static void setSolution(Data * d, Work * w, Sol * sol, Info * info) {
idxint l = d->n + d->m + 1;
setx(d, w, sol);
sety(d, w, sol);
sets(d, w, sol);
if (info->statusVal == 0 || info->statusVal == SOLVED) {
pfloat tau = w->u[l - 1];
pfloat kap = ABS(w->v[l - 1]);
if (tau > INDETERMINATE_TOL && tau > kap) {
info->statusVal = solved(d, sol, info, tau);
} else {
if (calcNorm(w->u, l) < INDETERMINATE_TOL * SQRTF((pfloat) l)) {
info->statusVal = indeterminate(d, sol, info);
} else {
pfloat bTy = innerProd(d->b, sol->y, d->m);
pfloat cTx = innerProd(d->c, sol->x, d->n);
if (bTy < cTx) {
info->statusVal = infeasible(d, sol, info);
} else {
info->statusVal = unbounded(d, sol, info);
}
}
}
} else if (info->statusVal == INFEASIBLE) {
info->statusVal = infeasible(d, sol, info);
} else {
info->statusVal = unbounded(d, sol, info);
}
}
ull binary_search(ull hi) {
ull lo = 0, mid;
while (lo < hi) {
mid = lo + (hi-lo)/2;
if (infeasible(mid))
hi = mid;
else
lo = mid+1;
}
if (!infeasible(lo))
return -1;
return lo;
}
inline double changeDownInCost(int sequence) const {
double returnValue = 0.0;
if (CLP_METHOD1) {
int iRange = whichRange_[sequence] + offset_[sequence];
if (iRange != start_[sequence] && !infeasible(iRange - 1))
returnValue = cost_[iRange] - cost_[iRange-1];
else
returnValue = 1.0e100;
}
if (CLP_METHOD2) {
returnValue = infeasibilityWeight_;
}
return returnValue;
}
int main()
{
infeasible(7,13);
return 0;
}
stResult<TCity> * main_genetic(mySlimTree* SlimTree, TCity * queryObject, stDistance range)
{
time_t begin, end;
begin = time(NULL);
srand(time(NULL));
Population * pop = new_population();
Population * aux_pop = new_empty_population();
infeasible(SlimTree, pop);
set_genotype(pop);
evaluate_fitness(pop, SlimTree, queryObject, range);
#if DEBUG
print_population(pop ,"Pop", number_of_gerations);
#endif
while( (number_of_gerations < MAX_NUMBER_OF_GERATIONS) && (delta_fitness > 0.0001) )
{
number_of_gerations++;
#if DEBUG
cout << "Number of Gerations: "<< number_of_gerations << endl;
#endif
selection_by_tournament(pop, aux_pop);
#if DEBUG
cout << "Torneio" << endl;
#endif
crossover_uniform(aux_pop);
#if DEBUG
cout << "Crossover Uniforme" << endl;
#endif
mutation(aux_pop);
#if DEBUG
cout << "Mutação" << endl;
#endif
set_phenotype(aux_pop);
#if DEBUG
cout << "Fenotipo" << endl;
#endif
infeasible(SlimTree, aux_pop);
#if DEBUG
cout << "Infactibilidade" << endl;
#endif
set_genotype(aux_pop);
#if DEBUG
cout << "Fenotipo" << endl;
#endif
copy_population(aux_pop, pop);
#if DEBUG
cout << "Copiar aux pop" << endl;
#endif
evaluate_fitness(pop, SlimTree, queryObject, range);
#if DEBUG
cout << "Fitness" << endl;
#endif
old_media_of_fitness = media_of_fitness;
media_of_fitness = get_media_of_population(pop);
delta_fitness = fabs(media_of_fitness - old_media_of_fitness);
}
end = time(NULL);
#if DEBUG
print_population(pop, "PopulatioN", number_of_gerations);
print_statistic(pop, begin, end);
#endif
}
