The leading approach to the simplex method, a widely used technique for balancing complex logistical constraints, can’t get ...

Introduction: In unsupervised learning, data clustering is essential. However, many current algorithms have issues like early convergence, inadequate local search capabilities, and trouble processing ...

Standard computer implementations of Dantzig's simplex method for linear programming are based upon forming the inverse of the basic matrix and updating the inverse ...

Yann LeCun’s argues that there are limitations of chain-of-thought (CoT) prompting and large language model (LLM) reasoning. LeCun argues that these fundamental limitations will require an entirely ...

This repository contains a collection of prototype linear programming (LP) applications that implement specific algorithms I learned in college. The purpose is to demonstrate that these mathematical ...

Introduction: This study focuses on broadening the applicability of the metaheuristic L1-norm fitted and penalized (L1L1) optimization method in finding a current pattern for multichannel transcranial ...

Solve linear optimization problems including minimization and maximization with simplex algorithm. Uses the Big M method to solve problems with larger equal constraints in Python ...

Abstract: This study proposes a novel technique for solving linear programming problems in a fully fuzzy environment. A modified version of the well-known dual simplex method is used for solving fuzzy ...