bool LMfit::do_iteration()
//pre: init() callled
{
if (na_ < 1)
throw ExecuteError("No parameters to fit.");
iter_nr_++;
alpha_ = alpha;
for (int j = 0; j < na_; j++)
alpha_[na_ * j + j] *= (1.0 + lambda);
beta_ = beta;
if (F_->get_verbosity() > 1) { // level: debug
F_->ui()->mesg(print_matrix (beta_, 1, na_, "beta"));
F_->ui()->mesg(print_matrix (alpha_, na_, na_, "alpha'"));
}
// Matrix solution (Ax=b) alpha_ * da == beta_
Jordan (alpha_, beta_, na_);
// da is in beta_
if (F_->get_verbosity() >= 1) { // level: verbose
vector<realt> rel(na_);
for (int q = 0; q < na_; q++)
rel[q] = beta_[q] / a[q] * 100;
if (F_->get_verbosity() >= 1)
F_->ui()->mesg(print_matrix (rel, 1, na_, "delta(A)/A[%]"));
}
for (int i = 0; i < na_; i++)
beta_[i] = a[i] + beta_[i]; // and now there is new a[] in beta_[]
if (F_->get_verbosity() >= 1)
output_tried_parameters(beta_);
// compute chi2_
chi2_ = compute_wssr(beta_, dmdm_);
if (chi2_ < chi2) { // better fitting
chi2 = chi2_;
a = beta_;
compute_derivatives(a, dmdm_, alpha, beta);
lambda /= F_->get_settings()->lm_lambda_down_factor;
return true;
}
else { // worse fitting
lambda *= F_->get_settings()->lm_lambda_up_factor;
return false;
}
}
double NLfit::calculate(int n, const double* par, double* grad)
{
assert(n == na_);
vector<realt> A(par, par+n);
if (F_->get_verbosity() >= 1)
output_tried_parameters(A);
bool stop = common_termination_criteria();
if (stop)
nlopt_force_stop(opt_);
double wssr;
if (!grad || stop)
wssr = compute_wssr(A, fitted_datas_);
else
wssr = compute_wssr_gradient(A, fitted_datas_, grad);
if (F_->get_verbosity() >= 1)
F_->ui()->mesg(iteration_info(wssr));
return wssr;
}
