int main (void) { const gsl_multifit_nlinear_type *T = gsl_multifit_nlinear_trust; gsl_multifit_nlinear_workspace *w; gsl_multifit_nlinear_fdf fdf; gsl_multifit_nlinear_parameters fdf_params = gsl_multifit_nlinear_default_parameters(); const size_t n = N; const size_t p = 3; gsl_vector *f; gsl_matrix *J; gsl_matrix *covar = gsl_matrix_alloc (p, p); double y[N], weights[N]; struct data d = { n, y }; double x_init[3] = { 1.0, 1.0, 0.0 }; /* starting values */ gsl_vector_view x = gsl_vector_view_array (x_init, p); gsl_vector_view wts = gsl_vector_view_array(weights, n); gsl_rng * r; double chisq, chisq0; int status, info; size_t i; const double xtol = 1e-8; const double gtol = 1e-8; const double ftol = 0.0; gsl_rng_env_setup(); r = gsl_rng_alloc(gsl_rng_default); /* define the function to be minimized */ fdf.f = expb_f; fdf.df = expb_df; /* set to NULL for finite-difference Jacobian */ fdf.fvv = NULL; /* not using geodesic acceleration */ fdf.n = n; fdf.p = p; fdf.params = &d; /* this is the data to be fitted */ for (i = 0; i < n; i++) { double t = i; double yi = 1.0 + 5 * exp (-0.1 * t); double si = 0.1 * yi; double dy = gsl_ran_gaussian(r, si); weights[i] = 1.0 / (si * si); y[i] = yi + dy; printf ("data: "F_ZU" %g %g\n", i, y[i], si); }; /* allocate workspace with default parameters */ w = gsl_multifit_nlinear_alloc (T, &fdf_params, n, p); /* initialize solver with starting point and weights */ gsl_multifit_nlinear_winit (&x.vector, &wts.vector, &fdf, w); /* compute initial cost function */ f = gsl_multifit_nlinear_residual(w); gsl_blas_ddot(f, f, &chisq0); /* solve the system with a maximum of 20 iterations */ status = gsl_multifit_nlinear_driver(20, xtol, gtol, ftol, callback, NULL, &info, w); /* compute covariance of best fit parameters */ J = gsl_multifit_nlinear_jac(w); gsl_multifit_nlinear_covar (J, 0.0, covar); /* compute final cost */ gsl_blas_ddot(f, f, &chisq); #define FIT(i) gsl_vector_get(w->x, i) #define ERR(i) sqrt(gsl_matrix_get(covar,i,i)) fprintf(stderr, "summary from method '%s/%s'\n", gsl_multifit_nlinear_name(w), gsl_multifit_nlinear_trs_name(w)); fprintf(stderr, "number of iterations: "F_ZU"\n", gsl_multifit_nlinear_niter(w)); fprintf(stderr, "function evaluations: "F_ZU"\n", fdf.nevalf); fprintf(stderr, "Jacobian evaluations: "F_ZU"\n", fdf.nevaldf); fprintf(stderr, "reason for stopping: %s\n", (info == 1) ? "small step size" : "small gradient"); fprintf(stderr, "initial |f(x)| = %f\n", sqrt(chisq0)); fprintf(stderr, "final |f(x)| = %f\n", sqrt(chisq)); { double dof = n - p; double c = GSL_MAX_DBL(1, sqrt(chisq / dof)); fprintf(stderr, "chisq/dof = %g\n", chisq / dof); fprintf (stderr, "A = %.5f +/- %.5f\n", FIT(0), c*ERR(0)); fprintf (stderr, "lambda = %.5f +/- %.5f\n", FIT(1), c*ERR(1)); fprintf (stderr, "b = %.5f +/- %.5f\n", FIT(2), c*ERR(2)); } fprintf (stderr, "status = %s\n", gsl_strerror (status)); gsl_multifit_nlinear_free (w); gsl_matrix_free (covar); gsl_rng_free (r); return 0; }
static void test_fdf(const gsl_multifit_nlinear_type * T, const gsl_multifit_nlinear_parameters * params, const double xtol, const double gtol, const double ftol, const double epsrel, test_fdf_problem *problem, const double *wts) { gsl_multifit_nlinear_fdf *fdf = problem->fdf; const size_t n = fdf->n; const size_t p = fdf->p; const size_t max_iter = 2500; gsl_vector *x0 = gsl_vector_alloc(p); gsl_vector_view x0v = gsl_vector_view_array(problem->x0, p); gsl_multifit_nlinear_workspace *w = gsl_multifit_nlinear_alloc (T, params, n, p); const char *pname = problem->name; char buf[2048]; char sname[2048]; int status, info; sprintf(buf, "%s/%s/scale=%s/solver=%s", gsl_multifit_nlinear_name(w), params->trs->name, params->scale->name, params->solver->name); if (problem->fdf->df == NULL) { if (params->fdtype == GSL_MULTIFIT_NLINEAR_FWDIFF) strcat(buf, "/fdjac,forward"); else strcat(buf, "/fdjac,center"); } if (problem->fdf->fvv == NULL) { strcat(buf, "/fdfvv"); } strcpy(sname, buf); gsl_vector_memcpy(x0, &x0v.vector); if (wts) { gsl_vector_const_view wv = gsl_vector_const_view_array(wts, n); gsl_multifit_nlinear_winit(x0, &wv.vector, fdf, w); } else gsl_multifit_nlinear_init(x0, fdf, w); status = gsl_multifit_nlinear_driver(max_iter, xtol, gtol, ftol, NULL, NULL, &info, w); gsl_test(status, "%s/%s did not converge, status=%s", sname, pname, gsl_strerror(status)); /* check solution */ test_fdf_checksol(sname, pname, epsrel, w, problem); if (wts == NULL) { /* test again with weighting matrix W = I */ gsl_vector *wv = gsl_vector_alloc(n); sprintf(sname, "%s/weighted", buf); gsl_vector_memcpy(x0, &x0v.vector); gsl_vector_set_all(wv, 1.0); gsl_multifit_nlinear_winit(x0, wv, fdf, w); status = gsl_multifit_nlinear_driver(max_iter, xtol, gtol, ftol, NULL, NULL, &info, w); gsl_test(status, "%s/%s did not converge, status=%s", sname, pname, gsl_strerror(status)); test_fdf_checksol(sname, pname, epsrel, w, problem); gsl_vector_free(wv); } gsl_multifit_nlinear_free(w); gsl_vector_free(x0); }