Vector MultiSolver::NLLeastSquareSolver(MultiSolverInput *input)
{
/**/condition.analyzer->solverSolving.startTimer(); // ---- ---- T5 start
const gsl_multifit_fdfsolver_type *T;
gsl_multifit_fdfsolver *s;
gsl_multifit_function_fdf f;
double x_init[3] = {0.0, 0.0, 150.0};
// x_init[0] = input->initLocation.x;
// x_init[1] = input->initLocation.y;
// x_init[2] = input->initLocation.z;
gsl_vector_view x = gsl_vector_view_array(x_init, 3);
int n = (int)input->data.size();
// sort restriction..
// n = 3;
int p = 3;
f.f = &ms_f;
f.df = &ms_df;
f.fdf = &ms_fdf;
f.n = n;
f.p = p;
f.params = input;
T = gsl_multifit_fdfsolver_lmsder;
s = gsl_multifit_fdfsolver_alloc(T, n, p);
gsl_multifit_fdfsolver_set(s, &f, &x.vector);
gsl_vector *gradt = gsl_vector_alloc(p);
int iter = 0;
int status;
do
{
iter ++;
status = gsl_multifit_fdfsolver_iterate(s);
if (status) break;
/// status = gsl_multifit_test_delta(s->dx, s->x, 1e-4, 1e-4);
gsl_multifit_gradient(s->J, s->f, gradt);
status = gsl_multifit_test_gradient(gradt, 1e-6);
}
while (status == GSL_CONTINUE && iter < 500);
Vector point = Vector(gsl_vector_get(s->x, 0), gsl_vector_get(s->x, 1), gsl_vector_get(s->x, 2));
gsl_vector_free(gradt);
gsl_multifit_fdfsolver_free(s);
/**/condition.analyzer->solverSolving.stopTimer(); // ---- ---- T5 end
return point;
}
/**
* C++ version of gsl_multifit_test_gradient().
* @param g Residual gradient
* @param epsabs Absolute error bounds
* @return GSL_SUCCESS if test condition reached
*/
inline int gradient( vector const& g, double epsabs ){
return gsl_multifit_test_gradient( g.get(), epsabs ); }
void PlaneDetector::solve()
{
// prepare calculation data
DetectionData baseData;
// prepare gsl variables
const gsl_multifit_fdfsolver_type *T;
gsl_multifit_fdfsolver *s;
int n = (int)data.size();
#ifdef VECTOR_SOLVER
const int p = 4;
double x_init[p] = {0.0, 0.0, 1.0, 0.0};
#endif
#ifdef ANGLE_SOLVER
const int p = 3;
// double x_init[p] = {0.0, 0.0, 0.0};
double x_init[p] = {0.0, 90.0 / 180.0 * M_PI, 0.0};
#endif
#ifdef VECTOR2_SOLVER
const int p = 6;
// double x_init[p] = {1.0, 0.0, 0.0, 500.0, 0.0, 0.0};
double x_init[p] = {0.0, 0.0, 0.0, 0.0, 0.0, 0.0};
#endif
#ifdef ANGLE2_SOLVER
const int p = 5;
double x_init[p] = {0.0, M_PI / 2.0 + 0.1, 000.0, 0.0, 0.0};
#endif
gsl_multifit_function_fdf f;
f.f = &pd_func_f;
f.df = &pd_func_df;
f.fdf = &pd_func_fdf;
f.n = n;
f.p = p;
f.params = &data;
gsl_vector_view x = gsl_vector_view_array(x_init, p);
T = gsl_multifit_fdfsolver_lmsder;
s = gsl_multifit_fdfsolver_alloc(T, n, p);
gsl_multifit_fdfsolver_set(s, &f, &x.vector);
gsl_vector *gradt = gsl_vector_alloc(p);
int iter = 0;
int status;
do
{
iter ++;
status = gsl_multifit_fdfsolver_iterate(s);
if (status)
{
// printf ("error: %s\n", gsl_strerror (status));
break;
}
gsl_multifit_gradient(s->J, s->f, gradt);
status = gsl_multifit_test_gradient(gradt, 1e-6);
} while (status == GSL_CONTINUE && iter < 10000);
#ifdef VECTOR_SOLVER
vN = Vector(
gsl_vector_get(s->x, 0),
gsl_vector_get(s->x, 1),
gsl_vector_get(s->x, 2)).getUnitVector();
fD = gsl_vector_get(s->x, 3);
#endif
#ifdef ANGLE_SOLVER
double theta = gsl_vector_get(s->x, 0);
double pi = gsl_vector_get(s->x, 1);
vN = Vector(sin(pi)*cos(theta), sin(pi)*sin(theta), cos(pi));
double d = gsl_vector_get(s->x, 2);
fD = - ((vN & vListener) + d);
#endif
#ifdef VECTOR2_SOLVER
vN = Vector(
gsl_vector_get(s->x, 0),
gsl_vector_get(s->x, 1),
gsl_vector_get(s->x, 2)).getUnitVector();
Vector vX1 = Vector(
gsl_vector_get(s->x, 3),
gsl_vector_get(s->x, 4),
gsl_vector_get(s->x, 5));
fD = -(vN & vX1);
#endif
#ifdef ANGLE2_SOLVER
double theta = gsl_vector_get(s->x, 0);
double pi = gsl_vector_get(s->x, 1);
vN = Vector(sin(pi)*cos(theta), sin(pi)*sin(theta), cos(pi));
Vector vX1 = Vector(
gsl_vector_get(s->x, 2),
gsl_vector_get(s->x, 3),
gsl_vector_get(s->x, 4));
fD = -(vN & vX1);
#endif
#if 0
for (double pp = 0; pp < M_PI; pp += 0.1)
{
for (double tt = 0; tt < M_PI; tt += 0.1)
{
Vector vNN = Vector(sin(pp)*cos(tt), sin(pp)*sin(tt), cos(pp));
printf("vN = ");
vNN.print();
double sum = 0;
for (size_t i = 0; i < data.size(); i++)
{
sum += pow(data[i].getF(tt, pp, fD), 2);
}
printf(" , sum = %f\n", sum);
}
}
#endif
#if 0
double sqrsum = 0;
for (size_t i = 0; i < data.size(); i++)
{
#ifdef VECTOR_SOLVER
sqrsum += pow(data[i].getF(vN, fD), 2);
#endif
#ifdef ANGLE_SOLVER
sqrsum += pow(data[i].getF(theta, pi, fD), 2);
#endif
#ifdef VECTOR2_SOLVER
sqrsum += pow(data[i].getF(vN, fD), 2);
#endif
// printf ("%f/", sqrsum);
}
vN.print();
printf(" %f %f\n", fD, sqrt(sqrsum));
#endif
// for (size_t i = 0; i < data.size(); i++)
// printf ("F_%d = %f\n", i, data[i].getF(vN));
gsl_vector_free(gradt);
gsl_multifit_fdfsolver_free(s);
vP.clear();
for (size_t i = 0; i < data.size(); i++)
{
vP.push_back(data[i].getP(vN));
}
}
static int MLalgo(struct dataStruct *data) {
/* declare solvers */
const gsl_multifit_fdfsolver_type *T;
gsl_multifit_fdfsolver *s;
gsl_vector_view x = gsl_vector_view_array(data->x_init, data->np);
const gsl_rng_type *type;
gsl_rng_env_setup();
type = gsl_rng_default;
gsl_multifit_function_fdf f;
f.f = &gaussian_f;
f.df = &gaussian_df;
f.fdf = &gaussian_fdf;
f.n = data->nValid;
f.p = data->np;
f.params = data;
T = gsl_multifit_fdfsolver_lmsder;
s = gsl_multifit_fdfsolver_alloc(T, data->nValid, data->np);
gsl_multifit_fdfsolver_set(s, &f, &x.vector);
int status, status2;
int iter = 0;
gsl_vector *gradt = gsl_vector_alloc(data->np);
do {
iter++;
status = gsl_multifit_fdfsolver_iterate(s);
if (status)
break;
status = gsl_multifit_test_delta(s->dx, s->x, data->eAbs, data->eRel);
gsl_multifit_gradient(s->J, s->f, gradt);
status2 = gsl_multifit_test_gradient(gradt, data->eAbs);
}
while ((status == GSL_CONTINUE || status2 == GSL_CONTINUE) && iter < data->maxIter);
gsl_vector_free(gradt);
int i;
for (i=0; i<data->np; ++i) {
data->prmVect[data->estIdx[i]] = gsl_vector_get(s->x, i);
}
data->prmVect[3] = fabs(data->prmVect[3]);
/* copy residuals */
data->residuals = gsl_vector_alloc(data->nValid);
gsl_vector_memcpy(data->residuals, s->f);
/* copy Jacobian */
data->J = gsl_matrix_alloc(data->nValid, data->np);
gsl_matrix_memcpy(data->J, s->J);
gsl_multifit_fdfsolver_free(s);
return 0;
}
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 */
}
int OptimizationOptions::lmpinvOptimize( NLSFunction *F, gsl_vector* x_vec,
IterationLogger *itLog ) {
int status, status_dx, status_grad, k;
double g_norm, x_norm;
if (this->maxiter < 0 || this->maxiter > 5000) {
throw new Exception("opt.maxiter should be in [0;5000].\n");
}
int scaled = 1; //this->submethod;
/* LM */
gsl_matrix *jac = gsl_matrix_alloc(F->getNsq(), F->getNvar());
gsl_vector *func = gsl_vector_alloc(F->getNsq());
gsl_vector *g = gsl_vector_alloc(F->getNvar());
gsl_vector *x_cur = gsl_vector_alloc(F->getNvar());
gsl_vector *x_new = gsl_vector_alloc(F->getNvar());
gsl_vector *dx = gsl_vector_alloc(F->getNvar());
gsl_vector *scaling = scaled ? gsl_vector_alloc(F->getNvar()) : NULL;
gsl_matrix *tempv = gsl_matrix_alloc(jac->size2, jac->size2);
gsl_vector *tempufuncsig = gsl_vector_alloc(jac->size2);
gsl_vector *templm = gsl_vector_alloc(jac->size2);
gsl_vector *sig = gsl_vector_alloc(mymin(jac->size1, jac->size2));
double lambda2 = 0, f_new;
int start_lm = 1;
/* Determine optimal work */
size_t status_svd = 0, minus1 = -1;
double tmp;
dgesvd_("A", "O", &jac->size2, &jac->size1, jac->data, &jac->tda, sig->data,
tempv->data, &tempv->size2, NULL, &jac->size1, &tmp, &minus1, &status_svd);
gsl_vector *work_vec = gsl_vector_alloc(tmp);
/* 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;
gsl_vector_memcpy(x_cur, x_vec);
F->computeFuncAndJac(x_cur, func, jac);
gsl_multifit_gradient(jac, func, g);
gsl_vector_scale(g, 2);
gsl_blas_ddot(func, func, &this->fmin);
if (itLog != NULL) {
itLog->reportIteration(0, x_cur, this->fmin, g);
}
{
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);
}
while (status_dx == GSL_CONTINUE &&
status_grad == GSL_CONTINUE &&
status == GSL_SUCCESS &&
this->iter < this->maxiter) {
/* Check convergence criteria (except dx) */
if (this->maxx > 0) {
if (gsl_vector_max(x_cur) > this->maxx || gsl_vector_min(x_cur) < -this->maxx ){
break;
}
}
this->iter++;
if (scaling != NULL) {
normalizeJacobian(jac, scaling);
}
/* Compute the SVD */
dgesvd_("A", "O", &jac->size2, &jac->size1, jac->data, &jac->tda, sig->data,
tempv->data, &tempv->size2, NULL, &jac->size1, work_vec->data,
&work_vec->size, &status_svd);
gsl_blas_dgemv(CblasTrans, -1.0, jac, func, 0.0, tempufuncsig);
gsl_vector_mul(tempufuncsig, sig);
while (1) {
moveGN(tempv, sig, tempufuncsig, lambda2, dx, F->getNEssVar(), scaling);
gsl_vector_memcpy(x_new, x_cur);
gsl_vector_add(x_new, dx);
F->computeFuncAndGrad(x_new, &f_new, NULL);
if (f_new <= this->fmin + 1e-16) {
lambda2 = 0.4 * lambda2;
break;
}
if (lambda2 > 1e100) {
status = GSL_ENOPROG;
break;
}
/* Else: update lambda */
if (start_lm) {
lambda2 = gsl_vector_get(sig, 0) * gsl_vector_get(sig, 0);
start_lm = 0;
} else {
lambda2 = 10 * lambda2;
Log::lprintf(Log::LOG_LEVEL_ITER, "lambda: %f\n", lambda2);
}
}
/* check the dx convergence criteria */
if (this->epsabs != 0 || this->epsrel != 0) {
status_dx = gsl_multifit_test_delta(dx, x_cur, this->epsabs, this->epsrel);
}
gsl_vector_memcpy(x_cur, x_new);
F->computeFuncAndJac(x_cur, func, jac);
gsl_multifit_gradient(jac, func, g);
gsl_vector_scale(g, 2);
gsl_blas_ddot(func, func, &this->fmin);
if (itLog != NULL) {
itLog->reportIteration(this->iter, x_cur, this->fmin, g);
}
status_grad = gsl_multifit_test_gradient(g, this->epsgrad);
}
if (this->iter >= this->maxiter) {
status = EITER;
}
gsl_blas_ddot(func, func, &this->fmin);
/* 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");
}
}
}
gsl_vector_memcpy(x_vec, x_cur);
gsl_vector_free(work_vec);
gsl_matrix_free(jac);
gsl_vector_free(func);
gsl_vector_free(g);
gsl_vector_free(x_cur);
gsl_vector_free(x_new);
if (scaling != NULL) {
gsl_vector_free(scaling);
}
gsl_vector_free(dx);
gsl_matrix_free(tempv);
gsl_vector_free(tempufuncsig);
gsl_vector_free(templm);
gsl_vector_free(sig);
return GSL_SUCCESS; /* <- correct with status */
}