CAMLprim value ml_gsl_multimin_fdfminimizer_minimum(value ox, value odx, value og, value T)
{
gsl_multimin_fdfminimizer *t=GSLMULTIMINFDFMINIMIZER_VAL(T);
if(Is_block(ox)) {
value x=Unoption(ox);
_DECLARE_VECTOR(x);
_CONVERT_VECTOR(x);
gsl_vector_memcpy(&v_x,
gsl_multimin_fdfminimizer_x(t));
}
if(Is_block(odx)) {
value dx=Unoption(odx);
_DECLARE_VECTOR(dx);
_CONVERT_VECTOR(dx);
gsl_vector_memcpy(&v_dx,
gsl_multimin_fdfminimizer_dx(t));
}
if(Is_block(og)) {
value g=Unoption(og);
_DECLARE_VECTOR(g);
_CONVERT_VECTOR(g);
gsl_vector_memcpy(&v_g,
gsl_multimin_fdfminimizer_gradient(t));
}
return copy_double(gsl_multimin_fdfminimizer_minimum(t));
}
static VALUE rb_gsl_fdfminimizer_minimum(VALUE obj)
{
gsl_multimin_fdfminimizer *gmf = NULL;
double min;
Data_Get_Struct(obj, gsl_multimin_fdfminimizer, gmf);
min = gsl_multimin_fdfminimizer_minimum(gmf);
return rb_float_new(min);
}
// Relax the map
void rmap_relax(rmap_t *self, int num_cycles)
{
int i, n;
gsl_vector *x, *y;
gsl_multimin_function_fdf fdf;
gsl_multimin_fdfminimizer *s;
double step_size, tol;
rmap_scan_t *scan;
// HACK
step_size = 0.01;
tol = 0.001;
// Compute number of free variables
n = 3 * self->num_key_scans;
// Set the initial vector
x = gsl_vector_alloc(n);
for (i = 0; i < self->num_scans; i++)
{
scan = self->scans + i;
if (scan->index >= 0)
{
gsl_vector_set(x, 3 * scan->index + 0, scan->pose.pos.x);
gsl_vector_set(x, 3 * scan->index + 1, scan->pose.pos.y);
gsl_vector_set(x, 3 * scan->index + 2, scan->pose.rot);
}
}
// Allocate minimizer
fdf.f = (double (*) (const gsl_vector*, void*)) rmap_fit_f;
fdf.df = (void (*) (const gsl_vector*, void*, gsl_vector*)) rmap_fit_df;
fdf.fdf = (void (*) (const gsl_vector*, void*, double*, gsl_vector*)) rmap_fit_fdf;
fdf.n = n;
fdf.params = self;
s = gsl_multimin_fdfminimizer_alloc(gsl_multimin_fdfminimizer_vector_bfgs, n);
// Initialize minimizer
gsl_multimin_fdfminimizer_set(s, &fdf, x, step_size, tol);
// Optimize
for (i = 0; i < num_cycles; i++)
{
if (gsl_multimin_fdfminimizer_iterate(s) != GSL_SUCCESS)
break;
}
self->relax_err = gsl_multimin_fdfminimizer_minimum(s);
// Copy corrections back to data structures
y = gsl_multimin_fdfminimizer_x(s);
for (i = 0; i < self->num_scans; i++)
{
scan = self->scans + i;
if (scan->index >= 0)
{
scan->delta.pos.x = gsl_vector_get(y, 3 * scan->index + 0) - scan->pose.pos.x;
scan->delta.pos.y = gsl_vector_get(y, 3 * scan->index + 1) - scan->pose.pos.y;
scan->delta.rot = gsl_vector_get(y, 3 * scan->index + 2) - scan->pose.rot;
}
}
// Clean up
gsl_multimin_fdfminimizer_free(s);
gsl_vector_free(x);
return;
}
int
main (int argc, char *argv[])
{
int i;
int outp;
int n = atoi (argv[1]);
double x, y, z;
gsl_vector *r = gsl_vector_alloc (3 * n);
gsl_vector *dr = gsl_vector_alloc (3 * n);
double uu = 0;
double est;
double tmp1, tmp2;
FILE *in, *out;
//Reading input
in = fopen (argv[2], "r");
if (in != NULL)
{
out = fopen (argv[3], "w");
for (i = 0; i < n; i++)
{
outp = fscanf (in, "%lf %lf %lf", &x, &y, &z);
gsl_vector_set (r, 3 * i, x);
gsl_vector_set (r, 3 * i + 1, y);
gsl_vector_set (r, 3 * i + 2, z);
}
fclose (in);
//Initial energies and forces before optimization
gupta_fdf (r, &n, &uu, dr);
tmp1 = tmp2 = 0.0;
for (i = 0; i < 3 * n; i++)
{
tmp1 = gsl_vector_get (dr, i);
tmp2 += tmp1 * tmp1;
}
tmp2 = sqrt (tmp2);
fprintf (stdout,
"\n \nInitial Energy in file %s was E=%lf and the value of the norm of the gradient was |df|=%lf\n ",
argv[2], uu, tmp2);
//Setting the optimizer
size_t iter = 0;
int status;
const gsl_multimin_fdfminimizer_type *T;
gsl_multimin_fdfminimizer *s;
gsl_multimin_function_fdf my_func;
my_func.n = 3 * n;
my_func.f = gupta_f;
my_func.df = gupta_df;
my_func.fdf = gupta_fdf;
my_func.params = &n;
T = gsl_multimin_fdfminimizer_vector_bfgs2;
s = gsl_multimin_fdfminimizer_alloc (T, 3 * n);
gsl_multimin_fdfminimizer_set (s, &my_func, r, 0.01, 1e-4);
//The actual optimization
do
{
iter++;
status = gsl_multimin_fdfminimizer_iterate (s);
if (status)
break;
status = gsl_multimin_test_gradient (s->gradient, 1e-6);
}
while (status == GSL_CONTINUE && iter < 10000);
est = gsl_multimin_fdfminimizer_minimum (s);
gsl_vector_memcpy (r, gsl_multimin_fdfminimizer_x (s));
//The values of the energies and forces after the optimization
gupta_fdf (r, &n, &uu, dr);
tmp1 = tmp2 = 0.0;
for (i = 0; i < 3 * n; i++)
{
tmp1 = gsl_vector_get (dr, i);
tmp2 += tmp1 * tmp1;
}
tmp2 = sqrt (tmp2);
fprintf (stdout,
"\n \nFinal Energy in file %s was E=%lf and the value of the norm of the gradient was |df|=%lf\n ",
argv[3], uu, tmp2);
for (i = 0; i < n; i++)
{
fprintf (out, "%.16e %.16e %.16e\n", gsl_vector_get (r, 3 * i),
gsl_vector_get (r, 3 * i + 1), gsl_vector_get (r,
3 * i + 2));
}
gsl_multimin_fdfminimizer_free (s);
gsl_vector_free (r);
}
else
{
fprintf (stdout, "The file %s was not found\n", argv[2]);
}
return 0;
}
int FC_FUNC_(oct_minimize, OCT_MINIMIZE)
(const int *method, const int *dim, double *point, const double *step, const double *line_tol,
const double *tolgrad, const double *toldr, const int *maxiter, func_d f,
const print_f_ptr write_info, double *minimum)
{
int iter = 0;
int status;
double maxgrad, maxdr;
int i;
double * oldpoint;
double * grad;
const gsl_multimin_fdfminimizer_type *T = NULL;
gsl_multimin_fdfminimizer *s;
gsl_vector *x;
gsl_vector *absgrad, *absdr;
gsl_multimin_function_fdf my_func;
param_fdf_t p;
p.func = f;
oldpoint = (double *) malloc(*dim * sizeof(double));
grad = (double *) malloc(*dim * sizeof(double));
my_func.f = &my_f;
my_func.df = &my_df;
my_func.fdf = &my_fdf;
my_func.n = *dim;
my_func.params = (void *) &p;
/* Starting point */
x = gsl_vector_alloc (*dim);
for(i=0; i<*dim; i++) gsl_vector_set (x, i, point[i]);
/* Allocate space for the gradient */
absgrad = gsl_vector_alloc (*dim);
absdr = gsl_vector_alloc (*dim);
//GSL recommends line_tol = 0.1;
switch(*method){
case 1:
T = gsl_multimin_fdfminimizer_steepest_descent;
break;
case 2:
T = gsl_multimin_fdfminimizer_conjugate_fr;
break;
case 3:
T = gsl_multimin_fdfminimizer_conjugate_pr;
break;
case 4:
T = gsl_multimin_fdfminimizer_vector_bfgs;
break;
case 5:
T = gsl_multimin_fdfminimizer_vector_bfgs2;
break;
}
s = gsl_multimin_fdfminimizer_alloc (T, *dim);
gsl_multimin_fdfminimizer_set (s, &my_func, x, *step, *line_tol);
do
{
iter++;
for(i=0; i<*dim; i++) oldpoint[i] = point[i];
/* Iterate */
status = gsl_multimin_fdfminimizer_iterate (s);
/* Get current minimum, point and gradient */
*minimum = gsl_multimin_fdfminimizer_minimum(s);
for(i=0; i<*dim; i++) point[i] = gsl_vector_get(gsl_multimin_fdfminimizer_x(s), i);
for(i=0; i<*dim; i++) grad[i] = gsl_vector_get(gsl_multimin_fdfminimizer_gradient(s), i);
/* Compute convergence criteria */
for(i=0; i<*dim; i++) gsl_vector_set(absdr, i, fabs(point[i]-oldpoint[i]));
maxdr = gsl_vector_max(absdr);
for(i=0; i<*dim; i++) gsl_vector_set(absgrad, i, fabs(grad[i]));
maxgrad = gsl_vector_max(absgrad);
/* Print information */
write_info(&iter, dim, minimum, &maxdr, &maxgrad, point);
/* Store infomation for next iteration */
for(i=0; i<*dim; i++) oldpoint[i] = point[i];
if (status)
break;
if ( (maxgrad <= *tolgrad) || (maxdr <= *toldr) ) status = GSL_SUCCESS;
else status = GSL_CONTINUE;
}
while (status == GSL_CONTINUE && iter <= *maxiter);
if(status == GSL_CONTINUE) status = 1025;
gsl_multimin_fdfminimizer_free (s);
gsl_vector_free (x); gsl_vector_free(absgrad); gsl_vector_free(absdr);
free(oldpoint);
free(grad);
return status;
}
int OptimizationOptions::gslOptimize( NLSFunction *F, gsl_vector* x_vec,
gsl_matrix *v, IterationLogger *itLog ) {
const gsl_multifit_fdfsolver_type *Tlm[] =
{ gsl_multifit_fdfsolver_lmder, gsl_multifit_fdfsolver_lmsder };
const gsl_multimin_fdfminimizer_type *Tqn[] =
{ gsl_multimin_fdfminimizer_vector_bfgs,
gsl_multimin_fdfminimizer_vector_bfgs2,
gsl_multimin_fdfminimizer_conjugate_fr,
gsl_multimin_fdfminimizer_conjugate_pr };
const gsl_multimin_fminimizer_type *Tnm[] =
{ gsl_multimin_fminimizer_nmsimplex, gsl_multimin_fminimizer_nmsimplex2,
gsl_multimin_fminimizer_nmsimplex2rand };
int gsl_submethod_max[] = { sizeof(Tlm) / sizeof(Tlm[0]),
sizeof(Tqn) / sizeof(Tqn[0]),
sizeof(Tnm) / sizeof(Tnm[0]) };
int status, status_dx, status_grad, k;
double g_norm, x_norm;
/* vectorize x row-wise */
size_t max_ind, min_ind;
double max_val, min_val, abs_max_val = 0, abs_min_val;
if (this->method < 0 ||
this->method > sizeof(gsl_submethod_max)/sizeof(gsl_submethod_max[0]) ||
this->submethod < 0 ||
this->submethod > gsl_submethod_max[this->method]) {
throw new Exception("Unknown optimization method.\n");
}
if (this->maxiter < 0 || this->maxiter > 5000) {
throw new Exception("opt.maxiter should be in [0;5000].\n");
}
/* LM */
gsl_multifit_fdfsolver* solverlm;
gsl_multifit_function_fdf fdflm = { &(F->_f_ls), &(F->_df_ls), &(F->_fdf_ls),
F->getNsq(), F->getNvar(), F };
gsl_vector *g;
/* QN */
double stepqn = this->step;
gsl_multimin_fdfminimizer* solverqn;
gsl_multimin_function_fdf fdfqn = {
&(F->_f), &(F->_df), &(F->_fdf), F->getNvar(), F };
/* NM */
double size;
gsl_vector *stepnm;
gsl_multimin_fminimizer* solvernm;
gsl_multimin_function fnm = { &(F->_f), F->getNvar(), F };
/* initialize the optimization method */
switch (this->method) {
case SLRA_OPT_METHOD_LM: /* LM */
solverlm = gsl_multifit_fdfsolver_alloc(Tlm[this->submethod],
F->getNsq(), F->getNvar());
gsl_multifit_fdfsolver_set(solverlm, &fdflm, x_vec);
g = gsl_vector_alloc(F->getNvar());
break;
case SLRA_OPT_METHOD_QN: /* QN */
solverqn = gsl_multimin_fdfminimizer_alloc(Tqn[this->submethod],
F->getNvar() );
gsl_multimin_fdfminimizer_set(solverqn, &fdfqn, x_vec,
stepqn, this->tol);
status_dx = GSL_CONTINUE;
break;
case SLRA_OPT_METHOD_NM: /* NM */
solvernm = gsl_multimin_fminimizer_alloc(Tnm[this->submethod], F->getNvar());
stepnm = gsl_vector_alloc(F->getNvar());
gsl_vector_set_all(stepnm, this->step);
gsl_multimin_fminimizer_set( solvernm, &fnm, x_vec, stepnm );
break;
}
/* optimization loop */
Log::lprintf(Log::LOG_LEVEL_FINAL, "SLRA optimization:\n");
status = GSL_SUCCESS;
status_dx = GSL_CONTINUE;
status_grad = GSL_CONTINUE;
this->iter = 0;
switch (this->method) {
case SLRA_OPT_METHOD_LM:
gsl_blas_ddot(solverlm->f, solverlm->f, &this->fmin);
gsl_multifit_gradient(solverlm->J, solverlm->f, g);
gsl_vector_scale(g, 2);
{
gsl_vector *g2 = gsl_vector_alloc(g->size);
F->computeFuncAndGrad(x_vec, NULL, g2);
gsl_vector_sub(g2, g);
if (gsl_vector_max(g2) > 1e-10 || gsl_vector_min(g2) < -1e-10) {
Log::lprintf(Log::LOG_LEVEL_NOTIFY,
"Gradient error, max = %14.10f, min = %14.10f ...",
gsl_vector_max(g2), gsl_vector_min(g2));
print_vec(g2);
}
gsl_vector_free(g2);
}
if (itLog != NULL) {
itLog->reportIteration(0, solverlm->x, this->fmin, g);
}
break;
case SLRA_OPT_METHOD_QN:
this->fmin = gsl_multimin_fdfminimizer_minimum(solverqn);
if (itLog != NULL) {
itLog->reportIteration(0, solverqn->x, this->fmin, solverqn->gradient);
}
break;
case SLRA_OPT_METHOD_NM:
this->fmin = gsl_multimin_fminimizer_minimum( solvernm );
if (itLog != NULL) {
itLog->reportIteration(this->iter, solvernm->x, this->fmin, NULL);
}
break;
}
while (status_dx == GSL_CONTINUE &&
status_grad == GSL_CONTINUE &&
status == GSL_SUCCESS &&
this->iter < this->maxiter) {
if (this->method == SLRA_OPT_METHOD_LM && this->maxx > 0) {
if (gsl_vector_max(solverlm->x) > this->maxx ||
gsl_vector_min(solverlm->x) < -this->maxx ){
break;
}
}
this->iter++;
switch (this->method) {
case SLRA_OPT_METHOD_LM: /* Levenberg-Marquardt */
status = gsl_multifit_fdfsolver_iterate(solverlm);
gsl_multifit_gradient(solverlm->J, solverlm->f, g);
gsl_vector_scale(g, 2);
/* check the convergence criteria */
if (this->epsabs != 0 || this->epsrel != 0) {
status_dx = gsl_multifit_test_delta(solverlm->dx, solverlm->x,
this->epsabs, this->epsrel);
} else {
status_dx = GSL_CONTINUE;
}
status_grad = gsl_multifit_test_gradient(g, this->epsgrad);
gsl_blas_ddot(solverlm->f, solverlm->f, &this->fmin);
if (itLog != NULL) {
itLog->reportIteration(this->iter, solverlm->x, this->fmin, g);
}
break;
case SLRA_OPT_METHOD_QN:
status = gsl_multimin_fdfminimizer_iterate( solverqn );
/* check the convergence criteria */
status_grad = gsl_multimin_test_gradient(
gsl_multimin_fdfminimizer_gradient(solverqn), this->epsgrad);
status_dx = gsl_multifit_test_delta(solverqn->dx, solverqn->x,
this->epsabs, this->epsrel);
this->fmin = gsl_multimin_fdfminimizer_minimum(solverqn);
if (itLog != NULL) {
itLog->reportIteration(this->iter, solverqn->x, this->fmin, solverqn->gradient);
}
break;
case SLRA_OPT_METHOD_NM:
status = gsl_multimin_fminimizer_iterate( solvernm );
/* check the convergence criteria */
size = gsl_multimin_fminimizer_size( solvernm );
status_dx = gsl_multimin_test_size( size, this->epsx );
this->fmin = gsl_multimin_fminimizer_minimum( solvernm );
if (itLog != NULL) {
itLog->reportIteration(this->iter, solvernm->x, this->fmin, NULL);
}
break;
}
}
if (this->iter >= this->maxiter) {
status = EITER;
}
switch (this->method) {
case SLRA_OPT_METHOD_LM:
gsl_vector_memcpy(x_vec, solverlm->x);
if (v != NULL) {
gsl_multifit_covar(solverlm->J, this->epscov, v); /* ??? Different eps */
}
gsl_blas_ddot(solverlm->f, solverlm->f, &this->fmin);
break;
case SLRA_OPT_METHOD_QN:
gsl_vector_memcpy(x_vec, solverqn->x);
this->fmin = solverqn->f;
break;
case SLRA_OPT_METHOD_NM:
gsl_vector_memcpy(x_vec, solvernm->x);
this->fmin = solvernm->fval;
break;
}
/* print exit information */
if (Log::getMaxLevel() >= Log::LOG_LEVEL_FINAL) { /* unless "off" */
switch (status) {
case EITER:
Log::lprintf("SLRA optimization terminated by reaching "
"the maximum number of iterations.\n"
"The result could be far from optimal.\n");
break;
case GSL_ETOLF:
Log::lprintf("Lack of convergence: "
"progress in function value < machine EPS.\n");
break;
case GSL_ETOLX:
Log::lprintf("Lack of convergence: "
"change in parameters < machine EPS.\n");
break;
case GSL_ETOLG:
Log::lprintf("Lack of convergence: "
"change in gradient < machine EPS.\n");
break;
case GSL_ENOPROG:
Log::lprintf("Possible lack of convergence: no progress.\n");
break;
}
if (status_grad != GSL_CONTINUE && status_dx != GSL_CONTINUE) {
Log::lprintf("Optimization terminated by reaching the convergence "
"tolerance for both X and the gradient.\n");
} else {
if (status_grad != GSL_CONTINUE) {
Log::lprintf("Optimization terminated by reaching the convergence "
"tolerance for the gradient.\n");
} else {
Log::lprintf("Optimization terminated by reaching the convergence "
"tolerance for X.\n");
}
}
}
/* Cleanup */
switch (this->method) {
case SLRA_OPT_METHOD_LM: /* LM */
gsl_multifit_fdfsolver_free(solverlm);
gsl_vector_free(g);
break;
case SLRA_OPT_METHOD_QN: /* QN */
gsl_multimin_fdfminimizer_free(solverqn);
break;
case SLRA_OPT_METHOD_NM: /* NM */
gsl_multimin_fminimizer_free(solvernm);
gsl_vector_free(stepnm);
break;
}
return GSL_SUCCESS; /* <- correct with status */
}
void minim(density_t * ndft){
const gsl_multimin_fdfminimizer_type *T;
gsl_multimin_fdfminimizer *s;
gsl_multimin_function_fdf my_func;
gsl_vector * grad_initial;
size_t iter;
ipf_t ipf_previous;
int status;
double minimum, g_initial, norm_initial;
char * output_string;
int i;
params_gsl_multimin_function_t params;
output_string = (char *) malloc(25*sizeof(char));
params.nDFT = density_get_val(ndft);
my_func.n = ipf.npar;
my_func.f = my_f;
my_func.df = my_df;
my_func.fdf = my_fdf;
my_func.params = (void *) (¶ms);
/* We use the BFGS algorithm from thee GNU Scientific Library (GSL)
in its optimized version bfgs2 */
T = gsl_multimin_fdfminimizer_vector_bfgs2;
messages_basic("\n\nStarting the optimization.\n\n\n");
grad_initial = gsl_vector_alloc(ipf.npar);
my_fdf (ipf.gamma, ¶ms, &g_initial, grad_initial);
norm_initial = gsl_blas_dnrm2(grad_initial);
if(g_initial < epsilon_gvalue){
if(myid == 0) printf("The value of G for the starting gamma is %.12lg,\n", g_initial);
if(myid == 0) printf("which is already below the requested threshold of %.12lg\n", epsilon_gvalue);
parallel_end(EXIT_SUCCESS);
}
if(norm_initial < epsilon_grad){
if(myid == 0) printf("The value of the normm of the gradient of G for the starting gamma is %.12lg,\n", norm_initial);
if(myid == 0) printf("which is already below the requested threshold of %.12lg\n", epsilon_grad);
parallel_end(EXIT_SUCCESS);
}
if(myid == 0) printf(" Starting from gamma = ");
if(myid == 0) {for(i = 0; i < ipf.npar; i++) printf ("%.5f ", gsl_vector_get (ipf.gamma, i));}
if(myid == 0) printf("\n G(gamma) = %.12lg\n", g_initial);
if(myid == 0) printf(" ||Grad G(gamma)|| = %.12lg\n\n", norm_initial);
/* Initialization of the minimizer s for the function
my_func starting at the x point
The step for the first try is 0.1
and the convergence criteria is 0.1 */
messages_basic("\n\nInitialization of the minimizer.\n\n\n");
s = gsl_multimin_fdfminimizer_alloc (T, ipf.npar);
gsl_multimin_fdfminimizer_set (s, &my_func, ipf.gamma, 0.1, 0.1);
minimum = g_initial;
iter = 0;
do{
iter++;
ipf_copy(&ipf_previous, &ipf);
if(myid == 0) printf(" Iter = %d\n", (int)iter);
if(myid == 0) printf(" starting gamma = ");
if(myid == 0) {for(i = 0; i < ipf.npar; i++) printf ("%.5f ", gsl_vector_get (gsl_multimin_fdfminimizer_x(s), i));}
if(myid == 0) printf("\n starting G(gamma) = %15.10f\n", minimum);
/* We make an iteration of the minimizer s */
status = gsl_multimin_fdfminimizer_iterate (s);
minimum = gsl_multimin_fdfminimizer_minimum(s);
if (status){
if(myid == 0) printf(" Breaking. Reason: %s\n", gsl_strerror(status));
break;
}
if(myid == 0) printf(" ||Grad G(gamma)|| = %15.10f\n", gsl_blas_dnrm2(gsl_multimin_fdfminimizer_gradient(s)));
if(myid == 0) printf(" G(gamma) = %15.10f\n", minimum);
/* Checks convergence */
if(minimum < epsilon_gvalue){
status = GSL_SUCCESS;
}else{
status = gsl_multimin_test_gradient (gsl_multimin_fdfminimizer_gradient(s), epsilon_grad);
}
gsl_vector_memcpy(ipf.gamma, gsl_multimin_fdfminimizer_x(s));
ipf_write(ipf, ipf_ref, "intermediate-pot");
/* Print the diff between initial and reference IPFs */
if(myid == 0) printf(" Diff[ IPF(previous), IPF(new) ] = %.12lg\n", ipf_diff(&ipf_previous, &ipf));
ipf_end(&ipf_previous);
}
while (status == GSL_CONTINUE && iter < minim_maxiter);
if(myid == 0) printf("\n\nFinished optimization. status = %d (%s)\n", status, gsl_strerror(status));
if(myid == 0) printf(" Final gamma = ");
if(myid == 0) {for(i = 0; i < ipf.npar; i++) printf ("%.5f ", gsl_vector_get (gsl_multimin_fdfminimizer_x(s), i));}
if(myid == 0) printf("\n With value: G(gamma) = %.12lg\n\n", gsl_multimin_fdfminimizer_minimum(s));
gsl_vector_memcpy(ipf.gamma, gsl_multimin_fdfminimizer_x(s));
sprintf(output_string, "pot");
ipf_write(ipf, ipf_ref, output_string);
gsl_multimin_fdfminimizer_free (s);
fflush(stdout);
}
void GslOptimizer::minimize_with_gradient( unsigned int dim, OptimizerMonitor* monitor )
{
// Set initial point
gsl_vector * x = gsl_vector_alloc(dim);
for (unsigned int i = 0; i < dim; i++) {
gsl_vector_set(x, i, (*m_initialPoint)[i]);
}
// Tell GSL which solver we're using
const gsl_multimin_fdfminimizer_type* type = NULL;
// We use m_solver_type here because we need the enum
switch(m_solver_type)
{
case(FLETCHER_REEVES_CG):
type = gsl_multimin_fdfminimizer_conjugate_fr;
break;
case(POLAK_RIBIERE_CG):
type = gsl_multimin_fdfminimizer_conjugate_pr;
break;
case(BFGS):
type = gsl_multimin_fdfminimizer_vector_bfgs;
break;
case(BFGS2):
type = gsl_multimin_fdfminimizer_vector_bfgs2;
break;
case(STEEPEST_DESCENT):
type = gsl_multimin_fdfminimizer_steepest_descent;
break;
case(NELDER_MEAD):
case(NELDER_MEAD2):
case(NELDER_MEAD2_RAND):
default:
// Wat?!
queso_error();
}
// Init solver
gsl_multimin_fdfminimizer * solver =
gsl_multimin_fdfminimizer_alloc(type, dim);
// Point GSL to the right functions
gsl_multimin_function_fdf minusLogPosterior;
minusLogPosterior.n = dim;
minusLogPosterior.f = &c_evaluate;
minusLogPosterior.df = &c_evaluate_derivative;
minusLogPosterior.fdf = &c_evaluate_with_derivative;
minusLogPosterior.params = (void *)(this);
gsl_multimin_fdfminimizer_set(solver, &minusLogPosterior, x, getFdfstepSize(), getLineTolerance());
int status;
size_t iter = 0;
do {
iter++;
status = gsl_multimin_fdfminimizer_iterate(solver);
if (status) {
if( m_objectiveFunction.domainSet().env().fullRank() == 0 )
{
std::cerr << "Error while GSL does optimisation. "
<< "See below for GSL error type." << std::endl;
std::cerr << "Gsl error: " << gsl_strerror(status) << std::endl;
}
break;
}
status = gsl_multimin_test_gradient(solver->gradient, this->getTolerance());
if(monitor)
{
gsl_vector* x = gsl_multimin_fdfminimizer_x(solver);
std::vector<double> x_min(dim);
for( unsigned int i = 0; i < dim; i++)
x_min[i] = gsl_vector_get(x,i);
double f = gsl_multimin_fdfminimizer_minimum(solver);
gsl_vector* grad = gsl_multimin_fdfminimizer_gradient(solver);
double grad_norm = gsl_blas_dnrm2(grad);
monitor->append( x_min, f, grad_norm );
}
} while ((status == GSL_CONTINUE) && (iter < this->getMaxIterations()));
for (unsigned int i = 0; i < dim; i++) {
(*m_minimizer)[i] = gsl_vector_get(solver->x, i);
}
// We're being good human beings and cleaning up the memory we allocated
gsl_multimin_fdfminimizer_free(solver);
gsl_vector_free(x);
return;
}
