Computational Optimization targets graduate seniors and master's level students in chemical engineering and relevant majors. This book introduces modern computational optimization (or mathematical programming) theory and algorithms, optimization modeling techniques, and various optimization applications in chemical engineering and energy systems. A unique feature of this book is that it has a good balance between theory, computation and applications.
Table of Contents
1. Introduction and optimization basics2. Linear programming (LP)
3. Mixed-integer linear programming (MILP)
4. Nonlinear programming (NLP) and dynamic optimization (DO)
5. Mixed-integer nonlinear programming (MINLP) and deterministic global optimization
6. Stochastic programming
7. Robust optimization
8. Optimization and big data analytics
9. Computation