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This book offers an up-to-date description of relaxation/approximation and decomposition techniques, demonstrating how their combined use efficiently solves large-scale optimization problems relevant to engineering, particularly in electrical, and industrial engineering, with a focus on energy. Specifically, it presents linear and nonlinear relaxations and approximations that are relevant to optimization problems, introduces complicating constraints and complicating variables decomposition techniques that can take advantage of relaxations and approximations, and examines their applications in the engineering field.
Written in an accessible engineering language and filled with numerous illustrative examples and end-of-chapter exercises for all chapters, this book is a valuable resource for advanced undergraduate and graduate students, researchers, and practitioners in power engineering and industrial engineering. Moreover, business students with a keen interest in decision-making problems will also benefit greatly from its practical insights.
Gonzalo E. Constante Flores is a Postdoctoral Scholar at Purdue University, USA. He received his M.S. and Ph.D. degrees from The Ohio State University, USA. His research interests include modeling, optimization, simulation, and the economics of power and energy systems, focusing on developing physics-based and data-driven tools for modern power systems. He has published 23 papers in Web of Science journals and was the recipient of a Fulbright Scholarship.
Antonio J. Conejo, a professor at The Ohio State University, Ohio, received his M.S. from MIT, and his Ph.D. from the Royal Institute of Technology, Sweden. He has published over 270 papers in Web of Science journals and is the author or coauthor of 14 books published by Springer, John Wiley, McGraw-Hill and CRC. He has been the principal investigator of many research projects financed by public agencies and the power industry and has supervised 27 PhD theses. He is a member of the National Academy of Engineering, an IEEE Fellow, an INFORMS Fellow, an AAAS Fellow, and a former Editor-in-Chief of the IEEE Transactions on Power Systems.
Relaxation and Decomposition.- Simplifying via Reformulation, Approximation, and Relaxation.- Approximating and Relaxing Optimization Problems.- Learning-Assisted Relaxations and Approximations.- Solving Optimization Problems with Complicating Variables.- Solving Optimization Problems via Lagrangian Decomposition.- Relaxations and Decomposition in Power Systems Operations.
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