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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.

Mathematics. --- Computer science --- Algorithms. --- Computer software. --- Mathematical optimization. --- Continuous Optimization. --- Mathematical Software. --- Math Applications in Computer Science. --- Linear programming. --- Computer mathematics --- Discrete mathematics --- Electronic data processing --- Math --- Science --- Mathematics --- Production scheduling --- Programming (Mathematics) --- Computer science. --- Algorism --- Algebra --- Arithmetic --- Informatics --- Software, Computer --- Computer systems --- Foundations --- Programación lineal --- Computer science—Mathematics. --- Optimization (Mathematics) --- Optimization techniques --- Optimization theory --- Systems optimization --- Mathematical analysis --- Maxima and minima --- Operations research --- Simulation methods --- System analysis --- Mathematical Applications in Computer Science.