Hybrid minimization algorithm for computationally expensive multi-dimensional fitting

TL;DR

This paper introduces a hybrid minimization method that efficiently finds best-fit parameters in multi-dimensional astrophysical models without relying on interpolation, reducing computational costs.

Contribution

A novel hybrid minimization algorithm that uses local quadratic approximation to avoid model interpolation in computationally expensive multi-dimensional fitting.

Findings

Effective in analyzing stellar spectra

Applicable to extragalactic spectra

Reduces computational expense

Abstract

Multi-dimensional optimization is widely used in virtually all areas of modern astrophysics. However, it is often too computationally expensive to evaluate a model on-the-fly. Typically, it is solved by pre-computing a grid of models for a predetermined set of positions in the parameter space and then interpolating. Here we present a hybrid minimization approach based on the local quadratic approximation of the $\chi^2$ profile from a discrete set of models in a multidimensional parameter space. The main idea of our approach is to eliminate the interpolation of models from the process of finding the best-fitting solution. We present several examples of applications of our minimization technique to the analysis of stellar and extragalactic spectra.