Simulation-based optimization. Parametric optimization techniques and reinforcement learning (Q2253773)

The goal of this book is to present the topics on simulation-based optimization techniques for large-scale and complex stochastic systems. The author discusses two approaches: parametric (static) optimization and control (dynamic) optimization. The book consists of 12 chapters, covering the following areas: introductionary topics, insights into simulation-based optimization, parametric optimization, control optimization, convergence, case studies. The book is self-contained, although some basic background in calculus and algebra is necessary. It is a valuable textbook for the students as well as a nice introduction for the researchers in the area.