nlopt_result auglag_minimize(int n, nlopt_func f, void *f_data,
int m, nlopt_constraint *fc,
int p, nlopt_constraint *h,
const double *lb, const double *ub, /* bounds */
double *x, /* in: initial guess, out: minimizer */
double *minf,
nlopt_stopping *stop,
nlopt_opt sub_opt, int sub_has_fc)
{
auglag_data d;
nlopt_result ret = NLOPT_SUCCESS;
double ICM = HUGE_VAL, minf_penalty = HUGE_VAL, penalty;
double *xcur = NULL, fcur;
int i, ii, feasible, minf_feasible = 0;
unsigned int k;
int auglag_iters = 0;
int max_constraint_dim;
/* magic parameters from Birgin & Martinez */
const double tau = 0.5, gam = 10;
const double lam_min = -1e20, lam_max = 1e20, mu_max = 1e20;
d.f = f; d.f_data = f_data;
d.m = m; d.fc = fc;
d.p = p; d.h = h;
d.stop = stop;
/* whether we handle inequality constraints via the augmented
Lagrangian penalty function, or directly in the sub-algorithm */
if (sub_has_fc)
d.m = 0;
else
m = 0;
max_constraint_dim = MAX(nlopt_max_constraint_dim(d.m, fc),
nlopt_max_constraint_dim(d.p, h));
d.mm = nlopt_count_constraints(d.m, fc);
d.pp = nlopt_count_constraints(d.p, h);
ret = nlopt_set_min_objective(sub_opt, auglag, &d); if (ret<0) return ret;
ret = nlopt_set_lower_bounds(sub_opt, lb); if (ret<0) return ret;
ret = nlopt_set_upper_bounds(sub_opt, ub); if (ret<0) return ret;
ret = nlopt_set_stopval(sub_opt,
d.m==0 && d.p==0 ? stop->minf_max : -HUGE_VAL);
if (ret<0) return ret;
ret = nlopt_remove_inequality_constraints(sub_opt); if (ret<0) return ret;
ret = nlopt_remove_equality_constraints(sub_opt); if (ret<0) return ret;
for (i = 0; i < m; ++i) {
if (fc[i].f)
ret = nlopt_add_inequality_constraint(sub_opt,
fc[i].f, fc[i].f_data,
fc[i].tol[0]);
else
ret = nlopt_add_inequality_mconstraint(sub_opt, fc[i].m,
fc[i].mf, fc[i].f_data,
fc[i].tol);
if (ret < 0) return ret;
}
xcur = (double *) malloc(sizeof(double) * (n
+ max_constraint_dim * (1 + n)
+ d.pp + d.mm));
if (!xcur) return NLOPT_OUT_OF_MEMORY;
memcpy(xcur, x, sizeof(double) * n);
d.restmp = xcur + n;
d.gradtmp = d.restmp + max_constraint_dim;
memset(d.gradtmp, 0, sizeof(double) * (n*max_constraint_dim + d.pp+d.mm));
d.lambda = d.gradtmp + n * max_constraint_dim;
d.mu = d.lambda + d.pp;
*minf = HUGE_VAL;
/* starting rho suggested by B & M */
if (d.p > 0 || d.m > 0) {
double con2 = 0;
++ *(d.stop->nevals_p);
fcur = f(n, xcur, NULL, f_data);
if (nlopt_stop_forced(stop)) {
ret = NLOPT_FORCED_STOP; goto done; }
penalty = 0;
feasible = 1;
for (i = 0; i < d.p; ++i) {
nlopt_eval_constraint(d.restmp, NULL, d.h + i, n, xcur);
if (nlopt_stop_forced(stop)) {
ret = NLOPT_FORCED_STOP; goto done; }
for (k = 0; k < d.h[i].m; ++k) {
double hi = d.restmp[k];
penalty += fabs(hi);
feasible = feasible && fabs(hi) <= h[i].tol[k];
con2 += hi * hi;
}
}
for (i = 0; i < d.m; ++i) {
nlopt_eval_constraint(d.restmp, NULL, d.fc + i, n, xcur);
if (nlopt_stop_forced(stop)) {
ret = NLOPT_FORCED_STOP; goto done; }
for (k = 0; k < d.fc[i].m; ++k) {
double fci = d.restmp[k];
penalty += fci > 0 ? fci : 0;
feasible = feasible && fci <= fc[i].tol[k];
if (fci > 0) con2 += fci * fci;
}
}
*minf = fcur;
minf_penalty = penalty;
minf_feasible = feasible;
d.rho = MAX(1e-6, MIN(10, 2 * fabs(*minf) / con2));
}
else
d.rho = 1; /* whatever, doesn't matter */
if (auglag_verbose) {
printf("auglag: initial rho=%g\nauglag initial lambda=", d.rho);
for (i = 0; i < d.pp; ++i) printf(" %g", d.lambda[i]);
printf("\nauglag initial mu = ");
for (i = 0; i < d.mm; ++i) printf(" %g", d.mu[i]);
printf("\n");
}
do {
double prev_ICM = ICM;
ret = nlopt_optimize_limited(sub_opt, xcur, &fcur,
stop->maxeval - *(stop->nevals_p),
stop->maxtime - (nlopt_seconds()
- stop->start));
if (auglag_verbose)
printf("auglag: subopt return code %d\n", ret);
if (ret < 0) break;
++ *(d.stop->nevals_p);
fcur = f(n, xcur, NULL, f_data);
if (nlopt_stop_forced(stop)) {
ret = NLOPT_FORCED_STOP; goto done; }
if (auglag_verbose)
printf("auglag: fcur = %g\n", fcur);
ICM = 0;
penalty = 0;
feasible = 1;
for (i = ii = 0; i < d.p; ++i) {
nlopt_eval_constraint(d.restmp, NULL, d.h + i, n, xcur);
if (nlopt_stop_forced(stop)) {
ret = NLOPT_FORCED_STOP; goto done; }
for (k = 0; k < d.h[i].m; ++k) {
double hi = d.restmp[k];
double newlam = d.lambda[ii] + d.rho * hi;
penalty += fabs(hi);
feasible = feasible && fabs(hi) <= h[i].tol[k];
ICM = MAX(ICM, fabs(hi));
d.lambda[ii++] = MIN(MAX(lam_min, newlam), lam_max);
}
}
for (i = ii = 0; i < d.m; ++i) {
nlopt_eval_constraint(d.restmp, NULL, d.fc + i, n, xcur);
if (nlopt_stop_forced(stop)) {
ret = NLOPT_FORCED_STOP; goto done; }
for (k = 0; k < d.fc[i].m; ++k) {
double fci = d.restmp[k];
double newmu = d.mu[ii] + d.rho * fci;
penalty += fci > 0 ? fci : 0;
feasible = feasible && fci <= fc[i].tol[k];
ICM = MAX(ICM, fabs(MAX(fci, -d.mu[ii] / d.rho)));
d.mu[ii++] = MIN(MAX(0.0, newmu), mu_max);
}
}
if (ICM > tau * prev_ICM) {
d.rho *= gam;
}
auglag_iters++;
if (auglag_verbose) {
printf("auglag %d: ICM=%g (%sfeasible), rho=%g\nauglag lambda=",
auglag_iters, ICM, feasible ? "" : "not ", d.rho);
for (i = 0; i < d.pp; ++i) printf(" %g", d.lambda[i]);
printf("\nauglag %d: mu = ", auglag_iters);
for (i = 0; i < d.mm; ++i) printf(" %g", d.mu[i]);
printf("\n");
}
if ((feasible && (!minf_feasible || penalty < minf_penalty
|| fcur < *minf)) ||
(!minf_feasible && penalty < minf_penalty)) {
ret = NLOPT_SUCCESS;
if (feasible) {
if (fcur < stop->minf_max)
ret = NLOPT_MINF_MAX_REACHED;
else if (nlopt_stop_ftol(stop, fcur, *minf))
ret = NLOPT_FTOL_REACHED;
else if (nlopt_stop_x(stop, xcur, x))
ret = NLOPT_XTOL_REACHED;
}
*minf = fcur;
minf_penalty = penalty;
minf_feasible = feasible;
memcpy(x, xcur, sizeof(double) * n);
if (ret != NLOPT_SUCCESS) break;
}
if (nlopt_stop_forced(stop)) {ret = NLOPT_FORCED_STOP; break;}
if (nlopt_stop_evals(stop)) {ret = NLOPT_MAXEVAL_REACHED; break;}
if (nlopt_stop_time(stop)) {ret = NLOPT_MAXTIME_REACHED; break;}
/* TODO: use some other stopping criterion on ICM? */
/* The paper uses ICM <= epsilon and DFM <= epsilon, where
DFM is a measure of the size of the Lagrangian gradient.
Besides the fact that these kinds of absolute tolerances
(non-scale-invariant) are unsatisfying and it is not
clear how the user should specify it, the ICM <= epsilon
condition seems not too different from requiring feasibility,
especially now that the user can provide constraint-specific
tolerances analogous to epsilon. */
if (ICM == 0) {ret = NLOPT_FTOL_REACHED; break;}
} while (1);
done:
free(xcur);
return ret;
}
nlopt_result isres_minimize(int n, nlopt_func f, void *f_data,
int m, nlopt_constraint *fc, /* fc <= 0 */
int p, nlopt_constraint *h, /* h == 0 */
const double *lb, const double *ub, /* bounds */
double *x, /* in: initial guess, out: minimizer */
double *minf,
nlopt_stopping *stop,
int population) /* pop. size (= 0 for default) */
{
const double ALPHA = 0.2; /* smoothing factor from paper */
const double GAMMA = 0.85; /* step-reduction factor from paper */
const double PHI = 1.0; /* expected rate of convergence, from paper */
const double PF = 0.45; /* fitness probability, from paper */
const double SURVIVOR = 1.0/7.0; /* survivor fraction, from paper */
int survivors;
nlopt_result ret = NLOPT_SUCCESS;
double *sigmas = 0, *xs; /* population-by-n arrays (row-major) */
double *fval; /* population array of function vals */
double *penalty; /* population array of penalty vals */
double *x0;
int *irank = 0;
int k, i, j, c;
int mp = m + p;
double minf_penalty = HUGE_VAL, minf_gpenalty = HUGE_VAL;
double taup, tau;
double *results = 0; /* scratch space for mconstraint results */
unsigned ires;
*minf = HUGE_VAL;
if (!population) population = 20 * (n + 1);
if (population < 1) return NLOPT_INVALID_ARGS;
survivors = ceil(population * SURVIVOR);
taup = PHI / sqrt((double) 2*n);
tau = PHI / sqrt((double) 2*sqrt((double) n));
/* we don't handle unbounded search regions */
for (j = 0; j < n; ++j) if (nlopt_isinf(lb[j]) || nlopt_isinf(ub[j]))
return NLOPT_INVALID_ARGS;
ires = imax2(nlopt_max_constraint_dim(m, fc),
nlopt_max_constraint_dim(p, h));
results = (double *) malloc(ires * sizeof(double));
if (ires > 0 && !results) return NLOPT_OUT_OF_MEMORY;
sigmas = (double*) malloc(sizeof(double) * (population*n*2
+ population
+ population
+ n));
if (!sigmas) { free(results); return NLOPT_OUT_OF_MEMORY; }
xs = sigmas + population*n;
fval = xs + population*n;
penalty = fval + population;
x0 = penalty + population;
irank = (int*) malloc(sizeof(int) * population);
if (!irank) { ret = NLOPT_OUT_OF_MEMORY; goto done; }
for (k = 0; k < population; ++k) {
for (j = 0; j < n; ++j) {
sigmas[k*n+j] = (ub[j] - lb[j]) / sqrt((double) n);
xs[k*n+j] = nlopt_urand(lb[j], ub[j]);
}
}
memcpy(xs, x, sizeof(double) * n); /* use input x for xs_0 */
while (1) { /* each loop body = one generation */
int all_feasible = 1;
/* evaluate f and constraint violations for whole population */
for (k = 0; k < population; ++k) {
int feasible = 1;
double gpenalty;
stop->nevals++;
fval[k] = f(n, xs + k*n, NULL, f_data);
if (nlopt_stop_forced(stop)) {
ret = NLOPT_FORCED_STOP; goto done; }
penalty[k] = 0;
for (c = 0; c < m; ++c) { /* inequality constraints */
nlopt_eval_constraint(results, NULL,
fc + c, n, xs + k*n);
if (nlopt_stop_forced(stop)) {
ret = NLOPT_FORCED_STOP; goto done; }
for (ires = 0; ires < fc[c].m; ++ires) {
double gval = results[ires];
if (gval > fc[c].tol[ires]) feasible = 0;
if (gval < 0) gval = 0;
penalty[k] += gval*gval;
}
}
gpenalty = penalty[k];
for (c = m; c < mp; ++c) { /* equality constraints */
nlopt_eval_constraint(results, NULL,
h + (c-m), n, xs + k*n);
if (nlopt_stop_forced(stop)) {
ret = NLOPT_FORCED_STOP; goto done; }
for (ires = 0; ires < h[c-m].m; ++ires) {
double hval = results[ires];
if (fabs(hval) > h[c-m].tol[ires]) feasible = 0;
penalty[k] += hval*hval;
}
}
if (penalty[k] > 0) all_feasible = 0;
/* convergence criteria (FIXME: improve?) */
/* FIXME: with equality constraints, how do
we decide which solution is the "best" so far?
... need some total order on the solutions? */
if ((penalty[k] <= minf_penalty || feasible)
&& (fval[k] <= *minf || minf_gpenalty > 0)
&& ((feasible ? 0 : penalty[k]) != minf_penalty
|| fval[k] != *minf)) {
if (fval[k] < stop->minf_max && feasible)
ret = NLOPT_MINF_MAX_REACHED;
else if (!nlopt_isinf(*minf)) {
if (nlopt_stop_f(stop, fval[k], *minf)
&& nlopt_stop_f(stop, feasible ? 0 : penalty[k],
minf_penalty))
ret = NLOPT_FTOL_REACHED;
else if (nlopt_stop_x(stop, xs+k*n, x))
ret = NLOPT_XTOL_REACHED;
}
memcpy(x, xs+k*n, sizeof(double)*n);
*minf = fval[k];
minf_penalty = feasible ? 0 : penalty[k];
minf_gpenalty = feasible ? 0 : gpenalty;
if (ret != NLOPT_SUCCESS) goto done;
}
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;
if (ret != NLOPT_SUCCESS) goto done;
}
/* "selection" step: rank the population */
for (k = 0; k < population; ++k) irank[k] = k;
if (all_feasible) /* special case: rank by objective function */
nlopt_qsort_r(irank, population, sizeof(int), fval,key_compare);
else {
/* Runarsson & Yao's stochastic ranking of the population */
for (i = 0; i < population; ++i) {
int swapped = 0;
for (j = 0; j < population-1; ++j) {
double u = nlopt_urand(0,1);
if (u < PF || (penalty[irank[j]] == 0
&& penalty[irank[j+1]] == 0)) {
if (fval[irank[j]] > fval[irank[j+1]]) {
int irankj = irank[j];
irank[j] = irank[j+1];
irank[j+1] = irankj;
swapped = 1;
}
}
else if (penalty[irank[j]] > penalty[irank[j+1]]) {
int irankj = irank[j];
irank[j] = irank[j+1];
irank[j+1] = irankj;
swapped = 1;
}
}
if (!swapped) break;
}
}
/* evolve the population:
differential evolution for the best survivors,
and standard mutation of the best survivors for the rest: */
for (k = survivors; k < population; ++k) { /* standard mutation */
double taup_rand = taup * nlopt_nrand(0,1);
int rk = irank[k], ri;
i = k % survivors;
ri = irank[i];
for (j = 0; j < n; ++j) {
double sigmamax = (ub[j] - lb[j]) / sqrt((double) n);
sigmas[rk*n+j] = sigmas[ri*n+j]
* exp(taup_rand + tau*nlopt_nrand(0,1));
if (sigmas[rk*n+j] > sigmamax)
sigmas[rk*n+j] = sigmamax;
do {
xs[rk*n+j] = xs[ri*n+j]
+ sigmas[rk*n+j] * nlopt_nrand(0,1);
} while (xs[rk*n+j] < lb[j] || xs[rk*n+j] > ub[j]);
sigmas[rk*n+j] = sigmas[ri*n+j] + ALPHA*(sigmas[rk*n+j]
- sigmas[ri*n+j]);
}
}
memcpy(x0, xs, n * sizeof(double));
for (k = 0; k < survivors; ++k) { /* differential variation */
double taup_rand = taup * nlopt_nrand(0,1);
int rk = irank[k];
for (j = 0; j < n; ++j) {
double xi = xs[rk*n+j];
if (k+1 < survivors)
xs[rk*n+j] += GAMMA * (x0[j] - xs[(k+1)*n+j]);
if (k+1 == survivors
|| xs[rk*n+j] < lb[j] || xs[rk*n+j] > ub[j]) {
/* standard mutation for last survivor and
for any survivor components that are now
outside the bounds */
double sigmamax = (ub[j] - lb[j]) / sqrt((double) n);
double sigi = sigmas[rk*n+j];
sigmas[rk*n+j] *= exp(taup_rand
+ tau*nlopt_nrand(0,1));
if (sigmas[rk*n+j] > sigmamax)
sigmas[rk*n+j] = sigmamax;
do {
xs[rk*n+j] = xi
+ sigmas[rk*n+j] * nlopt_nrand(0,1);
} while (xs[rk*n+j] < lb[j] || xs[rk*n+j] > ub[j]);
sigmas[rk*n+j] = sigi
+ ALPHA * (sigmas[rk*n+j] - sigi);
}
}
}
}
done:
if (irank) free(irank);
if (sigmas) free(sigmas);
if (results) free(results);
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 */
}