.DoubleParam.CutLo, bound);
    }
    else{
       patSolver.setParam(IloCplex.DoubleParam.CutLo, -Double.MAX_VALUE);
    }
    
    if(fixedAssignmentRequirements != null) patSolver.remove(fixedAssignmentRequirements);
    fixedAssignmentRequirements = new IloRange [problemInstanceSingleDevice.getNumbSlots()];
    
    //Processing partial solutions
    for(int j = 0; j < problemInstanceSingleDevice.getNumbSlots(); j++){
        if(partialSolutions.getPartialSolutions()[problemInstanceSingleDevice.getNumbOfDeviceInArray()][j] == 1){
            fixedAssignmentRequirements[j] = patSolver.addEq(x[j], 1);
        }

        if(partialSolutions.getPartialSolutions()[problemInstanceSingleDevice.getNumbOfDeviceInArray()][j] == 0){
            fixedAssignmentRequirements[j] = patSolver.addEq(x[j], 0);
        }
     }
    
    //sub-model does not solve it to optimum, but integrality gap should be less than "gap" value
    patSolver.setParam(IloCplex.DoubleParam.EpGap, gap);
}
 

import ilog.concert.IloRange; //导入依赖的package包/类
/**
 * If a violated inequality has been found add it to the master problem.
 * @param subtourInequality subtour inequality
 */
private void addCut(SubtourInequality subtourInequality){
	if(masterData.subtourInequalities.containsKey(subtourInequality))
		throw new RuntimeException("Error, duplicate subtour cut is being generated! This cut should already exist in the master problem: "+subtourInequality);
	//Create the inequality in cplex
	try {
		IloLinearNumExpr expr=masterData.cplex.linearNumExpr();
		//Register the columns with this constraint.
		for(PricingProblemByColor pricingProblem : masterData.pricingProblems){
			for(Matching matching: masterData.getColumnsForPricingProblemAsList(pricingProblem)){
				//Test how many edges in the matching enter/leave the cutSet (edges with exactly one endpoint in the cutSet)
				int crossings=0;
				for(DefaultWeightedEdge edge: matching.edges){
					if(subtourInequality.cutSet.contains(dataModel.getEdgeSource(edge)) ^ subtourInequality.cutSet.contains(dataModel.getEdgeTarget(edge)))
						crossings++;
				}
				if(crossings>0){
					IloNumVar var=masterData.getVar(pricingProblem,matching);
					expr.addTerm(crossings, var);
				}
			}
		}
		IloRange subtourConstraint = masterData.cplex.addGe(expr, 2, "subtour");
		masterData.subtourInequalities.put(subtourInequality, subtourConstraint);
	} catch (IloException e) {
		e.printStackTrace();
	}
}
 

import ilog.concert.IloRange; //导入依赖的package包/类
/**
 * Build the cplex problem
 */
@Override
protected CuttingStockMasterData buildModel() {
	try {
		cplex =new IloCplex(); //Create cplex instance
		cplex.setOut(null); //Disable cplex output
		cplex.setParam(IloCplex.IntParam.Threads, config.MAXTHREADS); //Set number of threads that may be used by the cplex

		//Define the objective
		obj= cplex.addMinimize();

		//Define constraints
		satisfyDemandConstr=new IloRange[dataModel.nrFinals];
		for(int i=0; i< dataModel.nrFinals; i++)
			satisfyDemandConstr[i]= cplex.addRange(dataModel.demandForFinals[i], dataModel.demandForFinals[i], "satisfyDemandFinal_"+i);

		//Define a container for the variables
	} catch (IloException e) {
		e.printStackTrace();
	}

	//Define a container for the variables
	Map<PricingProblem,OrderedBiMap<CuttingPattern, IloNumVar>> varMap=new LinkedHashMap<>();
	varMap.put(pricingProblems.get(0),new OrderedBiMap<>());

	//Return a new data object which will hold data from the Master Problem. Since we are not working with inequalities in this example,
	//we can simply return the default.
	return new CuttingStockMasterData(varMap);
}
 

import ilog.concert.IloRange; //导入依赖的package包/类
/**
 * Using dynamic programming, this method computes an expression representing the value of a given action not chosen to be made optimal. This is done by creating an expression whose value is the sum of the value of descendant information sets under the action, according to the evaulation function and strategy for the rational player
 * @param informationSetId
 * @param dominatedAction the action to compute a value expression for
 * @param dominatedActionExpressionTable dynamic programming table
 * @return expression that represents the value of the action
 * @throws IloException
 */
private IloLinearNumExpr getDominatedActionExpression(int informationSetId, Action dominatedAction, HashMap<Action,IloLinearNumExpr> dominatedActionExpressionTable) throws IloException {
	if (dominatedActionExpressionTable.containsKey(dominatedAction)) {
		return dominatedActionExpressionTable.get(dominatedAction);
	} else {
		// this expression represents the value of dominatedAction
		IloLinearNumExpr expr = cplex.linearNumExpr();
		//TIntObjectMap<IloNumVar> informationSetToVariableMap = new TIntObjectHashMap<IloNumVar>();
		TIntObjectMap<HashMap<String, IloRange>> rangeMap = new TIntObjectHashMap<HashMap<String, IloRange>>();
		IloNumVar actionValueVar = cplex.numVar(-Double.MAX_VALUE, Double.MAX_VALUE, "DomActionValue;"+Integer.toString(informationSetId) + dominatedAction.getName());
		expr.addTerm(1, actionValueVar);
		IloRange range = cplex.addGe(actionValueVar, 0, actionValueVar.getName());
		// Iterate over nodes in information set and add value of each node for dominatedAction
		TIntArrayList informationSet = game.getInformationSet(playerNotToSolveFor, informationSetId);
		for (int i = 0; i < informationSet.size(); i++) {
			Node node = game.getNodeById(informationSet.get(i));
			// we need to locate the action that corresponds to dominatedAction at the current node, so that we can pull the correct childId
			Action dominatedActionForNode = node.getActions()[0];
			for (Action action : node.getActions()) {
				if (action.getName().equals(dominatedAction.getName())) dominatedActionForNode = action;
			}

			// find the descendant information sets and add their values to the expression
			//fillDominatedActionExpr(expr, dominatedActionForNode.getChildId(), informationSetToVariableMap, exprMap, 1, Integer.toString(informationSetId) + dominatedAction.getName());
			fillDominatedActionRange(range, dominatedActionForNode.getChildId(), rangeMap, 1, Integer.toString(informationSetId) + dominatedAction.getName());
		}
		// remember the expression for future method calls
		dominatedActionExpressionTable.put(dominatedAction, expr);
		
		return expr;
	}
}
 

import ilog.concert.IloRange; //导入依赖的package包/类
/**
 * Using dynamic programming, this method computes an expression representing the value of a given action not chosen to be made optimal. This is done by creating an expression whose value is the sum of the value of descendant information sets under the action, according to the evaulation function and strategy for the rational player
 * @param informationSetId
 * @param dominatedAction the action to compute a value expression for
 * @param dominatedActionExpressionTable dynamic programming table
 * @return expression that represents the value of the action
 * @throws IloException
 */
private IloLinearNumExpr getDominatedActionExpression(int informationSetId, Action dominatedAction, HashMap<Action,IloLinearNumExpr> dominatedActionExpressionTable) throws IloException {
    if (dominatedActionExpressionTable.containsKey(dominatedAction)) {
        return dominatedActionExpressionTable.get(dominatedAction);
    } else {
        // this expression represents the value of dominatedAction
        IloLinearNumExpr expr = cplex.linearNumExpr();
        //TIntObjectMap<IloNumVar> informationSetToVariableMap = new TIntObjectHashMap<IloNumVar>();
        TIntObjectMap<HashMap<String, IloRange>> rangeMap = new TIntObjectHashMap<HashMap<String, IloRange>>();
        IloNumVar actionValueVar = cplex.numVar(-Double.MAX_VALUE, Double.MAX_VALUE, "DomActionValue;"+Integer.toString(informationSetId) + dominatedAction.getName());
        expr.addTerm(1, actionValueVar);
        IloRange range = cplex.addGe(actionValueVar, 0, actionValueVar.getName());
        // Iterate over nodes in information set and add value of each node for dominatedAction
        TIntArrayList informationSet = game.getInformationSet(playerNotToSolveFor, informationSetId);
        for (int i = 0; i < informationSet.size(); i++) {
            Node node = game.getNodeById(informationSet.get(i));
            // we need to locate the action that corresponds to dominatedAction at the current node, so that we can pull the correct childId
            Action dominatedActionForNode = node.getActions()[0];
            for (Action action : node.getActions()) {
                if (action.getName().equals(dominatedAction.getName())) dominatedActionForNode = action;
            }

            // find the descendant information sets and add their values to the expression
            //fillDominatedActionExpr(expr, dominatedActionForNode.getChildId(), informationSetToVariableMap, exprMap, 1, Integer.toString(informationSetId) + dominatedAction.getName());
            fillDominatedActionRange(range, dominatedActionForNode.getChildId(), rangeMap, 1, Integer.toString(informationSetId) + dominatedAction.getName());
        }
        // remember the expression for future method calls
        dominatedActionExpressionTable.put(dominatedAction, expr);

        return expr;
    }
}