Decomposition Techniques in Mathematical Programming: Engineering and Science Applications - Softcover

Über die Autorin bzw. den Autor

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.

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This textbook for students and practitioners presents a practical approach to decomposition techniques in optimization. It provides an appropriate blend of theoretical background and practical applications in engineering and science, which makes the book interesting for practitioners, as well as engineering, operations research and applied economics graduate and postgraduate students. "Decomposition Techniques in Mathematical Programming" is based on clarifying, illustrative and computational examples and applications from electrical, mechanical, energy and civil engineering as well as applied mathematics and economics. It addresses decomposition in linear programming, mixed-integer linear programming, nonlinear programming, and mixed-integer nonlinear programming, and provides rigorous decomposition algorithms as well as heuristic ones. Practical applications are developed up to working algorithms that can be readily used. The theoretical background of the book is deep enough to be of interest to applied mathematicians. It includes end of chapter exercises and the solutions to the even numbered exercises are included as an appendix.