/* set x to a random trial value, as defined by CRS:
x = 2G - x_n,
where x_0 ... x_n are distinct points in the population
with x_0 the current best point and the other points are random,
and G is the centroid of x_0...x_{n-1} */
static void random_trial(crs_data *d, double *x, rb_node *best)
{
int n = d->n, n1 = n+1, i, k, i0, jn;
double *ps = d->ps, *xi;
/* initialize x to x_0 = best point */
memcpy(x, best->k + 1, sizeof(double) * n);
i0 = (best->k - ps) / n1;
jn = nlopt_iurand(n); /* which of remaining n points is "x_n",
i.e. which to reflect through ...
this is necessary since we generate
the remaining points in order, so
just picking the last point would not
be very random */
/* use "method A" from
Jeffrey Scott Vitter, "An efficient algorithm for
sequential random sampling," ACM Trans. Math. Soft. 13 (1),
58--67 (1987).
to randomly pick n distinct points out of the remaining N-1 (not
including i0!). (The same as "method S" in Knuth vol. 2.)
This method requires O(N) time, which is fine in our case
(there are better methods if n << N). */
{
int Nleft = d->N - 1, nleft = n;
int Nfree = Nleft - nleft;
i = 0; i += i == i0;
while (nleft > 1) {
double q = ((double) Nfree) / Nleft;
double v = nlopt_urand(0., 1.);
while (q > v) {
++i; i += i == i0;
--Nfree; --Nleft;
q = (q * Nfree) / Nleft;
}
xi = ps + n1 * i + 1;
if (jn-- == 0) /* point to reflect through */
for (k = 0; k < n; ++k) x[k] -= xi[k] * (0.5*n);
else /* point to include in centroid */
for (k = 0; k < n; ++k) x[k] += xi[k];
++i; i += i == i0;
--Nleft; --nleft;
}
i += nlopt_iurand(Nleft); i += i == i0;
xi = ps + n1 * i + 1;
if (jn-- == 0) /* point to reflect through */
for (k = 0; k < n; ++k) x[k] -= xi[k] * (0.5*n);
else /* point to include in centroid */
for (k = 0; k < n; ++k) x[k] += xi[k];
}
for (k = 0; k < n; ++k) {
x[k] *= 2.0 / n; /* renormalize */
if (x[k] > d->ub[k]) x[k] = d->ub[k];
else if (x[k] < d->lb[k]) x[k] = d->lb[k];
}
}
nlopt_result chevolutionarystrategy(
unsigned nparameters, /* Number of input parameters */
nlopt_func f, /* Recursive Objective Funtion Call */
void * data_f, /* Data to Objective Function */
const double* lb, /* Lower bound values */
const double* ub, /* Upper bound values */
double* x, /*in: initial guess, out: minimizer */
double* minf,
nlopt_stopping* stop, /* nlopt stop condition */
unsigned np, /* Number of Parents */
unsigned no) { /* Number of Offsprings */
/* variables from nlopt */
nlopt_result ret = NLOPT_SUCCESS;
double vetor[8];
unsigned i, id, item;
int parent1, parent2;
unsigned crosspoint; /* crossover parameteres */
int contmutation, totalmutation; /* mutation parameters */
int idoffmutation, paramoffmutation; /* mutation parameters */
Individual * esparents; /* Parents population */
Individual * esoffsprings; /* Offsprings population */
Individual * estotal;/* copy containing Parents and Offsprings pops */
/* It is interesting to maintain the parents and offsprings
* populations stablished and sorted; when the final iterations
* is achieved, they are ranked and updated. */
/*********************************
* controling the population size
*********************************/
if (!np) np = 40;
if (!no) no = 60;
if ((np < 1)||(no<1)) {
nlopt_stop_msg(stop, "populations %d, %d are too small", np, no);
return NLOPT_INVALID_ARGS;
}
esparents = (Individual*) malloc(sizeof(Individual) * np);
esoffsprings = (Individual*) malloc(sizeof(Individual) * no);
estotal = (Individual*) malloc(sizeof(Individual) * (np+no));
if ((!esparents)||(!esoffsprings)||(!estotal)) {
free(esparents); free(esoffsprings); free(estotal);
return NLOPT_OUT_OF_MEMORY;
}
for (id=0; id < np; id++) esparents[id].parameters = NULL;
for (id=0; id < no; id++) esoffsprings[id].parameters = NULL;
/* From here the population is initialized */
/* we don't handle unbounded search regions;
this check is unnecessary since it is performed in nlopt_optimize.
for (j = 0; j < nparameters; ++j)
if (nlopt_isinf(low[j]) || nlopt_isinf(up[j]))
return NLOPT_INVALID_ARGS;
*/
/* main vector of parameters to randcauchy */
vetor[0] = 4; /* ignored */
vetor[3] = 0;
vetor[4] = 1;
vetor[5] = 10;
vetor[6] = 1;
vetor[7] = 0; /* ignored */
/**************************************
* Initializing parents population
**************************************/
for (id=0; id < np; id++) {
esparents[id].parameters =
(double*) malloc(sizeof(double) * nparameters);
if (!esparents[id].parameters) {
ret = NLOPT_OUT_OF_MEMORY;
goto done;
}
for (item=0; item<nparameters; item++) {
vetor[1] = lb[item];
vetor[2] = ub[item];
vetor[7]=vetor[7]+1;
esparents[id].parameters[item] = randcauchy(vetor);
}
}
memcpy(esparents[0].parameters, x, nparameters * sizeof(double));
/**************************************
* Initializing offsprings population
**************************************/
for (id=0; id < no; id++) {
esoffsprings[id].parameters =
(double*) malloc(sizeof(double) * nparameters);
if (!esoffsprings[id].parameters) {
ret = NLOPT_OUT_OF_MEMORY;
goto done;
}
for (item=0; item<nparameters; item++) {
vetor[1] = lb[item];
vetor[2] = ub[item];
vetor[7]=vetor[7]+1;
esoffsprings[id].parameters[item] = randcauchy(vetor);
}
}
/**************************************
* Parents fitness evaluation
**************************************/
for (id=0; id < np; id++) {
esparents[id].fitness =
f(nparameters, esparents[id].parameters, NULL, data_f);
estotal[id].fitness = esparents[id].fitness;
stop->nevals++;
if (*minf > esparents[id].fitness) {
*minf = esparents[id].fitness;
memcpy(x, esparents[id].parameters,
nparameters * sizeof(double));
}
if (nlopt_stop_forced(stop)) ret = NLOPT_FORCED_STOP;
else if (*minf < stop->minf_max) ret = NLOPT_MINF_MAX_REACHED;
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;
}
/**************************************
* Main Loop - Generations
**************************************/
while (1) {
/**************************************
* Crossover
**************************************/
for (id=0; id < no; id++)
{
parent1 = nlopt_iurand((int) np);
parent2 = nlopt_iurand((int) np);
crosspoint = (unsigned) nlopt_iurand((int) nparameters);
for (item=0; item < crosspoint; item++)
esoffsprings[id].parameters[item]
= esparents[parent1].parameters[item];
for (item=crosspoint; item < nparameters; item++)
esoffsprings[id].parameters[item]
= esparents[parent2].parameters[item];
}
/**************************************
* Gaussian Mutation
**************************************/
totalmutation = (int) ((no * nparameters) / 10);
if (totalmutation < 1) totalmutation = 1;
for (contmutation=0; contmutation < totalmutation;
contmutation++) {
idoffmutation = nlopt_iurand((int) no);
paramoffmutation = nlopt_iurand((int) nparameters);
vetor[1] = lb[paramoffmutation];
vetor[2] = ub[paramoffmutation];
vetor[7] = vetor[7]+contmutation;
esoffsprings[idoffmutation].parameters[paramoffmutation]
= randcauchy(vetor);
}
/**************************************
* Offsprings fitness evaluation
**************************************/
for (id=0; id < no; id++){
/*esoffsprings[id].fitness = (double)fitness(esoffsprings[id].parameters, nparameters,fittype);*/
esoffsprings[id].fitness = f(nparameters, esoffsprings[id].parameters, NULL, data_f);
estotal[id+np].fitness = esoffsprings[id].fitness;
stop->nevals++;
if (*minf > esoffsprings[id].fitness) {
*minf = esoffsprings[id].fitness;
memcpy(x, esoffsprings[id].parameters,
nparameters * sizeof(double));
}
if (nlopt_stop_forced(stop)) ret = NLOPT_FORCED_STOP;
else if (*minf < stop->minf_max)
ret = NLOPT_MINF_MAX_REACHED;
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;
}
/**************************************
* Individual selection
**************************************/
/* all the individuals are copied to one vector to easily identify best solutions */
for (i=0; i < np; i++)
estotal[i] = esparents[i];
for (i=0; i < no; i++)
estotal[np+i] = esoffsprings[i];
/* Sorting */
nlopt_qsort_r(estotal, no+np, sizeof(Individual), NULL,
CompareIndividuals);
/* copy after sorting: */
for (i=0; i < no+np; i++) {
if (i<np)
esparents[i] = estotal[i];
else
esoffsprings[i-np] = estotal[i];
}
} /* generations loop */
done:
for (id=0; id < np; id++) free(esparents[id].parameters);
for (id=0; id < no; id++) free(esoffsprings[id].parameters);
if (esparents) free(esparents);
if (esoffsprings) free(esoffsprings);
if (estotal) free(estotal);
return ret;
}
static nlopt_result divide_largest(params *p)
{
int L = p->L;
int n = p->n;
rb_node *node = rb_tree_max(&p->rtree); /* just using it as a heap */
double minf_start = p->minf;
double *r = node->k, *rnew = NULL;
double *x = r + 3, *c = x + n, *w = c + n;
const double *lb = p->lb, *ub = p->ub;
int i, idiv;
double wmax;
nlopt_result ret;
/* printf("rect:, %d, %g, %g, %g, %g\n", p->stop->nevals, c[0], c[1], w[0], w[1]); */
/* check xtol */
for (i = 0; i < n; ++i)
if (w[i] > p->stop->xtol_rel * (ub[i] - lb[i])
&& w[i] > p->stop->xtol_abs[i])
break;
if (i == n) return NLOPT_XTOL_REACHED;
if (p->randomized_div) { /* randomly pick among ~largest sides */
int nlongest = 0;
wmax = longest(n, w);
for (i = 0; i < n; ++i)
if (wmax - w[i] < EQUAL_SIDE_TOL * wmax) ++nlongest;
i = 1 + nlopt_iurand(nlongest);
for (idiv = 0; idiv < n; ++idiv) {
if (wmax - w[idiv] < EQUAL_SIDE_TOL * wmax) --i;
if (!i) break;
}
}
else { /* just pick first largest side */
wmax = w[idiv = 0];
for (i = 1; i < n; ++i) if (w[i] > wmax) wmax = w[idiv = i];
}
if (fabs(x[idiv] - c[idiv]) > (0.5 * THIRD) * w[idiv]) { /* bisect */
double deltac = (x[idiv] > c[idiv] ? 0.25 : -0.25) * w[idiv];
w[idiv] *= 0.5;
c[idiv] += deltac;
r[0] = longest(n, w); /* new diameter */
/* r[1] unchanged since still contains local optimum x */
r[2] = p->age--;
node = rb_tree_resort(&p->rtree, node);
rnew = (double *) malloc(sizeof(double) * L);
if (!rnew) return NLOPT_OUT_OF_MEMORY;
memcpy(rnew, r, sizeof(double) * L);
rnew[2] = p->age--;
rnew[3+n+idiv] -= deltac*2;
if (p->randomized_div)
randomize_x(n, rnew);
else
memcpy(rnew+3, rnew+3+n, sizeof(double) * n); /* x = c */
ret = optimize_rect(rnew, p);
if (ret != NLOPT_SUCCESS) { free(rnew); return ret; }
if (!rb_tree_insert(&p->rtree, rnew)) {
free(rnew); return NLOPT_OUT_OF_MEMORY;
}
}
else { /* trisect */
w[idiv] *= THIRD;
r[0] = longest(n, w);
/* r[1] unchanged since still contains local optimum x */
r[2] = p->age--;
node = rb_tree_resort(&p->rtree, node);
for (i = -1; i <= +1; i += 2) {
rnew = (double *) malloc(sizeof(double) * L);
if (!rnew) return NLOPT_OUT_OF_MEMORY;
memcpy(rnew, r, sizeof(double) * L);
rnew[2] = p->age--;
rnew[3+n+idiv] += w[i] * i;
if (p->randomized_div)
randomize_x(n, rnew);
else
memcpy(rnew+3, rnew+3+n, sizeof(double) * n); /* x = c */
ret = optimize_rect(rnew, p);
if (ret != NLOPT_SUCCESS) { free(rnew); return ret; }
if (!rb_tree_insert(&p->rtree, rnew)) {
free(rnew); return NLOPT_OUT_OF_MEMORY;
}
}
}
if (p->minf < minf_start && nlopt_stop_f(p->stop, p->minf, minf_start))
return NLOPT_FTOL_REACHED;
return NLOPT_SUCCESS;
}
static nlopt_result divide_good_rects(params *p)
{
const int n = p->n;
double **hull;
int nhull, i, xtol_reached = 1, divided_some = 0;
double magic_eps = p->magic_eps;
if (p->hull_len < p->rtree.N) {
p->hull_len += p->rtree.N;
p->hull = (double **) realloc(p->hull, sizeof(double*)*p->hull_len);
if (!p->hull) return NLOPT_OUT_OF_MEMORY;
}
nhull = convex_hull(&p->rtree, hull = p->hull, p->which_opt != 1);
divisions:
for (i = 0; i < nhull; ++i) {
double K1 = -HUGE_VAL, K2 = -HUGE_VAL, K;
int im, ip;
/* find unequal points before (im) and after (ip) to get slope */
for (im = i-1; im >= 0 && hull[im][0] == hull[i][0]; --im) ;
for (ip = i+1; ip < nhull && hull[ip][0] == hull[i][0]; ++ip) ;
if (im >= 0)
K1 = (hull[i][1] - hull[im][1]) / (hull[i][0] - hull[im][0]);
if (ip < nhull)
K2 = (hull[i][1] - hull[ip][1]) / (hull[i][0] - hull[ip][0]);
K = MAX(K1, K2);
if (hull[i][1] - K * hull[i][0]
<= p->minf - magic_eps * fabs(p->minf) || ip == nhull) {
/* "potentially optimal" rectangle, so subdivide */
nlopt_result ret = divide_rect(hull[i], p);
divided_some = 1;
if (ret != NLOPT_SUCCESS) return ret;
xtol_reached = xtol_reached && small(hull[i] + 3+n, p);
}
/* for the DIRECT-L variant, we only divide one rectangle out
of all points with equal diameter and function values
... note that for p->which_opt == 1, i == ip-1 should be a no-op
anyway, since we set allow_dups=0 in convex_hull above */
if (p->which_opt == 1)
i = ip - 1; /* skip to next unequal point for next iteration */
else if (p->which_opt == 2) /* like DIRECT-L but randomized */
i += nlopt_iurand(ip - i); /* possibly do another equal pt */
}
if (!divided_some) {
if (magic_eps != 0) {
magic_eps = 0;
goto divisions; /* try again */
}
else { /* WTF? divide largest rectangle with smallest f */
/* (note that this code actually gets called from time
to time, and the heuristic here seems to work well,
but I don't recall this situation being discussed in
the references?) */
rb_node *max = rb_tree_max(&p->rtree);
rb_node *pred = max;
double wmax = max->k[0];
do { /* note: this loop is O(N) worst-case time */
max = pred;
pred = rb_tree_pred(max);
} while (pred && pred->k[0] == wmax);
return divide_rect(max->k, p);
}
}
return xtol_reached ? NLOPT_XTOL_REACHED : NLOPT_SUCCESS;
}
/* divide rectangle idiv in the list p->rects */
static nlopt_result divide_rect(double *rdiv, params *p)
{
int i;
const int n = p->n;
const int L = p->L;
double *c = rdiv + 3; /* center of rect to divide */
double *w = c + n; /* widths of rect to divide */
double wmax = w[0];
int imax = 0, nlongest = 0;
rb_node *node;
for (i = 1; i < n; ++i)
if (w[i] > wmax)
wmax = w[imax = i];
for (i = 0; i < n; ++i)
if (wmax - w[i] <= wmax * EQUAL_SIDE_TOL)
++nlongest;
if (p->which_div == 1 || (p->which_div == 0 && nlongest == n)) {
/* trisect all longest sides, in increasing order of the average
function value along that direction */
double *fv = p->work;
int *isort = p->iwork;
for (i = 0; i < n; ++i) {
if (wmax - w[i] <= wmax * EQUAL_SIDE_TOL) {
double csave = c[i];
c[i] = csave - w[i] * THIRD;
FUNCTION_EVAL(fv[2*i], c, p, 0);
c[i] = csave + w[i] * THIRD;
FUNCTION_EVAL(fv[2*i+1], c, p, 0);
c[i] = csave;
}
else {
fv[2*i] = fv[2*i+1] = HUGE_VAL;
}
}
sort_fv(n, fv, isort);
if (!(node = rb_tree_find(&p->rtree, rdiv)))
return NLOPT_FAILURE;
for (i = 0; i < nlongest; ++i) {
int k;
w[isort[i]] *= THIRD;
rdiv[0] = rect_diameter(n, w, p);
rdiv[2] = p->age++;
node = rb_tree_resort(&p->rtree, node);
for (k = 0; k <= 1; ++k) {
double *rnew;
ALLOC_RECT(rnew, L);
memcpy(rnew, rdiv, sizeof(double) * L);
rnew[3 + isort[i]] += w[isort[i]] * (2*k-1);
rnew[1] = fv[2*isort[i]+k];
rnew[2] = p->age++;
if (!rb_tree_insert(&p->rtree, rnew)) {
free(rnew);
return NLOPT_OUT_OF_MEMORY;
}
}
}
}
else {
int k;
if (nlongest > 1 && p->which_div == 2) {
/* randomly choose longest side */
i = nlopt_iurand(nlongest);
for (k = 0; k < n; ++k)
if (wmax - w[k] <= wmax * EQUAL_SIDE_TOL) {
if (!i) { i = k; break; }
--i;
}
}
else
i = imax; /* trisect longest side */
if (!(node = rb_tree_find(&p->rtree, rdiv)))
return NLOPT_FAILURE;
w[i] *= THIRD;
rdiv[0] = rect_diameter(n, w, p);
rdiv[2] = p->age++;
node = rb_tree_resort(&p->rtree, node);
for (k = 0; k <= 1; ++k) {
double *rnew;
ALLOC_RECT(rnew, L);
memcpy(rnew, rdiv, sizeof(double) * L);
rnew[3 + i] += w[i] * (2*k-1);
FUNCTION_EVAL(rnew[1], rnew + 3, p, rnew);
rnew[2] = p->age++;
if (!rb_tree_insert(&p->rtree, rnew)) {
free(rnew);
return NLOPT_OUT_OF_MEMORY;
}
}
}
return NLOPT_SUCCESS;
}
static int test_function(int ifunc)
{
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));
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;
ret = nlopt_minimize(algorithm,
func.n, bounds_wrap_func, &bw,
lb, ub,
x, &minf,
minf_max, ftol_rel, ftol_abs, xtol_rel, xtabs,
maxeval, maxtime);
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.\n",
minf, testfuncs_counter);
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 (val != minf) {
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;
}
