This textbook presents an extensive exploration of modern and classical optimization methods designed for students, educators, and practitioners across engineering, computer science, economics, and applied mathematics. The text is organized to build foundational understanding before progressing to advanced concepts, ensuring a clear and structured learning pathway.
The book begins with a structured introduction to optimization, covering fundamental concepts, historical background, and problem formulation, followed by a MATLAB section that builds essential skills in scripting, visualization, and documentation. It presents key mathematical preliminaries—linear algebra, calculus, Taylor series, and optimization basics—before moving into analytical and numerical solutions of algebraic equations. Major optimization areas are explored in detail, including unconstrained optimization, linear and quadratic programming with classical methods and MATLAB implementations, nonlinear programming with feasible regions and graphical techniques, and mixed‑integer programming using enumeration and branch‑and‑bound approaches. The text further covers multi‑objective optimization through Pareto optimality and conversion techniques, dynamic programming and shortest‑path algorithms for sequential decision processes, and concludes with intelligent optimization methods such as genetic algorithms, particle swarm optimization, and MATLAB’s Global Optimization Toolbox, offering a comprehensive blend of theoretical foundations and practical computational applications.
Each chapter balances theoretical exposition, mathematical formulation, manual problem‑solving methods, and detailed computational examples. By bridging theory with hands‑on practice, this textbook serves as a comprehensive and versatile resource, making it indispensable for learners and professionals seeking strong grounding and practical fluency in optimization.
Table of Contents:
1. Introduction to Optimization.- 2. Introducing MATLAB and the MATLAB Working Environment.- 3. Mathematical Preliminaries for Optimization.- 4. Solutions of Algebraic Equations.- 5. Unconstrained Optimization Problems.- 6. Linear and Quadratic Programming.- 7. Nonlinear Programming.- 8. Mixed Integer Programming.- 9. Multi-objective Optimization.- 10. Dynamic Programming and Shortest Paths.- 11. Intelligent Optimization Methods.
About the Author :
Rupesh Kumar Tipu serves as an Assistant Professor at K.R. Mangalam University and brings a diverse blend of academic and industry experience to his role. With over ten years of teaching experience and four years in structural design practice, he demonstrates strong expertise across both theoretical and applied aspects of structural engineering. His interdisciplinary research interests span structural engineering, machine learning, deep learning, and web programming using Python and JavaScript--an uncommon but valuable combination that positions him well for modern, computationally driven engineering problems. He has authored numerous research papers in international journals and conferences and contributed several books reflecting a strong commitment to scholarly dissemination and academic development. Kartik S. Pandya, a Senior Member of IEEE, is an Professor in the Department of Electrical Engineering at Parul Institute of Engineering and Technology (PIET), Parul University, Vadodara, India. His research areas encompass optimization, computational intelligence methods, power systems, smart grids, and renewable integrations--key thrust areas in modern power engineering. With over 70 publications in reputed international journals and conferences, Dr. Pandya maintains a significant academic footprint and demonstrates sustained research activity in evolving and impactful domains. His industry-relevant research expertise and scholarly output reinforce his standing in the electrical engineering community.
