Efficiency of numerical analysis with Python, Matlab and Octave

Abstract

Numerical analysis deals with the approximate solution of mathematical problems that can be performed efficiently with computers. The goal of this article is that, to find out, which programming tool is the fastest in various numerical problems. The software tools that I test are the Python with NumPy and SciPy packages, the Matlab and the GNU Octave. Our tests are focusing at the basic numerical operations, like matrix operations, interpolations or solving linear equation systems or compute derivates and integrates and trying to find out which tool is the fastest in the given test and which is the fastest overall.