nlopt_opt make_opt(const mxArray *opts, unsigned n)
{
nlopt_opt opt = NULL, local_opt = NULL;
nlopt_algorithm algorithm;
double *tmp = NULL;
unsigned i;
algorithm = (nlopt_algorithm)
struct_val_default(opts, "algorithm", NLOPT_NUM_ALGORITHMS);
CHECK1(((int)algorithm) >= 0 && algorithm < NLOPT_NUM_ALGORITHMS,
"invalid opt.algorithm");
tmp = (double *) mxCalloc(n, sizeof(double));
opt = nlopt_create(algorithm, n);
CHECK1(opt, "nlopt: out of memory");
nlopt_set_lower_bounds(opt, struct_arrval(opts, "lower_bounds", n,
fill(tmp, n, -HUGE_VAL)));
nlopt_set_upper_bounds(opt, struct_arrval(opts, "upper_bounds", n,
fill(tmp, n, +HUGE_VAL)));
nlopt_set_stopval(opt, struct_val_default(opts, "stopval", -HUGE_VAL));
nlopt_set_ftol_rel(opt, struct_val_default(opts, "ftol_rel", 0.0));
nlopt_set_ftol_abs(opt, struct_val_default(opts, "ftol_abs", 0.0));
nlopt_set_xtol_rel(opt, struct_val_default(opts, "xtol_rel", 0.0));
nlopt_set_xtol_abs(opt, struct_arrval(opts, "xtol_abs", n,
fill(tmp, n, 0.0)));
nlopt_set_maxeval(opt, struct_val_default(opts, "maxeval", 0.0) < 0 ?
0 : struct_val_default(opts, "maxeval", 0.0));
nlopt_set_maxtime(opt, struct_val_default(opts, "maxtime", 0.0));
nlopt_set_population(opt, struct_val_default(opts, "population", 0));
nlopt_set_vector_storage(opt, struct_val_default(opts, "vector_storage", 0));
if (struct_arrval(opts, "initial_step", n, NULL))
nlopt_set_initial_step(opt,
struct_arrval(opts, "initial_step", n, NULL));
if (mxGetField(opts, 0, "local_optimizer")) {
const mxArray *local_opts = mxGetField(opts, 0, "local_optimizer");
CHECK1(mxIsStruct(local_opts),
"opt.local_optimizer must be a structure");
CHECK1(local_opt = make_opt(local_opts, n),
"error initializing local optimizer");
nlopt_set_local_optimizer(opt, local_opt);
nlopt_destroy(local_opt); local_opt = NULL;
}
mxFree(tmp);
return opt;
}
Eigen::VectorXd TargetTrackingController::getControl(const EKF *ekf, const MultiAgentMotionModel *motionModel, const std::vector<const SensorModel*> &sensorModel, double *f) const {
Evaluator evaluator(ekf, motionModel, sensorModel, params);
Eigen::VectorXd p = Eigen::VectorXd::Zero(motionModel->getControlDim());
Eigen::VectorXd lowerBound = Eigen::VectorXd::Constant(motionModel->getControlDim(), params("multi_rotor_control/controlMin").toDouble());
Eigen::VectorXd upperBound = Eigen::VectorXd::Constant(motionModel->getControlDim(), params("multi_rotor_control/controlMax").toDouble());
nlopt_opt opt = nlopt_create(NLOPT_LN_COBYLA, p.size());
// nlopt_opt opt = nlopt_create(NLOPT_LN_BOBYQA, p.size());
// nlopt_opt opt = nlopt_create(NLOPT_LN_NEWUOA_BOUND, p.size());
// nlopt_opt opt = nlopt_create(NLOPT_LN_PRAXIS, p.size());
// nlopt_opt opt = nlopt_create(NLOPT_LN_NELDERMEAD, p.size());
// nlopt_opt opt = nlopt_create(NLOPT_LN_SBPLX, p.size());
// nlopt_opt opt = nlopt_create(NLOPT_GN_ORIG_DIRECT, p.size()); // failed
// nlopt_opt opt = nlopt_create(NLOPT_GN_ORIG_DIRECT_L, p.size()); // very good: p 0.0118546 -6.27225e-05 6.27225e-05 -2.09075e-05 2.09075e-05 -8.51788e-06 -2.09075e-05 10
// nlopt_opt opt = nlopt_create(NLOPT_GN_ISRES, p.size()); // rather bad
// nlopt_opt opt = nlopt_create(NLOPT_GN_CRS2_LM, p.size());
// nlopt_opt opt = nlopt_create(NLOPT_LD_MMA, p.size());
// nlopt_opt opt = nlopt_create(NLOPT_LD_CCSAQ, p.size());
// nlopt_opt opt = nlopt_create(NLOPT_LD_SLSQP, p.size());
// nlopt_opt opt = nlopt_create(NLOPT_LD_LBFGS, p.size());
// nlopt_opt opt = nlopt_create(NLOPT_LD_TNEWTON_PRECOND, p.size()); // bad
// nlopt_opt opt = nlopt_create(NLOPT_LD_TNEWTON_PRECOND_RESTART, p.size()); // bad
// nlopt_opt opt = nlopt_create(NLOPT_LD_VAR2, p.size());
nlopt_set_min_objective(opt, f_evaluate, &evaluator);
nlopt_set_lower_bounds(opt, lowerBound.data());
nlopt_set_upper_bounds(opt, upperBound.data());
nlopt_set_ftol_abs(opt, 1E-6);
nlopt_set_xtol_rel(opt, 1E-3);
nlopt_set_maxeval(opt, 1E8);
nlopt_set_maxtime(opt, 7200);
double pa[p.size()];
memcpy(pa, p.data(), p.size()*sizeof(double));
double cost = 0;
// std::string tmp; std::cerr << "Press enter to start optimization\n"; std::getline(std::cin, tmp);
nlopt_result ret = nlopt_optimize(opt, pa, &cost);
Eigen::VectorXd p_res = Eigen::Map<Eigen::VectorXd>(pa, p.size());
if (f)
*f = cost;
std::cerr << "\nInitial guess:\n";
std::cerr << " p " << p.transpose() << "\n";
std::cerr << " value " << evaluator.evaluate(p) << "\n";
std::cerr << "Optimization result (return code " << ret << "):\n";
std::cerr << " p " << p_res.transpose() << "\n";
std::cerr << " value " << evaluator.evaluate(p_res) << "\n";
nlopt_destroy(opt);
return p_res;
}
int call_nlopt(nlopt_algorithm alg, double ftol, double *step_size, double *params, unsigned np, double (*func) (unsigned,const double*,double*,void*),double *lower, double *upper) {
nlopt_opt opt;
double minf; /* the minimum objective value, upon return */
int retval;
int i;
opt = nlopt_create(alg,np);
nlopt_set_lower_bounds(opt,lower);
nlopt_set_upper_bounds(opt,upper);
nlopt_set_min_objective(opt,func,NULL);
nlopt_set_ftol_abs(opt,ftol);
nlopt_set_initial_step(opt,step_size);
retval = nlopt_optimize(opt, params, &minf) ;
nlopt_destroy(opt);
return retval;
}
Shredder::Shredder(double lower_limit, double upper_limit, double tolerance)
: lower_limit(lower_limit)
, upper_limit(upper_limit)
, tolerance(tolerance)
, opt(NULL)
{
opt = nlopt_create(NLOPT_LD_SLSQP, 1);
nlopt_set_lower_bounds(opt, &lower_limit);
nlopt_set_upper_bounds(opt, &upper_limit);
nlopt_set_max_objective(opt, shredder_opt_objective,
reinterpret_cast<void*>(this));
nlopt_set_maxeval(opt, 20);
nlopt_set_ftol_abs(opt, 1e-7);
nlopt_set_xtol_abs(opt, &tolerance);
}
nlopt_result
NLOPT_STDCALL nlopt_minimize_econstrained(
nlopt_algorithm algorithm,
int n, nlopt_func_old f, void *f_data,
int m, nlopt_func_old fc, void *fc_data_, ptrdiff_t fc_datum_size,
int p, nlopt_func_old h, void *h_data_, ptrdiff_t h_datum_size,
const double *lb, const double *ub, /* bounds */
double *x, /* in: initial guess, out: minimizer */
double *minf, /* out: minimum */
double minf_max, double ftol_rel, double ftol_abs,
double xtol_rel, const double *xtol_abs,
double htol_rel, double htol_abs,
int maxeval, double maxtime)
{
char *fc_data = (char *) fc_data_;
char *h_data = (char *) h_data_;
nlopt_opt opt;
nlopt_result ret;
int i;
if (n < 0 || m < 0 || p < 0) return NLOPT_INVALID_ARGS;
opt = nlopt_create(algorithm, (unsigned) n);
if (!opt) return NLOPT_INVALID_ARGS;
ret = nlopt_set_min_objective(opt, (nlopt_func) f, f_data);
if (ret != NLOPT_SUCCESS) { nlopt_destroy(opt); return ret; }
for (i = 0; i < m; ++i) {
ret = nlopt_add_inequality_constraint(opt, (nlopt_func) fc,
fc_data + i*fc_datum_size,
0.0);
if (ret != NLOPT_SUCCESS) { nlopt_destroy(opt); return ret; }
}
(void) htol_rel; /* unused */
for (i = 0; i < p; ++i) {
ret = nlopt_add_equality_constraint(opt, (nlopt_func) h,
h_data + i*h_datum_size,
htol_abs);
if (ret != NLOPT_SUCCESS) { nlopt_destroy(opt); return ret; }
}
ret = nlopt_set_lower_bounds(opt, lb);
if (ret != NLOPT_SUCCESS) { nlopt_destroy(opt); return ret; }
ret = nlopt_set_upper_bounds(opt, ub);
if (ret != NLOPT_SUCCESS) { nlopt_destroy(opt); return ret; }
ret = nlopt_set_stopval(opt, minf_max);
if (ret != NLOPT_SUCCESS) { nlopt_destroy(opt); return ret; }
ret = nlopt_set_ftol_rel(opt, ftol_rel);
if (ret != NLOPT_SUCCESS) { nlopt_destroy(opt); return ret; }
ret = nlopt_set_ftol_abs(opt, ftol_abs);
if (ret != NLOPT_SUCCESS) { nlopt_destroy(opt); return ret; }
ret = nlopt_set_xtol_rel(opt, xtol_rel);
if (ret != NLOPT_SUCCESS) { nlopt_destroy(opt); return ret; }
ret = nlopt_set_xtol_abs(opt, xtol_abs);
if (ret != NLOPT_SUCCESS) { nlopt_destroy(opt); return ret; }
ret = nlopt_set_maxeval(opt, maxeval);
if (ret != NLOPT_SUCCESS) { nlopt_destroy(opt); return ret; }
ret = nlopt_set_maxtime(opt, maxtime);
if (ret != NLOPT_SUCCESS) { nlopt_destroy(opt); return ret; }
ret = nlopt_optimize(opt, x, minf);
nlopt_destroy(opt);
return ret;
}
nlopt_result ccsa_quadratic_minimize(
unsigned n, nlopt_func f, void *f_data,
unsigned m, nlopt_constraint *fc,
nlopt_precond pre,
const double *lb, const double *ub, /* bounds */
double *x, /* in: initial guess, out: minimizer */
double *minf,
nlopt_stopping *stop,
nlopt_opt dual_opt)
{
nlopt_result ret = NLOPT_SUCCESS;
double *xcur, rho, *sigma, *dfdx, *dfdx_cur, *xprev, *xprevprev, fcur;
double *dfcdx, *dfcdx_cur;
double *fcval, *fcval_cur, *rhoc, *gcval, *y, *dual_lb, *dual_ub;
double *pre_lb, *pre_ub;
unsigned i, ifc, j, k = 0;
dual_data dd;
int feasible;
double infeasibility;
unsigned mfc;
unsigned no_precond;
nlopt_opt pre_opt = NULL;
m = nlopt_count_constraints(mfc = m, fc);
if (nlopt_get_dimension(dual_opt) != m) return NLOPT_INVALID_ARGS;
sigma = (double *) malloc(sizeof(double) * (6*n + 2*m*n + m*7));
if (!sigma) return NLOPT_OUT_OF_MEMORY;
dfdx = sigma + n;
dfdx_cur = dfdx + n;
xcur = dfdx_cur + n;
xprev = xcur + n;
xprevprev = xprev + n;
fcval = xprevprev + n;
fcval_cur = fcval + m;
rhoc = fcval_cur + m;
gcval = rhoc + m;
dual_lb = gcval + m;
dual_ub = dual_lb + m;
y = dual_ub + m;
dfcdx = y + m;
dfcdx_cur = dfcdx + m*n;
dd.n = n;
dd.x = x;
dd.lb = lb;
dd.ub = ub;
dd.sigma = sigma;
dd.dfdx = dfdx;
dd.dfcdx = dfcdx;
dd.fcval = fcval;
dd.rhoc = rhoc;
dd.xcur = xcur;
dd.gcval = gcval;
dd.pre = pre; dd.pre_data = f_data;
dd.prec = NULL; dd.prec_data = NULL;
dd.scratch = NULL;
if (m) {
dd.prec = (nlopt_precond *) malloc(sizeof(nlopt_precond) * m);
dd.prec_data = (void **) malloc(sizeof(void *) * m);
if (!dd.prec || !dd.prec_data) {
ret = NLOPT_OUT_OF_MEMORY;
goto done;
}
for (i = ifc = 0; ifc < mfc; ++ifc) {
unsigned inext = i + fc[ifc].m;
for (; i < inext; ++i) {
dd.prec[i] = fc[ifc].pre;
dd.prec_data[i] = fc[ifc].f_data;
}
}
}
no_precond = pre == NULL;
if (dd.prec)
for (i = 0; i < m; ++i)
no_precond = no_precond && dd.prec[i] == NULL;
if (!no_precond) {
dd.scratch = (double*) malloc(sizeof(double) * (4*n));
if (!dd.scratch) {
free(sigma);
return NLOPT_OUT_OF_MEMORY;
}
pre_lb = dd.scratch + 2*n;
pre_ub = pre_lb + n;
pre_opt = nlopt_create(nlopt_get_algorithm(dual_opt), n);
if (!pre_opt) { ret = NLOPT_FAILURE; goto done; }
ret = nlopt_set_min_objective(pre_opt, g0, &dd);
if (ret < 0) goto done;
ret = nlopt_add_inequality_mconstraint(pre_opt, m, gi, &dd, NULL);
if (ret < 0) goto done;
ret = nlopt_set_ftol_rel(pre_opt, nlopt_get_ftol_rel(dual_opt));
if (ret < 0) goto done;
ret = nlopt_set_ftol_abs(pre_opt, nlopt_get_ftol_abs(dual_opt));
if (ret < 0) goto done;
ret = nlopt_set_maxeval(pre_opt, nlopt_get_maxeval(dual_opt));
if (ret < 0) goto done;
}
for (j = 0; j < n; ++j) {
if (nlopt_isinf(ub[j]) || nlopt_isinf(lb[j]))
sigma[j] = 1.0; /* arbitrary default */
else
sigma[j] = 0.5 * (ub[j] - lb[j]);
}
rho = 1.0;
for (i = 0; i < m; ++i) {
rhoc[i] = 1.0;
dual_lb[i] = y[i] = 0.0;
dual_ub[i] = HUGE_VAL;
}
dd.fval = fcur = *minf = f(n, x, dfdx, f_data);
stop->nevals++;
memcpy(xcur, x, sizeof(double) * n);
if (nlopt_stop_forced(stop)) { ret = NLOPT_FORCED_STOP; goto done; }
feasible = 1; infeasibility = 0;
for (i = ifc = 0; ifc < mfc; ++ifc) {
nlopt_eval_constraint(fcval + i, dfcdx + i*n,
fc + ifc, n, x);
i += fc[ifc].m;
if (nlopt_stop_forced(stop)) { ret = NLOPT_FORCED_STOP; goto done; }
}
for (i = 0; i < m; ++i) {
feasible = feasible && fcval[i] <= 0;
if (fcval[i] > infeasibility) infeasibility = fcval[i];
}
/* For non-feasible initial points, set a finite (large)
upper-bound on the dual variables. What this means is that,
if no feasible solution is found from the dual problem, it
will minimize the dual objective with the unfeasible
constraint weighted by 1e40 -- basically, minimizing the
unfeasible constraint until it becomes feasible or until we at
least obtain a step towards a feasible point.
Svanberg suggested a different approach in his 1987 paper, basically
introducing additional penalty variables for unfeasible constraints,
but this is easier to implement and at least as efficient. */
if (!feasible)
for (i = 0; i < m; ++i) dual_ub[i] = 1e40;
nlopt_set_min_objective(dual_opt, dual_func, &dd);
nlopt_set_lower_bounds(dual_opt, dual_lb);
nlopt_set_upper_bounds(dual_opt, dual_ub);
nlopt_set_stopval(dual_opt, -HUGE_VAL);
nlopt_remove_inequality_constraints(dual_opt);
nlopt_remove_equality_constraints(dual_opt);
while (1) { /* outer iterations */
double fprev = fcur;
if (nlopt_stop_forced(stop)) ret = NLOPT_FORCED_STOP;
else if (nlopt_stop_evals(stop)) ret = NLOPT_MAXEVAL_REACHED;
else if (nlopt_stop_time(stop)) ret = NLOPT_MAXTIME_REACHED;
else if (feasible && *minf < stop->minf_max)
ret = NLOPT_MINF_MAX_REACHED;
if (ret != NLOPT_SUCCESS) goto done;
if (++k > 1) memcpy(xprevprev, xprev, sizeof(double) * n);
memcpy(xprev, xcur, sizeof(double) * n);
while (1) { /* inner iterations */
double min_dual, infeasibility_cur;
int feasible_cur, inner_done;
unsigned save_verbose;
nlopt_result reti;
if (no_precond) {
/* solve dual problem */
dd.rho = rho; dd.count = 0;
save_verbose = ccsa_verbose;
ccsa_verbose = 0; /* no recursive verbosity */
reti = nlopt_optimize_limited(dual_opt, y, &min_dual,
0,
stop->maxtime
- (nlopt_seconds()
- stop->start));
ccsa_verbose = save_verbose;
if (reti < 0 || reti == NLOPT_MAXTIME_REACHED) {
ret = reti;
goto done;
}
dual_func(m, y, NULL, &dd); /* evaluate final xcur etc. */
}
else {
double pre_min;
for (j = 0; j < n; ++j) {
pre_lb[j] = MAX(lb[j], x[j] - sigma[j]);
pre_ub[j] = MIN(ub[j], x[j] + sigma[j]);
xcur[j] = x[j];
}
nlopt_set_lower_bounds(pre_opt, pre_lb);
nlopt_set_upper_bounds(pre_opt, pre_ub);
dd.rho = rho; dd.count = 0;
save_verbose = ccsa_verbose;
ccsa_verbose = 0; /* no recursive verbosity */
reti = nlopt_optimize_limited(pre_opt, xcur, &pre_min,
0, stop->maxtime
- (nlopt_seconds()
- stop->start));
ccsa_verbose = save_verbose;
if (reti < 0 || reti == NLOPT_MAXTIME_REACHED) {
ret = reti;
goto done;
}
/* evaluate final xcur etc */
dd.gval = g0(n, xcur, NULL, &dd);
gi(m, dd.gcval, n, xcur, NULL, &dd);
}
if (ccsa_verbose) {
printf("CCSA dual converged in %d iters to g=%g:\n",
dd.count, dd.gval);
for (i = 0; i < MIN(ccsa_verbose, m); ++i)
printf(" CCSA y[%d]=%g, gc[%d]=%g\n",
i, y[i], i, dd.gcval[i]);
}
fcur = f(n, xcur, dfdx_cur, f_data);
stop->nevals++;
if (nlopt_stop_forced(stop)) {
ret = NLOPT_FORCED_STOP; goto done; }
feasible_cur = 1; infeasibility_cur = 0;
inner_done = dd.gval >= fcur;
for (i = ifc = 0; ifc < mfc; ++ifc) {
nlopt_eval_constraint(fcval_cur + i, dfcdx_cur + i*n,
fc + ifc, n, xcur);
i += fc[ifc].m;
if (nlopt_stop_forced(stop)) {
ret = NLOPT_FORCED_STOP; goto done; }
}
for (i = ifc = 0; ifc < mfc; ++ifc) {
unsigned i0 = i, inext = i + fc[ifc].m;
for (; i < inext; ++i) {
feasible_cur = feasible_cur
&& fcval_cur[i] <= fc[ifc].tol[i-i0];
inner_done = inner_done &&
(dd.gcval[i] >= fcval_cur[i]);
if (fcval_cur[i] > infeasibility_cur)
infeasibility_cur = fcval_cur[i];
}
}
if ((fcur < *minf && (inner_done || feasible_cur || !feasible))
|| (!feasible && infeasibility_cur < infeasibility)) {
if (ccsa_verbose && !feasible_cur)
printf("CCSA - using infeasible point?\n");
dd.fval = *minf = fcur;
infeasibility = infeasibility_cur;
memcpy(fcval, fcval_cur, sizeof(double)*m);
memcpy(x, xcur, sizeof(double)*n);
memcpy(dfdx, dfdx_cur, sizeof(double)*n);
memcpy(dfcdx, dfcdx_cur, sizeof(double)*n*m);
/* once we have reached a feasible solution, the
algorithm should never make the solution infeasible
again (if inner_done), although the constraints may
be violated slightly by rounding errors etc. so we
must be a little careful about checking feasibility */
if (infeasibility_cur == 0) {
if (!feasible) { /* reset upper bounds to infin. */
for (i = 0; i < m; ++i) dual_ub[i] = HUGE_VAL;
nlopt_set_upper_bounds(dual_opt, dual_ub);
}
feasible = 1;
}
}
if (nlopt_stop_forced(stop)) ret = NLOPT_FORCED_STOP;
else if (nlopt_stop_evals(stop)) ret = NLOPT_MAXEVAL_REACHED;
else if (nlopt_stop_time(stop)) ret = NLOPT_MAXTIME_REACHED;
else if (feasible && *minf < stop->minf_max)
ret = NLOPT_MINF_MAX_REACHED;
if (ret != NLOPT_SUCCESS) goto done;
if (inner_done) break;
if (fcur > dd.gval)
rho = MIN(10*rho, 1.1 * (rho + (fcur-dd.gval) / dd.wval));
for (i = 0; i < m; ++i)
if (fcval_cur[i] > dd.gcval[i])
rhoc[i] =
MIN(10*rhoc[i],
1.1 * (rhoc[i] + (fcval_cur[i]-dd.gcval[i])
/ dd.wval));
if (ccsa_verbose)
printf("CCSA inner iteration: rho -> %g\n", rho);
for (i = 0; i < MIN(ccsa_verbose, m); ++i)
printf(" CCSA rhoc[%d] -> %g\n", i,rhoc[i]);
}
if (nlopt_stop_ftol(stop, fcur, fprev))
ret = NLOPT_FTOL_REACHED;
if (nlopt_stop_x(stop, xcur, xprev))
ret = NLOPT_XTOL_REACHED;
if (ret != NLOPT_SUCCESS) goto done;
/* update rho and sigma for iteration k+1 */
rho = MAX(0.1 * rho, CCSA_RHOMIN);
if (ccsa_verbose)
printf("CCSA outer iteration: rho -> %g\n", rho);
for (i = 0; i < m; ++i)
rhoc[i] = MAX(0.1 * rhoc[i], CCSA_RHOMIN);
for (i = 0; i < MIN(ccsa_verbose, m); ++i)
printf(" CCSA rhoc[%d] -> %g\n", i, rhoc[i]);
if (k > 1) {
for (j = 0; j < n; ++j) {
double dx2 = (xcur[j]-xprev[j]) * (xprev[j]-xprevprev[j]);
double gam = dx2 < 0 ? 0.7 : (dx2 > 0 ? 1.2 : 1);
sigma[j] *= gam;
if (!nlopt_isinf(ub[j]) && !nlopt_isinf(lb[j])) {
sigma[j] = MIN(sigma[j], 10*(ub[j]-lb[j]));
/* use a smaller lower bound than Svanberg's
0.01*(ub-lb), which seems unnecessarily large */
sigma[j] = MAX(sigma[j], 1e-8*(ub[j]-lb[j]));
}
}
for (j = 0; j < MIN(ccsa_verbose, n); ++j)
printf(" CCSA sigma[%d] -> %g\n",
j, sigma[j]);
}
}
done:
nlopt_destroy(pre_opt);
if (dd.scratch) free(dd.scratch);
if (m) {
free(dd.prec_data);
free(dd.prec);
}
free(sigma);
return ret;
}
/* unlike nlopt_optimize() below, only handles minimization case */
static nlopt_result nlopt_optimize_(nlopt_opt opt, double *x, double *minf)
{
const double *lb, *ub;
nlopt_algorithm algorithm;
nlopt_func f; void *f_data;
unsigned n, i;
int ni;
nlopt_stopping stop;
if (!opt || !x || !minf || !opt->f
|| opt->maximize) return NLOPT_INVALID_ARGS;
/* reset stopping flag */
nlopt_set_force_stop(opt, 0);
opt->force_stop_child = NULL;
/* copy a few params to local vars for convenience */
n = opt->n;
ni = (int) n; /* most of the subroutines take "int" arg */
lb = opt->lb; ub = opt->ub;
algorithm = opt->algorithm;
f = opt->f; f_data = opt->f_data;
if (n == 0) { /* trivial case: no degrees of freedom */
*minf = opt->f(n, x, NULL, opt->f_data);
return NLOPT_SUCCESS;
}
*minf = HUGE_VAL;
/* make sure rand generator is inited */
nlopt_srand_time_default(); /* default is non-deterministic */
/* check bound constraints */
for (i = 0; i < n; ++i)
if (lb[i] > ub[i] || x[i] < lb[i] || x[i] > ub[i])
return NLOPT_INVALID_ARGS;
stop.n = n;
stop.minf_max = opt->stopval;
stop.ftol_rel = opt->ftol_rel;
stop.ftol_abs = opt->ftol_abs;
stop.xtol_rel = opt->xtol_rel;
stop.xtol_abs = opt->xtol_abs;
stop.nevals = 0;
stop.maxeval = opt->maxeval;
stop.maxtime = opt->maxtime;
stop.start = nlopt_seconds();
stop.force_stop = &(opt->force_stop);
switch (algorithm) {
case NLOPT_GN_DIRECT:
case NLOPT_GN_DIRECT_L:
case NLOPT_GN_DIRECT_L_RAND:
if (!finite_domain(n, lb, ub)) return NLOPT_INVALID_ARGS;
return cdirect(ni, f, f_data,
lb, ub, x, minf, &stop, 0.0,
(algorithm != NLOPT_GN_DIRECT)
+ 3 * (algorithm == NLOPT_GN_DIRECT_L_RAND
? 2 : (algorithm != NLOPT_GN_DIRECT))
+ 9 * (algorithm == NLOPT_GN_DIRECT_L_RAND
? 1 : (algorithm != NLOPT_GN_DIRECT)));
case NLOPT_GN_DIRECT_NOSCAL:
case NLOPT_GN_DIRECT_L_NOSCAL:
case NLOPT_GN_DIRECT_L_RAND_NOSCAL:
if (!finite_domain(n, lb, ub)) return NLOPT_INVALID_ARGS;
return cdirect_unscaled(ni, f, f_data, lb, ub, x, minf,
&stop, 0.0,
(algorithm != NLOPT_GN_DIRECT)
+ 3 * (algorithm == NLOPT_GN_DIRECT_L_RAND ? 2 : (algorithm != NLOPT_GN_DIRECT))
+ 9 * (algorithm == NLOPT_GN_DIRECT_L_RAND ? 1 : (algorithm != NLOPT_GN_DIRECT)));
case NLOPT_GN_ORIG_DIRECT:
case NLOPT_GN_ORIG_DIRECT_L: {
direct_return_code dret;
if (!finite_domain(n, lb, ub)) return NLOPT_INVALID_ARGS;
opt->work = malloc(sizeof(double) *
nlopt_max_constraint_dim(opt->m,
opt->fc));
if (!opt->work) return NLOPT_OUT_OF_MEMORY;
dret = direct_optimize(f_direct, opt, ni, lb, ub, x, minf,
stop.maxeval, -1,
stop.start, stop.maxtime,
0.0, 0.0,
pow(stop.xtol_rel, (double) n), -1.0,
stop.force_stop,
stop.minf_max, 0.0,
NULL,
algorithm == NLOPT_GN_ORIG_DIRECT
? DIRECT_ORIGINAL
: DIRECT_GABLONSKY);
free(opt->work); opt->work = NULL;
switch (dret) {
case DIRECT_INVALID_BOUNDS:
case DIRECT_MAXFEVAL_TOOBIG:
case DIRECT_INVALID_ARGS:
return NLOPT_INVALID_ARGS;
case DIRECT_INIT_FAILED:
case DIRECT_SAMPLEPOINTS_FAILED:
case DIRECT_SAMPLE_FAILED:
return NLOPT_FAILURE;
case DIRECT_MAXFEVAL_EXCEEDED:
case DIRECT_MAXITER_EXCEEDED:
return NLOPT_MAXEVAL_REACHED;
case DIRECT_MAXTIME_EXCEEDED:
return NLOPT_MAXTIME_REACHED;
case DIRECT_GLOBAL_FOUND:
return NLOPT_MINF_MAX_REACHED;
case DIRECT_VOLTOL:
case DIRECT_SIGMATOL:
return NLOPT_XTOL_REACHED;
case DIRECT_OUT_OF_MEMORY:
return NLOPT_OUT_OF_MEMORY;
case DIRECT_FORCED_STOP:
return NLOPT_FORCED_STOP;
}
break;
}
case NLOPT_GD_STOGO:
case NLOPT_GD_STOGO_RAND:
#ifdef WITH_CXX
if (!finite_domain(n, lb, ub)) return NLOPT_INVALID_ARGS;
if (!stogo_minimize(ni, f, f_data, x, minf, lb, ub, &stop,
algorithm == NLOPT_GD_STOGO
? 0 : (int) POP(2*n)))
return NLOPT_FAILURE;
break;
#else
return NLOPT_INVALID_ARGS;
#endif
#if 0
/* lacking a free/open-source license, we no longer use
Rowan's code, and instead use by "sbplx" re-implementation */
case NLOPT_LN_SUBPLEX: {
int iret, freedx = 0;
if (!opt->dx) {
freedx = 1;
if (nlopt_set_default_initial_step(opt, x) != NLOPT_SUCCESS)
return NLOPT_OUT_OF_MEMORY;
}
iret = nlopt_subplex(f_bound, minf, x, n, opt, &stop, opt->dx);
if (freedx) { free(opt->dx); opt->dx = NULL; }
switch (iret) {
case -2: return NLOPT_INVALID_ARGS;
case -20: return NLOPT_FORCED_STOP;
case -10: return NLOPT_MAXTIME_REACHED;
case -1: return NLOPT_MAXEVAL_REACHED;
case 0: return NLOPT_XTOL_REACHED;
case 1: return NLOPT_SUCCESS;
case 2: return NLOPT_MINF_MAX_REACHED;
case 20: return NLOPT_FTOL_REACHED;
case -200: return NLOPT_OUT_OF_MEMORY;
default: return NLOPT_FAILURE; /* unknown return code */
}
break;
}
#endif
case NLOPT_LN_PRAXIS: {
double step;
if (initial_step(opt, x, &step) != NLOPT_SUCCESS)
return NLOPT_OUT_OF_MEMORY;
return praxis_(0.0, DBL_EPSILON,
step, ni, x, f_bound, opt, &stop, minf);
}
case NLOPT_LD_LBFGS:
return luksan_plis(ni, f, f_data, lb, ub, x, minf,
&stop, opt->vector_storage);
case NLOPT_LD_VAR1:
case NLOPT_LD_VAR2:
return luksan_plip(ni, f, f_data, lb, ub, x, minf,
&stop, opt->vector_storage,
algorithm == NLOPT_LD_VAR1 ? 1 : 2);
case NLOPT_LD_TNEWTON:
case NLOPT_LD_TNEWTON_RESTART:
case NLOPT_LD_TNEWTON_PRECOND:
case NLOPT_LD_TNEWTON_PRECOND_RESTART:
return luksan_pnet(ni, f, f_data, lb, ub, x, minf,
&stop, opt->vector_storage,
1 + (algorithm - NLOPT_LD_TNEWTON) % 2,
1 + (algorithm - NLOPT_LD_TNEWTON) / 2);
case NLOPT_GN_CRS2_LM:
if (!finite_domain(n, lb, ub)) return NLOPT_INVALID_ARGS;
return crs_minimize(ni, f, f_data, lb, ub, x, minf, &stop,
(int) POP(0), 0);
case NLOPT_G_MLSL:
case NLOPT_G_MLSL_LDS:
case NLOPT_GN_MLSL:
case NLOPT_GD_MLSL:
case NLOPT_GN_MLSL_LDS:
case NLOPT_GD_MLSL_LDS: {
nlopt_opt local_opt = opt->local_opt;
nlopt_result ret;
if (!finite_domain(n, lb, ub)) return NLOPT_INVALID_ARGS;
if (!local_opt && (algorithm == NLOPT_G_MLSL
|| algorithm == NLOPT_G_MLSL_LDS))
return NLOPT_INVALID_ARGS;
if (!local_opt) { /* default */
nlopt_algorithm local_alg = (algorithm == NLOPT_GN_MLSL ||
algorithm == NLOPT_GN_MLSL_LDS)
? nlopt_local_search_alg_nonderiv
: nlopt_local_search_alg_deriv;
/* don't call MLSL recursively! */
if (local_alg >= NLOPT_GN_MLSL
&& local_alg <= NLOPT_GD_MLSL_LDS)
local_alg = (algorithm == NLOPT_GN_MLSL ||
algorithm == NLOPT_GN_MLSL_LDS)
? NLOPT_LN_COBYLA : NLOPT_LD_MMA;
local_opt = nlopt_create(local_alg, n);
if (!local_opt) return NLOPT_FAILURE;
nlopt_set_ftol_rel(local_opt, opt->ftol_rel);
nlopt_set_ftol_abs(local_opt, opt->ftol_abs);
nlopt_set_xtol_rel(local_opt, opt->xtol_rel);
nlopt_set_xtol_abs(local_opt, opt->xtol_abs);
nlopt_set_maxeval(local_opt, nlopt_local_search_maxeval);
}
if (opt->dx) nlopt_set_initial_step(local_opt, opt->dx);
for (i = 0; i < n && stop.xtol_abs[i] > 0; ++i) ;
if (local_opt->ftol_rel <= 0 && local_opt->ftol_abs <= 0 &&
local_opt->xtol_rel <= 0 && i < n) {
/* it is not sensible to call MLSL without *some*
nonzero tolerance for the local search */
nlopt_set_ftol_rel(local_opt, 1e-15);
nlopt_set_xtol_rel(local_opt, 1e-7);
}
opt->force_stop_child = local_opt;
ret = mlsl_minimize(ni, f, f_data, lb, ub, x, minf, &stop,
local_opt, (int) POP(0),
algorithm >= NLOPT_GN_MLSL_LDS &&
algorithm != NLOPT_G_MLSL);
opt->force_stop_child = NULL;
if (!opt->local_opt) nlopt_destroy(local_opt);
return ret;
}
case NLOPT_LD_MMA: case NLOPT_LD_CCSAQ: {
nlopt_opt dual_opt;
nlopt_result ret;
#define LO(param, def) (opt->local_opt ? opt->local_opt->param : (def))
dual_opt = nlopt_create(LO(algorithm,
nlopt_local_search_alg_deriv),
nlopt_count_constraints(opt->m,
opt->fc));
if (!dual_opt) return NLOPT_FAILURE;
nlopt_set_ftol_rel(dual_opt, LO(ftol_rel, 1e-14));
nlopt_set_ftol_abs(dual_opt, LO(ftol_abs, 0.0));
nlopt_set_maxeval(dual_opt, LO(maxeval, 100000));
#undef LO
if (algorithm == NLOPT_LD_MMA)
ret = mma_minimize(n, f, f_data, opt->m, opt->fc,
lb, ub, x, minf, &stop, dual_opt);
else
ret = ccsa_quadratic_minimize(
n, f, f_data, opt->m, opt->fc, opt->pre,
lb, ub, x, minf, &stop, dual_opt);
nlopt_destroy(dual_opt);
return ret;
}
case NLOPT_LN_COBYLA: {
nlopt_result ret;
int freedx = 0;
if (!opt->dx) {
freedx = 1;
if (nlopt_set_default_initial_step(opt, x) != NLOPT_SUCCESS)
return NLOPT_OUT_OF_MEMORY;
}
return cobyla_minimize(n, f, f_data,
opt->m, opt->fc,
opt->p, opt->h,
lb, ub, x, minf, &stop,
opt->dx);
if (freedx) { free(opt->dx); opt->dx = NULL; }
return ret;
}
case NLOPT_LN_NEWUOA: {
double step;
if (initial_step(opt, x, &step) != NLOPT_SUCCESS)
return NLOPT_OUT_OF_MEMORY;
return newuoa(ni, 2*n+1, x, 0, 0, step,
&stop, minf, f_noderiv, opt);
}
case NLOPT_LN_NEWUOA_BOUND: {
double step;
if (initial_step(opt, x, &step) != NLOPT_SUCCESS)
return NLOPT_OUT_OF_MEMORY;
return newuoa(ni, 2*n+1, x, lb, ub, step,
&stop, minf, f_noderiv, opt);
}
case NLOPT_LN_BOBYQA: {
nlopt_result ret;
int freedx = 0;
if (!opt->dx) {
freedx = 1;
if (nlopt_set_default_initial_step(opt, x) != NLOPT_SUCCESS)
return NLOPT_OUT_OF_MEMORY;
}
ret = bobyqa(ni, 2*n+1, x, lb, ub, opt->dx,
&stop, minf, opt->f, opt->f_data);
if (freedx) { free(opt->dx); opt->dx = NULL; }
return ret;
}
case NLOPT_LN_NELDERMEAD:
case NLOPT_LN_SBPLX:
{
nlopt_result ret;
int freedx = 0;
if (!opt->dx) {
freedx = 1;
if (nlopt_set_default_initial_step(opt, x) != NLOPT_SUCCESS)
return NLOPT_OUT_OF_MEMORY;
}
if (algorithm == NLOPT_LN_NELDERMEAD)
ret= nldrmd_minimize(ni,f,f_data,lb,ub,x,minf,opt->dx,&stop);
else
ret= sbplx_minimize(ni,f,f_data,lb,ub,x,minf,opt->dx,&stop);
if (freedx) { free(opt->dx); opt->dx = NULL; }
return ret;
}
case NLOPT_AUGLAG:
case NLOPT_AUGLAG_EQ:
case NLOPT_LN_AUGLAG:
case NLOPT_LN_AUGLAG_EQ:
case NLOPT_LD_AUGLAG:
case NLOPT_LD_AUGLAG_EQ: {
nlopt_opt local_opt = opt->local_opt;
nlopt_result ret;
if ((algorithm == NLOPT_AUGLAG || algorithm == NLOPT_AUGLAG_EQ)
&& !local_opt)
return NLOPT_INVALID_ARGS;
if (!local_opt) { /* default */
local_opt = nlopt_create(
algorithm == NLOPT_LN_AUGLAG ||
algorithm == NLOPT_LN_AUGLAG_EQ
? nlopt_local_search_alg_nonderiv
: nlopt_local_search_alg_deriv, n);
if (!local_opt) return NLOPT_FAILURE;
nlopt_set_ftol_rel(local_opt, opt->ftol_rel);
nlopt_set_ftol_abs(local_opt, opt->ftol_abs);
nlopt_set_xtol_rel(local_opt, opt->xtol_rel);
nlopt_set_xtol_abs(local_opt, opt->xtol_abs);
nlopt_set_maxeval(local_opt, nlopt_local_search_maxeval);
}
if (opt->dx) nlopt_set_initial_step(local_opt, opt->dx);
opt->force_stop_child = local_opt;
ret = auglag_minimize(ni, f, f_data,
opt->m, opt->fc,
opt->p, opt->h,
lb, ub, x, minf, &stop,
local_opt,
algorithm == NLOPT_AUGLAG_EQ
|| algorithm == NLOPT_LN_AUGLAG_EQ
|| algorithm == NLOPT_LD_AUGLAG_EQ);
opt->force_stop_child = NULL;
if (!opt->local_opt) nlopt_destroy(local_opt);
return ret;
}
case NLOPT_GN_ISRES:
if (!finite_domain(n, lb, ub)) return NLOPT_INVALID_ARGS;
return isres_minimize(ni, f, f_data,
(int) (opt->m), opt->fc,
(int) (opt->p), opt->h,
lb, ub, x, minf, &stop,
(int) POP(0));
// case NLOPT_GN_ESCH:
// if (!finite_domain(n, lb, ub)) return NLOPT_INVALID_ARGS;
// return chevolutionarystrategy(n, f, f_data,
// lb, ub, x, minf, &stop,
// (unsigned) POP(0),
// (unsigned) (POP(0)*1.5));
case NLOPT_LD_SLSQP:
return nlopt_slsqp(n, f, f_data,
opt->m, opt->fc,
opt->p, opt->h,
lb, ub, x, minf, &stop);
default:
return NLOPT_INVALID_ARGS;
}
return NLOPT_SUCCESS; /* never reached */
}
void omxInvokeNLOPT(double *est, GradientOptimizerContext &goc)
{
goc.optName = "SLSQP";
goc.setupSimpleBounds();
goc.useGradient = true;
FitContext *fc = goc.fc;
int oldWanted = fc->wanted;
fc->wanted = 0;
omxState *globalState = fc->state;
nlopt_opt opt = nlopt_create(NLOPT_LD_SLSQP, fc->numParam);
goc.extraData = opt;
//local_opt = nlopt_create(NLOPT_LD_SLSQP, n); // Subsidiary algorithm
//nlopt_set_local_optimizer(opt, local_opt);
nlopt_set_lower_bounds(opt, goc.solLB.data());
nlopt_set_upper_bounds(opt, goc.solUB.data());
int eq, ieq;
globalState->countNonlinearConstraints(eq, ieq);
if (fc->CI) {
nlopt_set_xtol_rel(opt, 5e-3);
std::vector<double> tol(fc->numParam, std::numeric_limits<double>::epsilon());
nlopt_set_xtol_abs(opt, tol.data());
} else {
// The *2 is there to roughly equate accuracy with NPSOL.
nlopt_set_ftol_rel(opt, goc.ControlTolerance * 2);
nlopt_set_ftol_abs(opt, std::numeric_limits<double>::epsilon());
}
nlopt_set_min_objective(opt, SLSQP::nloptObjectiveFunction, &goc);
double feasibilityTolerance = Global->feasibilityTolerance;
SLSQP::context ctx(goc);
if (eq + ieq) {
ctx.origeq = eq;
if (ieq > 0){
goc.inequality.resize(ieq);
std::vector<double> tol(ieq, feasibilityTolerance);
nlopt_add_inequality_mconstraint(opt, ieq, SLSQP::nloptInequalityFunction, &goc, tol.data());
}
if (eq > 0){
goc.equality.resize(eq);
std::vector<double> tol(eq, feasibilityTolerance);
nlopt_add_equality_mconstraint(opt, eq, SLSQP::nloptEqualityFunction, &ctx, tol.data());
}
}
int priorIterations = fc->iterations;
int code = nlopt_optimize(opt, est, &fc->fit);
if (ctx.eqredundent) {
nlopt_remove_equality_constraints(opt);
eq -= ctx.eqredundent;
std::vector<double> tol(eq, feasibilityTolerance);
nlopt_add_equality_mconstraint(opt, eq, SLSQP::nloptEqualityFunction, &ctx, tol.data());
code = nlopt_optimize(opt, est, &fc->fit);
}
if (goc.verbose >= 2) mxLog("nlopt_optimize returned %d", code);
nlopt_destroy(opt);
fc->wanted = oldWanted;
if (code == NLOPT_INVALID_ARGS) {
Rf_error("NLOPT invoked with invalid arguments");
} else if (code == NLOPT_OUT_OF_MEMORY) {
Rf_error("NLOPT ran out of memory");
} else if (code == NLOPT_FORCED_STOP) {
if (fc->iterations - priorIterations <= 1) {
goc.informOut = INFORM_STARTING_VALUES_INFEASIBLE;
} else {
goc.informOut = INFORM_ITERATION_LIMIT;
}
} else if (code == NLOPT_ROUNDOFF_LIMITED) {
if (fc->iterations - priorIterations <= 2) {
Rf_error("%s: Failed due to singular matrix E or C in LSQ subproblem or "
"rank-deficient equality constraint subproblem or "
"positive directional derivative in line search", goc.optName);
} else {
goc.informOut = INFORM_NOT_AT_OPTIMUM; // is this correct? TODO
}
} else if (code < 0) {
Rf_error("NLOPT fatal error %d", code);
} else if (code == NLOPT_MAXEVAL_REACHED) {
goc.informOut = INFORM_ITERATION_LIMIT;
} else {
goc.informOut = INFORM_CONVERGED_OPTIMUM;
}
}
static int test_function(int ifunc)
{
nlopt_opt opt;
testfunc func;
int i, iter;
double *x, minf, minf_max, f0, *xtabs, *lb, *ub;
nlopt_result ret;
double start = nlopt_seconds();
int total_count = 0, max_count = 0, min_count = 1 << 30;
double total_err = 0, max_err = 0;
bounds_wrap_data bw;
if (ifunc < 0 || ifunc >= NTESTFUNCS) {
fprintf(stderr, "testopt: invalid function %d\n", ifunc);
listfuncs(stderr);
return 0;
}
func = testfuncs[ifunc];
x = (double *) malloc(sizeof(double) * func.n * 5);
if (!x) {
fprintf(stderr, "testopt: Out of memory!\n");
return 0;
}
lb = x + func.n * 3;
ub = lb + func.n;
xtabs = x + func.n * 2;
bw.lb = lb;
bw.ub = ub;
bw.f = func.f;
bw.f_data = func.f_data;
for (i = 0; i < func.n; ++i)
xtabs[i] = xtol_abs;
minf_max = minf_max_delta > (-HUGE_VAL) ? minf_max_delta + func.minf : (-HUGE_VAL);
printf("-----------------------------------------------------------\n");
printf("Optimizing %s (%d dims) using %s algorithm\n", func.name, func.n, nlopt_algorithm_name(algorithm));
printf("lower bounds at lb = [");
for (i = 0; i < func.n; ++i)
printf(" %g", func.lb[i]);
printf("]\n");
printf("upper bounds at ub = [");
for (i = 0; i < func.n; ++i)
printf(" %g", func.ub[i]);
printf("]\n");
memcpy(lb, func.lb, func.n * sizeof(double));
memcpy(ub, func.ub, func.n * sizeof(double));
for (i = 0; i < func.n; ++i)
if (fix_bounds[i]) {
printf("fixing bounds for dim[%d] to xmin[%d]=%g\n", i, i, func.xmin[i]);
lb[i] = ub[i] = func.xmin[i];
}
if (force_constraints) {
for (i = 0; i < func.n; ++i) {
if (nlopt_iurand(2) == 0)
ub[i] = nlopt_urand(lb[i], func.xmin[i]);
else
lb[i] = nlopt_urand(func.xmin[i], ub[i]);
}
printf("adjusted lower bounds at lb = [");
for (i = 0; i < func.n; ++i)
printf(" %g", lb[i]);
printf("]\n");
printf("adjusted upper bounds at ub = [");
for (i = 0; i < func.n; ++i)
printf(" %g", ub[i]);
printf("]\n");
}
if (fabs(func.f(func.n, func.xmin, 0, func.f_data) - func.minf) > 1e-8) {
fprintf(stderr, "BUG: function does not achieve given lower bound!\n");
fprintf(stderr, "f(%g", func.xmin[0]);
for (i = 1; i < func.n; ++i)
fprintf(stderr, ", %g", func.xmin[i]);
fprintf(stderr, ") = %0.16g instead of %0.16g, |diff| = %g\n", func.f(func.n, func.xmin, 0, func.f_data), func.minf, fabs(func.f(func.n, func.xmin, 0, func.f_data) - func.minf));
free(x);
return 0;
}
for (iter = 0; iter < iterations; ++iter) {
double val;
testfuncs_counter = 0;
printf("Starting guess x = [");
for (i = 0; i < func.n; ++i) {
if (center_start)
x[i] = (ub[i] + lb[i]) * 0.5;
else if (xinit_tol < 0) { /* random starting point near center of box */
double dx = (ub[i] - lb[i]) * 0.25;
double xm = 0.5 * (ub[i] + lb[i]);
x[i] = nlopt_urand(xm - dx, xm + dx);
} else {
x[i] = nlopt_urand(-xinit_tol, xinit_tol)
+ (1 + nlopt_urand(-xinit_tol, xinit_tol)) * func.xmin[i];
if (x[i] > ub[i])
x[i] = ub[i];
else if (x[i] < lb[i])
x[i] = lb[i];
}
printf(" %g", x[i]);
}
printf("]\n");
f0 = func.f(func.n, x, x + func.n, func.f_data);
printf("Starting function value = %g\n", f0);
if (iter == 0 && testfuncs_verbose && func.has_gradient) {
printf("checking gradient:\n");
for (i = 0; i < func.n; ++i) {
double f;
x[i] *= 1 + 1e-6;
f = func.f(func.n, x, NULL, func.f_data);
x[i] /= 1 + 1e-6;
printf(" grad[%d] = %g vs. numerical derivative %g\n", i, x[i + func.n], (f - f0) / (x[i] * 1e-6));
}
}
testfuncs_counter = 0;
opt = nlopt_create(algorithm, func.n);
nlopt_set_min_objective(opt, bounds_wrap_func, &bw);
nlopt_set_lower_bounds(opt, lb);
nlopt_set_upper_bounds(opt, ub);
nlopt_set_stopval(opt, minf_max);
nlopt_set_ftol_rel(opt, ftol_rel);
nlopt_set_ftol_abs(opt, ftol_abs);
nlopt_set_xtol_rel(opt, xtol_rel);
nlopt_set_xtol_abs(opt, xtabs);
nlopt_set_maxeval(opt, maxeval);
nlopt_set_maxtime(opt, maxtime);
ret = nlopt_optimize(opt, x, &minf);
printf("finished after %g seconds.\n", nlopt_seconds() - start);
printf("return code %d from nlopt_minimize\n", ret);
if (ret < 0 && ret != NLOPT_ROUNDOFF_LIMITED && ret != NLOPT_FORCED_STOP) {
fprintf(stderr, "testopt: error in nlopt_minimize\n");
free(x);
return 0;
}
printf("Found minimum f = %g after %d evaluations (numevals = %d).\n", minf, testfuncs_counter, nlopt_get_numevals(opt));
nlopt_destroy(opt);
total_count += testfuncs_counter;
if (testfuncs_counter > max_count)
max_count = testfuncs_counter;
if (testfuncs_counter < min_count)
min_count = testfuncs_counter;
printf("Minimum at x = [");
for (i = 0; i < func.n; ++i)
printf(" %g", x[i]);
printf("]\n");
if (func.minf == 0)
printf("|f - minf| = %g\n", fabs(minf - func.minf));
else
printf("|f - minf| = %g, |f - minf| / |minf| = %e\n", fabs(minf - func.minf), fabs(minf - func.minf) / fabs(func.minf));
total_err += fabs(minf - func.minf);
if (fabs(minf - func.minf) > max_err)
max_err = fabs(minf - func.minf);
printf("vs. global minimum f = %g at x = [", func.minf);
for (i = 0; i < func.n; ++i)
printf(" %g", func.xmin[i]);
printf("]\n");
val = func.f(func.n, x, NULL, func.f_data);
if (fabs(val - minf) > 1e-12) {
fprintf(stderr, "Mismatch %g between returned minf=%g and f(x) = %g\n", minf - val, minf, val);
free(x);
return 0;
}
}
if (iterations > 1)
printf("average #evaluations = %g (%d-%d)\naverage |f-minf| = %g, max |f-minf| = %g\n", total_count * 1.0 / iterations, min_count, max_count, total_err / iterations, max_err);
free(x);
return 1;
}
