C++ IloModel::add方法代码示例

/* initialisiere N^2 Variablen und erstelle eine zu minimierende
 * Strecken-Zielfunktion über die Distanzmatrix c
 *
 * eigentlich sind nur (N*N-N)/2 Variablen nötig, aber dafür müsste
 * ich mir etwas schlaues zur Adressierung ausdenken (weil das untere linke
 * Dreieck einer Matrix adressiert werden muss, ist das nicht trivial)
 * Der Presolver scheint die überflüssigen Variablen allerdings
 * direkt zu verwerfen, weshalb das nicht dringend ist.
 * */
IloNumVarArray CplexTSPSolver::init_symmetric_var(IloModel model)
{
    IloEnv env = model.getEnv();

    // Edge Variables
    IloNumVarArray x(env);
    for(int i=0; i<N; i++)
        for(int j=0; j<N; j++)
            if(j<i)
                x.add(IloNumVar(env, 0, 1, mip ? ILOINT : ILOFLOAT));
            else
                x.add(IloNumVar(env, 0, 0, ILOFLOAT)); // fülle oben rechts mit dummies
    model.add(x);

    // Cost Function
    IloExpr expr(env);

    // die folgenden Schleifen adressieren ein unteres linkes
    // Dreieck in einer quadratischen Matrix
    for(int i=0; i<N; i++)
        for (int j=0; j<i; j++)
            expr += c[i*N + j] * x[i*N + j];

    model.add(IloMinimize(env, expr));
    expr.end();

    return x;
}

// This routine creates the master ILP (arc variables x and degree constraints).
//
// Modeling variables:
// forall (i,j) in A:
//    x(i,j) = 1, if arc (i,j) is selected
//           = 0, otherwise
//
// Objective:
// minimize sum((i,j) in A) c(i,j) * x(i,j)
//
// Degree constraints:
// forall i in V: sum((i,j) in delta+(i)) x(i,j) = 1
// forall i in V: sum((j,i) in delta-(i)) x(j,i) = 1
//
// Binary constraints on arc variables:
// forall (i,j) in A: x(i,j) in {0, 1}
//
void
createMasterILP(IloModel mod, Arcs x, IloNumArray2 arcCost)
{
   IloInt i, j;
   IloEnv env = mod.getEnv();
   IloInt numNodes = x.getSize();

   // Create variables x(i,j) for (i,j) in A 
   // For simplicity, also dummy variables x(i,i) are created.
   // Those variables are fixed to 0 and do not partecipate to 
   // the constraints.

   char varName[100];
   for (i = 0; i < numNodes; ++i) {
      x[i] = IloIntVarArray(env, numNodes, 0, 1);
      x[i][i].setBounds(0, 0); 
      for (j = 0; j < numNodes; ++j) {
         sprintf(varName, "x.%d.%d", (int) i, (int) j); 
         x[i][j].setName(varName);
      }
      mod.add(x[i]);
   }
  
   // Create objective function: minimize sum((i,j) in A ) c(i,j) * x(i,j)

   IloExpr obj(env);
   for (i = 0; i < numNodes; ++i) {
      arcCost[i][i] = 0;
      obj += IloScalProd(x[i], arcCost[i]);
   }
   mod.add(IloMinimize(env, obj));
   obj.end();

   // Add the out degree constraints.
   // forall i in V: sum((i,j) in delta+(i)) x(i,j) = 1

   for (i = 0; i < numNodes; ++i) {
      IloExpr expr(env);
      for (j = 0;   j < i; ++j)  expr += x[i][j];
      for (j = i+1; j < numNodes; ++j)  expr += x[i][j];
      mod.add(expr == 1);
      expr.end();
   }

   // Add the in degree constraints.
   // forall i in V: sum((j,i) in delta-(i)) x(j,i) = 1

   for (i = 0; i < numNodes; i++) {
      IloExpr expr(env);
      for (j = 0;   j < i; j++)  expr += x[j][i];
      for (j = i+1; j < numNodes; j++)  expr += x[j][i];
      mod.add(expr == 1);
      expr.end();
   }

}// END createMasterILP

static void
populatebynonzero (IloModel model, IloIntVarArray x, IloRangeArray c)
{
   IloEnv env = model.getEnv();

   IloObjective obj = IloMaximize(env);
	int n, a;
	scanf("%d", &n);

		
	scanf("%d", &a);
	//restrição
   c.add(IloRange(env, -IloInfinity, a));

	//variaveis
	for(int i=0 ; i<n; i++){
		x.add(IloIntVar(env, 0, 1));
	}

   /*x.add(IloIntVar(env, 0.0, 40.0));
   x.add(IloIntVar(env));
   x.add(IloIntVar(env));*/

	
   /*obj.setLinearCoef(x[0], 1.0);
   obj.setLinearCoef(x[1], 2.0);
   obj.setLinearCoef(x[2], 3.0);*/

	/*restricoes*/
	for(int i=0 ; i<n; i++){
		scanf("%d", &a);
		c[0].setLinearCoef(x[i], a);
	}

	//objetivo	
	for(int i=0 ; i<n; i++){
		scanf("%d", &a);
		obj.setLinearCoef(x[i], a);
	}
   
   /*c[0].setLinearCoef(x[1],  1.0);
   c[0].setLinearCoef(x[2],  1.0);
   c[1].setLinearCoef(x[0],  1.0);
   c[1].setLinearCoef(x[1], -3.0);
   c[1].setLinearCoef(x[2],  1.0);*/

   c[0].setName("c1");
	for(int i=0; i<n; i++){
		char tmp[10];
		printf("x%d", i+1);
		x[i].setName(tmp);	
	}

   model.add(obj);
   model.add(c);
}  // END populatebynonzero

int createConstraintEnergy(const Problem<double>& P, IloEnv& env, IloModel& model, IloNumVarArray& t, IloNumVarMatrix& b, IloNumVarMatrix& w){
  for (int i=0;i<P.nbTask;++i){
    IloExpr expr(env);
    for (int e=0;e<2*P.nbTask-1;++e){
      for (int q=0;q<P.A[i].Fi.nbPiece;++q)
	model.add(P.A[i].Fi.F[q].f.a*b[i][e]+
		  P.A[i].Fi.F[q].f.c*(t[e+1]-t[e])>=w[i][e]);
      expr+=w[i][e];
    }
    model.add(expr == P.W(i));
    expr.end();
  }
  return 0;
}

static void
populatebyrow (IloModel model, IloNumVarArray x, IloRangeArray c)
{
   IloEnv env = model.getEnv();

   x.add(IloNumVar(env, -1.0, 1.0));
   x.add(IloNumVar(env,  0.0, 1.0));
   model.add(IloMinimize(env, 0.5 * (-3*x[0]*x[0] - 3*x[1]*x[1] +
                                       - 1*x[0]*x[1]               ) ));

   c.add( - x[0] + x[1] >= 0);
   c.add(   x[0] + x[1] >= 0);
   model.add(c);
}  // END populatebyrow

int createConstraintTimeW(const Problem<double>& P, IloModel& model,
			  IloNumVarArray& t, IloNumVarMatrix& x,  IloNumVarMatrix& y, const std::vector<double>& M_te){
  const int E=2*P.nbTask;  
  for (int i=0;i<P.nbTask;i++) {
    for (int e=0;e<E;++e){
      model.add(x[i][e]*P.r(i) <= t[e]);
      model.add(t[e] <= x[i][e]*P.smax(i) + 
		(1-x[i][e])*M_te[e]);
      model.add(P.d(i)*y[i][e] + (1-y[i][e])*M_te[e]>= t[e]);
      model.add(t[e] >= y[i][e]*P.emin(i));
    }
  }
  return 0;
}

void profit_create_objective(graph g, IloModel model, IloNumVarArray x, IloNumVarArray z, int **columns) {
  IloNumExpr e(model.getEnv());
  for(int i = 0; i < g->bidders; i++) {
    e += z[i];
  }
  model.add(IloMaximize(model.getEnv(), e));
}

static void
populatebycolumn (IloModel model, IloNumVarArray x, IloRangeArray c)
{
   IloEnv env = model.getEnv();

   IloObjective obj = IloMaximize(env);
   c.add(IloRange(env, -IloInfinity, 20.0));
   c.add(IloRange(env, -IloInfinity, 30.0));

   x.add(IloNumVar(obj(1.0) + c[0](-1.0) + c[1]( 1.0), 0.0, 40.0));
   x.add(obj(2.0) + c[0]( 1.0) + c[1](-3.0));
   x.add(obj(3.0) + c[0]( 1.0) + c[1]( 1.0));

   model.add(obj);
   model.add(c);
}  // END populatebycolumn

/* füge die symmetrsichen Constraints hinzu (sum_{j<i} x_{ij} = 2)
 * Diese sind zwar auch Randfälle der SubtourEliminationConstraints, aber wenn man
 * sie per Hand hinzufügt, beschleunigt das die Berechnungen extrem
 * */
void CplexTSPSolver::add_symmetric_inout_constraints(IloModel model, IloNumVarArray x)
{

    // wegen der Symmetrie ist nur das untere linke Dreieck mit
    // variablen gefüllt. die inneren Schleifen gehen zeilen entlang
    // und "reflektieren" an der Diagonalen.
    // Die Kommentare "eingehend" und "ausgehend" stimmen so nicht,
    // da tatsächlich symmetrie vorliegt, machen aber dennoch den Zweck
    // deutlich

    IloEnv env = model.getEnv();
    for(int i=0; i<N; i++)
    {
        IloExpr expr(env);
        // eingehende
        for(int j=0; j<i; j++)
            expr += x[i*N+j];
        // ausgehende
        for(int j=i+1; j<N; j++)
            expr += x[j*N+i];

        model.add(expr == 2);
        expr.end();
    }
}

int createConstraintSepEvt(const Problem<double>& P,IloModel& model, 
			   IloNumVarArray& t,const std::vector<std::vector<double>>& bound){
  for (int e=0;e<2*P.nbTask-1;++e)
    for (uint f=1;f<bound[e].size();++f)
      model.add(t[e+f]-t[e]<=bound[e][f-1]);
  return 0;
}

static void
populatebyrow (IloModel model, IloNumVarArray x, IloRangeArray c)
{
   IloEnv env = model.getEnv();

   x.add(IloNumVar(env, 0.0, 40.0));
   x.add(IloNumVar(env));
   x.add(IloNumVar(env));
   x.add(IloNumVar(env, 2.0, 3.0, ILOINT));
   model.add(IloMaximize(env, x[0] + 2 * x[1] + 3 * x[2] + x[3]));

   c.add( - x[0] +     x[1] + x[2] + 10 * x[3] <= 20);
   c.add(   x[0] - 3 * x[1] + x[2]             <= 30);
   c.add(              x[1]        - 3.5* x[3] == 0);
   model.add(c);

}  // END populatebyrow

int createConstraintBmax(const Problem<double>&P, IloModel& model, 
			 IloNumVarArray& t, IloNumVarMatrix& b){
  for (int i=0;i<P.nbTask;i++){
    for (int e=0;e<2*P.nbTask-1;e++)
      model.add((t[e+1]-t[e])*P.bmax(i) >= b[i][e]);
  }
  return 0;
}

static void
populatebyrow (IloModel model, IloNumVarArray x, IloRangeArray c)
{
   IloEnv env = model.getEnv();

   x.add(IloNumVar(env, 0.0, 40.0));
   x.add(IloNumVar(env));
   x.add(IloNumVar(env));
   model.add(IloMaximize(env, x[0] + 2 * x[1] + 3 * x[2]
                            - 0.5 * (33*x[0]*x[0] + 22*x[1]*x[1] +
                                     11*x[2]*x[2] - 12*x[0]*x[1] -
                                     23*x[1]*x[2]                 ) ));

   c.add( - x[0] +     x[1] + x[2] <= 20);
   c.add(   x[0] - 3 * x[1] + x[2] <= 30);
   model.add(c);
}  // END populatebyrow

/* 
 * $\sum_{i > 0} v_i = k$. Ensure that exactly k nodes are active. 
 */
static void addConstraint_k_nodes_active(IloEnv env, IloModel model, IloBoolVarArray vs, Instance& instance, u_int k)
{
	IloExpr e_num_nodes(env);
	for (u_int i = 1; i < instance.n_nodes; i++) {
		e_num_nodes += vs[i];
	}
	model.add(k == e_num_nodes);
	e_num_nodes.end();
}

/**
 * Objective function:
 * $\sum_{i, j} c_{ij} x_{ij}$ 
 */ 
static void addObjectiveFunction(IloEnv env, IloModel model, IloBoolVarArray xs, vector<Instance::Edge> edges, u_int n_edges)
{
	IloExpr e_objective(env);
	for (u_int m = 0; m < n_edges; m++) {
		e_objective += xs[m] * edges[m].weight;
	}
	model.add(IloMinimize(env, e_objective));
	e_objective.end();
}