This repository contains code for generating and optimizing bivariate multimodal polynomials. The goal is to compare the efficacy and efficiency of different optimization algorithms in reaching a global minima in bivariate multimodal functions.
Bivariate multimodal polynomials are polynomial functions with two variables that have multiple local minima and maxima. Optimizing such functions is a challenging task due to the presence of multiple local optima and the tendency of optimization algorithms to get stuck in these. This projects aimes to compare the efficacy and efficiency of Gradient Decent and Nelder-Mead optimization at reaching a global optima. The code contained in this repository can generate a set of multimodal polynomials by fitting functions to matrices filled with perlin noise. Then, Gradient Decent or Nelder-Mead optimization can be applied to these functions and data about the optimization is collected.
To install the necessary dependencies, run the following command:
pip install -r requirements.txtThis project is licensed under the MIT License. See the LICENSE file for more details.