Computer Science & Information Management

Advances in Optimization and Linear Programming
Ivan Stanimirovic, PhD

Advances in Optimization and Linear Programming

Published. Available now.
Pub Date: January 2022
Hardback Price: see ordering info
Hard ISBN: 9781774637401
E-Book ISBN: 9781003256052
Pages: 204pp w/index
Binding Type: Hardback

This new volume provides the information needed to understand the simplex method, the revised simplex method, dual simplex method, and more for solving linear programming problems.

Following a logical order, the book first gives a mathematical model of the linear problem programming and describes the usual assumptions under which the problem is solved. It gives a brief description of classic algorithms for solving linear programming problems as well as some theoretical results. It goes on to explain the definitions and solutions of linear programming problems, outlining the simplest geometric methods, and showing how they can be implemented. Practical examples are included along the way. The book concludes with a discussion of multi-criteria decision-making methods.

This volume is a highly useful guide to linear programming for professors and students in optimization and linear programming.

CONTENTS:

Preface

1. INTRODUCTION
1.1 Multiobjective Optimization
1.2 Symbolic Transformations in Multi-Sector Optimization
1.3. Pareto Optimality Test
1.4 The Method of Weight Coefficients
1.5 Mathematical Model
1.6 Properties of a Set of Constraints
1.7 Geometrical Method

2. SIMPLEX METHODS
2.1 Properties of Simplex Methods
2.2 The Algebraic Essence of the Simplex Method
2.3 The Term Tucker


About the Authors / Editors:
Ivan Stanimirovic, PhD
Associate Professor, Department of Computer Science, Faculty of Sciences and Mathematics, University of Niš, Serbia

Ivan Stanimirovic, PhD, is currently Associate Professor at the Department of Computer Science, Faculty of Sciences and Mathematics at the University of Nis, Serbia. He formerly was with the Faculty of Management at Megatrend University, Belgrade, as a Lecturer. His work spans from multi-objective optimization methods to applications of generalized matrix inverses in areas such as image processing and restoration and computer graphics. His current research interests include computing generalized matrix inverses and their applications, applied multi-objective optimization and decision-making, as well as deep learning neural networks. Dr. Stanimirovic was the chairman of a workshop held at the 13th Serbian Mathematical Congress, Vrnjaeka Banja, Serbia, in 2014.




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