Python project for analyzing simplex method complexity on LPs in canonical form. Tracks FLOPs, generates problems, fits models, visualizes with Matplotlib, and caches results. Builds on work by Pavlov, Kyselov, et al. Uses Nelder-Mead optimization and MSE loss to fit empirical data to theoretical models like Borgwardt, Smoothed, and Polynomial.