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This book offers a theoretical and computational presentation of a variety of linear programming algorithms and methods with an emphasis on the revised simplex method and its components. A theoretical background and mathematical formulation is included for each algorithm as well as comprehensive numerical examples and corresponding MATLAB® code. The MATLAB® implementations presented in this book are sophisticated and allow users to find solutions to large-scale benchmark linear programs. Each algorithm is followed by a computational study on benchmark problems that analyze the computational behavior of the presented algorithms. As a solid companion to existing algorithmic-specific literature, this book will be useful to researchers, scientists, mathematical programmers, and students with a basic knowledge of linear algebra and calculus. The clear presentation enables the reader to understand and utilize all components of simplex-type methods, such as presolve techniques, scaling techniques, pivoting rules, basis update methods, and sensitivity analysis.
An implementation of the revised simplex algorithm in CUDA for solving linear optimization problems in the form max{c*x | A*x=b, l<=x<=u}
Computing the optimized values using Simplex method
Python code to solve any standard form LP using 2 Phase Revised Simplex Algorithm.
Prize collecting TSP with integer programming, branch & bound and revised simplex.
Built a linear Constraints Optimization Algorithm from Scratch that works on any type of linear optimization problem
Implementation of Simplex method in Python (My Assignment in Linear Optimization course [MTH305] [IIIT-Delhi]).