optipoly: A Python package for boxed-constrained multi-variable polynomial cost functions optimization
Mazen Alamir
TL;DR
optipoly introduces a Python package for box-constrained optimization of multivariate polynomial cost functions. It describes a dedicated optimization algorithm and compares its performance to general-purpose NLP solvers in Gekko and SciPy, reporting statistically better best solutions with significantly shorter computation times. The package is intended to be freely available via pip install, enabling easy adoption for practical engineering and optimization tasks. Overall, the work delivers a specialized tool that improves solution quality and speed for polynomial-based boxed optimization problems, with accessible software deployment.
Abstract
In this paper, a new python package (optipoly) is described that solves box-constrained optimization problem over multivariate polynomial cost functions. The principle of the algorithm is described before its performance is compared to three general purpose NLP solvers implemented in the state-of-the-art Gekko and scipy packages. The comparison show statistically better best solution provided by the algorithm with significantly less computation times. The package will be shortly made freely and easily available through the simple (pip install) process.