private void updateLog(
double[] gama,
double[] mi,
double teta,
double lambda,
int k,
double ub,
double lb,
ModelLagrange model) {
String vec_gama = "";
String vec_mi = "";
printer.println("k:" + k);
for (int i = 0; i < data.nLinks; i++) {
vec_gama = vec_gama + " " + String.format("%5.2f", gama[i]);
}
printer.println(String.format("%-15s", "Gama:") + vec_gama);
for (int i = 0; i < data.nLinks; i++) {
vec_mi = vec_mi + " " + String.format("%5.2f", mi[i]);
}
printer.println(String.format("%-15s", "Mi:") + vec_mi);
printer.println(String.format("%-15s", "Teta:") + String.format("%15.2f", teta));
printer.println(String.format("%-15s", "Lambda:") + String.format("%15.2f", lambda));
printer.println(String.format("%-15s", "Lower Bound:") + String.format("%15.2f", lb));
printer.println(String.format("%-15s", "Upper Bound:") + String.format("%15.2f", ub));
printer.println(
String.format("%-15s", "GAP: ")
+ String.format("%15.2f", Math.abs(ub - lb) / ((1E-10) + lb)));
printer.println(
String.format("%-15s", "Time:") + String.format("%15.2f", model.getSolverTime()));
printer.println("");
}
public Solver() {
super();
try {
data = new Data(filename);
printer = new PrintStream("out.txt");
// Dimensionando vetores
mi = new double[data.nLinks];
mi_new = new double[data.nLinks];
mi_diff = new double[data.nLinks];
gama = new double[data.nLinks];
// Resolvo o modelo com todos os Y=1 para iniciar o loop
ModelY1 modely1 = new ModelY1(data);
UB = modely1.getUB();
modely1.finalizeModel();
// Resolvo o modelo linear, ou seja, y do tipo numvar
ModelLinear modellinear = new ModelLinear(data);
linearObjectiveValue = modellinear.getOV();
modellinear.finalizeModel();
printer.println(
String.format("%-15s", "Linear Objective Value:")
+ String.format("%15.2f", linearObjectiveValue));
// Lower Bound comeca igual a -infinito
LB = -Double.MAX_VALUE;
// Crio meu modelo Lagrange
ModelLagrange model = new ModelLagrange(data);
for (int k = 0; k < K; k++) {
model.buildAndSolveObjectiveFunction(mi);
// Atualizo o LB
double lb = model.getLB();
if (lb > LB) {
LB = lb;
// zero contabilizacao de ocorrencia de progresso
beta = 0;
}
// Acho gama
gama = model.getGama();
// atualizacao do UB se o gama for viavel
if (model.isFeasible(gama) && (model.getUB() < UB)) {
UB = model.getUB();
}
// Calculo o Teta
teta = (lambda * (UB - model.getLB())) / Math.pow(Util.calculaNorma(gama), 2);
// Calculo o MI(k+1) e o MI(k+1 - k)
for (int i = 0; i < data.nLinks; i++) {
mi_new[i] = Math.max(0, mi[i] + (teta * gama[i]));
mi_diff[i] = mi_new[i] - mi[i];
}
if (Util.calculaNorma(mi_diff) < EPSLON) {
break;
}
if (beta == BETA) {
lambda = (lambda / 2);
beta = 0;
} else {
beta++;
}
updateLog(gama, mi, teta, lambda, k, UB, LB, model);
for (int i = 0; i < data.nLinks; i++) {
mi[i] = mi_new[i];
}
}
} catch (IOException exc1) {
System.err.println(exc1);
} catch (IloException exc2) {
System.err.println(exc2);
} catch (OutOfMemoryError exc3) {
System.err.println(exc3);
}
}