Some remarks on the performance of Matlab, Python and Octave in simulating dynamical systems

TL;DR

This paper compares Matlab, Python, and Octave in simulating dynamical systems, focusing on accuracy and computational efficiency, with Octave being most reliable and Matlab fastest.

Contribution

It provides a novel comparison of these languages using the lower bound error on chaotic systems, highlighting their strengths and weaknesses.

Findings

Octave offers the most reliable simulation results.

Matlab achieves the shortest computation time.

Python performs poorly on the stop simulation criterion.

Abstract

Matlab has been considered as a leader computational platform for many engineering fields. Well documented and reliable, Matlab presents as a great advantage its ability to increase the user productivity. However, Python and Octave are among some of the languages that have challenged Matlab. Octave and Python are well known examples of high-level scripting languages, with a great advantage of being open source software. The novelty of this paper is devoted to offer a comparison among these tree languages in the simulation of dynamical systems. We have applied the lower bound error to estimate the error of simulation. The comparison was performed with the chaotic systems Duffing-Ueda oscillator and the Chua's circuit, both identified with polynomial NARMAX. Octave presents the best reliable outcome. Nevertheless, Matlab needs the lowest time to undertake the same activity. Python has presented the worse result for the stop simulation criterion.