Memory allocation in facet redundancy removal in dingo

Sampling and volume computation of convex polytopes is a challenging computational problem with many applications in inference from linear constraints [1]. Thus, sampling algorithms are essential in many scientific fields. Biology in general, and most particularly metabolic modeling, is among those scientific fields that need such approaches the most to address their modern challenges. Flux Balance Analysis (FBA) is a typical example of a constraint-based metabolic model (CBMM) [2]. However, flux sampling is considered a superior methodology. Python package dingo provides the Multiphase Monte Carlo Sampling (MMC) algorithm to sample from the flux space of a metabolic network. However, the preprocessing methods implemented in dingo need major improvements regarding memory allocation. The goal of this project is to improve the preprocessing methods in dingo to support faster sampling and rounding pipelines.