//######################################################################
/// this is the Volume Algorithm
int
VOL_problem::solve(VOL_user_hooks& hooks, const bool use_preset_dual)
{
if (initialize(use_preset_dual) < 0) // initialize several parameters
return -1;
double best_ub = parm.ubinit; // upper bound
int retval = 0;
VOL_dvector rc(psize); // reduced costs
VOL_dual dual(dsize); // dual vector
dual.u = dsol;
VOL_primal primal(psize, dsize); // primal vector
retval = hooks.compute_rc(dual.u, rc); // compute reduced costs
if (retval < 0) return -1;
// solve relaxed problem
retval = hooks.solve_subproblem(dual.u, rc, dual.lcost,
primal.x, primal.v, primal.value);
if (retval < 0) return -1;
// set target for the lagrangian value
double target = readjust_target(-COIN_DBL_MAX/2, dual.lcost);
// find primal violation
primal.find_max_viol(dual_lb, dual_ub); // this may be left out for speed
VOL_primal pstar(primal); // set pstar=primal
pstar.find_max_viol(dual_lb, dual_ub); // set violation of pstar
dual.compute_xrc(pstar.x, primal.x, rc); // compute xrc
//
VOL_dual dstar(dual); // dstar is the best dual solution so far
VOL_dual dlast(dual); // set dlast=dual
iter_ = 0;
if (parm.printflag)
print_info(iter_, primal, pstar, dual);
VOL_swing swing;
VOL_alpha_factor alpha_factor;
double * lcost_sequence = new double[parm.ascent_check_invl];
const int ascent_first_check = VolMax(parm.ascent_first_check,
parm.ascent_check_invl);
for (iter_ = 1; iter_ <= parm.maxsgriters; ++iter_) { // main iteration
dlast = dual;
// take a dual step
dual.step(target, lambda_, dual_lb, dual_ub, pstar.v);
// compute reduced costs
retval = hooks.compute_rc(dual.u, rc);
if (retval < 0) break;
// solve relaxed problem
retval = hooks.solve_subproblem(dual.u, rc, dual.lcost,
primal.x, primal.v, primal.value);
if (retval < 0) break;
// set the violation of primal
primal.find_max_viol(dual_lb, dual_ub); // this may be left out for speed
dual.compute_xrc(pstar.x, primal.x, rc); // compute xrc
if (dual.lcost > dstar.lcost) {
dstar = dual; // update dstar
}
// check if target should be updated
target = readjust_target(target, dstar.lcost);
// compute inner product between the new subgradient and the
// last direction. This to decide among green, yellow, red
const double ascent = dual.ascent(primal.v, dlast.u);
// green, yellow, red
swing.cond(dlast, dual.lcost, ascent, iter_);
// change lambda if needed
lambda_ *= swing.lfactor(parm, lambda_, iter_);
if (iter_ % parm.alphaint == 0) { // change alpha if needed
const double fact = alpha_factor.factor(parm, dstar.lcost, alpha_);
if (fact != 1.0 && (parm.printflag & 2)) {
printf(" ------------decreasing alpha to %f\n", alpha_*fact);
}
alpha_ *= fact;
}
// convex combination with new primal vector
pstar.cc(power_heur(primal, pstar, dual), primal);
pstar.find_max_viol(dual_lb, dual_ub); // find maximum violation of pstar
if (swing.rd)
dual = dstar; // if there is no improvement reset dual=dstar
if ((iter_ % parm.printinvl == 0) && parm.printflag) { // printing iteration information
print_info(iter_, primal, pstar, dual);
swing.print();
}
if (iter_ % parm.heurinvl == 0) { // run primal heuristic
double ub = COIN_DBL_MAX;
retval = hooks.heuristics(*this, pstar.x, ub);
if (retval < 0) break;
if (ub < best_ub)
best_ub = ub;
}
// save dual solution every 500 iterations
if (iter_ % 500 == 0 && parm.temp_dualfile != 0) {
FILE* outfile = fopen(parm.temp_dualfile, "w");
const VOL_dvector& u = dstar.u;
const int m = u.size();
for (int i = 0; i < m; ++i) {
fprintf(outfile, "%i %f\n", i+1, u[i]);
}
fclose(outfile);
}
// test terminating criteria
const bool primal_feas =
(pstar.viol < parm.primal_abs_precision);
//const double gap = VolAbs(pstar.value - dstar.lcost);
const double gap = pstar.value - dstar.lcost;
const bool small_gap = VolAbs(dstar.lcost) < 0.0001 ?
(gap < parm.gap_abs_precision) :
( (gap < parm.gap_abs_precision) ||
(gap/VolAbs(dstar.lcost) < parm.gap_rel_precision) );
// test optimality
if (primal_feas && small_gap){
if (parm.printflag) printf(" small lp gap \n");
break;
}
// test proving integer optimality
if (best_ub - dstar.lcost < parm.granularity){
if (parm.printflag) printf(" small ip gap \n");
break;
}
// test for non-improvement
const int k = iter_ % parm.ascent_check_invl;
if (iter_ > ascent_first_check) {
if (dstar.lcost - lcost_sequence[k] <
VolAbs(lcost_sequence[k]) * parm.minimum_rel_ascent){
if (parm.printflag) printf(" small improvement \n");
break;
}
}
lcost_sequence[k] = dstar.lcost;
}
delete[] lcost_sequence;
if (parm.printflag)
print_info(iter_, primal, pstar, dual);
// set solution to return
value = dstar.lcost;
psol = pstar.x;
dsol = dstar.u;
viol = pstar.v;
return retval;
}
//######################################################################
/// this is the Volume Algorithm
int
VOL_problem::solve(VOL_user_hooks& hooks, const bool use_preset_dual)
{
if (initialize(use_preset_dual) < 0) // initialize several parameters
return -1;
// JWB: the use_preset_dual option works with files. In my application,
// I have initial values for the dual in memory and the UFL object has
// a method to transfer these to the dsol object. I've added a
// call to that method below.
double best_ub = parm.ubinit; // upper bound
int retval = 0;
int found_integer_feasible = 0;
VOL_dvector rc(psize); // reduced costs
VOL_dual dual(dsize); // dual vector
// JWB: here's the call to initialize the dual from an array
if (use_preset_dual)
{
hooks.init_u(dual.u);
}
else
{
dual.u = dsol;
}
VOL_primal primal(psize, dsize); // primal vector
retval = hooks.compute_rc(dual.u, rc); // compute reduced costs
if (retval < 0) return -1;
// solve relaxed problem
retval = hooks.solve_subproblem(dual.u, rc, dual.lcost,
primal.x, primal.v, primal.value);
if (retval < 0) return -1;
// set target for the lagrangian value
double target = readjust_target(-DBL_MAX / 2, dual.lcost);
// find primal violation
primal.find_max_viol(dual_lb, dual_ub); // this may be left out for speed
VOL_primal pstar(primal); // set pstar=primal
pstar.find_max_viol(dual_lb, dual_ub); // set violation of pstar
dual.compute_xrc(pstar.x, primal.x, rc); // compute xrc
//
VOL_dual dstar(dual); // dstar is the best dual solution so far
VOL_dual dlast(dual); // set dlast=dual
iter_ = 0;
if (parm.printflag) print_info(iter_, primal, pstar, dual);
VOL_swing swing;
VOL_alpha_factor alpha_factor;
double* lcost_sequence = new double[parm.ascent_check_invl];
const int ascent_first_check = VolMax(parm.ascent_first_check,
parm.ascent_check_invl);
for (iter_ = 1; iter_ <= parm.maxsgriters; ++iter_) // main iteration
{
dlast = dual;
cur_u = &dlast.u;
// take a dual step
dual.step(target, lambda_, dual_lb, dual_ub, pstar.v);
// compute reduced costs
retval = hooks.compute_rc(dual.u, rc);
if (retval < 0)
{
printf("VOL breaking because of compute_rc\n");
break;
}
// solve relaxed problem
retval = hooks.solve_subproblem(dual.u, rc, dual.lcost,
primal.x, primal.v, primal.value);
if (retval < 0)
{
printf("VOL breaking because of solve_subproblem\n");
break;
}
// set the violation of primal
primal.find_max_viol(dual_lb, dual_ub); // this may be left out for speed
dual.compute_xrc(pstar.x, primal.x, rc); // compute xrc
if (dual.lcost > dstar.lcost)
{
dstar = dual; // update dstar
}
// check if target should be updated
target = readjust_target(target, dstar.lcost);
// compute inner product between the new subgradient and the
// last direction. This to decide among green, yellow, red
const double ascent = dual.ascent(primal.v, dlast.u);
// green, yellow, red
swing.cond(dlast, dual.lcost, ascent, iter_);
// change lambda if needed
lambda_ *= swing.lfactor(parm, lambda_, iter_);
if (iter_ % parm.alphaint == 0) // change alpha if needed
{
const double fact = alpha_factor.factor(parm, dstar.lcost, alpha_);
alpha_ *= fact;
}
// convex combination with new primal vector
pstar.cc(power_heur(primal, pstar, dual), primal);
pstar.find_max_viol(dual_lb, dual_ub); // find maximum violation of pstar
if (swing.rd) dual = dstar; // if there is no improvement reset dual=dstar
if ((iter_ % parm.printinvl == 0) && parm.printflag) // printing iteration information
{
print_info(iter_, primal, pstar, dual);
swing.print();
}
if ((iter_ + 1) % parm.heurinvl == 0) // run primal heuristic
{
double ub = DBL_MAX;
printf("Vol: iter: %d, heurinvl: %d\n", iter_, parm.heurinvl);
fflush(stdout);
retval = hooks.heuristics(*this, pstar.x, ub, dual.lcost);
if (retval < 0)
{
found_integer_feasible = true;
break;
}
if (retval > 0) found_integer_feasible = 1;
if (ub < best_ub) best_ub = ub;
}
// save dual solution every 500 iterations
if (iter_ % 500 == 0 && parm.temp_dualfile != 0)
{
FILE* outfile = fopen(parm.temp_dualfile, "w");
const VOL_dvector& u = dstar.u;
const int m = u.size();
for (int i = 0; i < m; ++i)
{
fprintf(outfile, "%i %f\n", i + 1, u[i]);
}
fclose(outfile);
}
// test terminating criteria
const bool primal_feas =
(pstar.viol < parm.primal_abs_precision);
const double gap = VolAbs(pstar.value - dstar.lcost);
printf("Vol: pstar: %f, lbound: %f\n", pstar.value, dstar.lcost);
const bool small_gap = VolAbs(dstar.lcost) < 0.0001 ?
(gap < parm.gap_abs_precision) :
((gap < parm.gap_abs_precision) ||
(gap / VolAbs(dstar.lcost) < parm.gap_rel_precision));
// test optimality
if (primal_feas && small_gap)
{
printf("VOL breaking because of small lp gap:(%lf/%lf)\n",
pstar.value, dstar.lcost);
break;
}
// test proving integer optimality
if (best_ub - dstar.lcost < parm.granularity)
{
printf("VOL breaking because of small integer gap\n");
break;
}
// test for non-improvement
const int k = iter_ % parm.ascent_check_invl;
if (iter_ > ascent_first_check)
{
if (dstar.lcost - lcost_sequence[k] <
VolAbs(lcost_sequence[k]) * parm.minimum_rel_ascent)
{
printf("VOL breaking because non-improvement\n");
break;
}
}
lcost_sequence[k] = dstar.lcost;
}
printf("VOL took %d iterations\n", iter_);
delete[] lcost_sequence;
if (parm.printflag) print_info(iter_, primal, pstar, dual);
// set solution to return
value = dstar.lcost;
psol = pstar.x;
dsol = dstar.u;
viol = pstar.v;
//return retval;
return found_integer_feasible;
}