Advances in Optimization and Linear Programming

Linear programming (LP) is one of the simplest ways to perform optimization. It helps you solve some very complex optimization problems by making a few simplifying assumptions. Decision-making problems may be classified into two categories: deterministic and probabilistic decision models. In deterministic models, good decisions bring about good outcomes. This depends largely on how influential the uncontrollable factors are in determining the outcome of a decision, and how much information the decision-maker has in predicting these factors. This book provides the information needed to understand the simplex method, dual simplex method, and more for solving linear programming problems. 

Print ISBN: 9781682519219 | $165 | 2023 | Hardcover

Subject: Mathematics and Statistics

Editor:  Lance Bunt

About the Editor: Lance Bunt, Ph.D., is a Professor of Statistics and Dean of the School of Mathematics and Statistics. His research background is in optimization algorithms, intelligent manufacturing systems, high-performance computing, and chaos systems. He has edited three books and numerous journal papers, book chapters, and conference publications in the field of computational intelligence.