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

Conejo, Antonio J. J.; Castillo, Enrique; Minguez, Roberto; Garcia-Bertrand, Raquel

Softcover

ISBN 10: 3642066070 ISBN 13: 9783642066078

Verlag: Springer, 2010

Inhaltsangabe

Optimization plainly dominates the design, planning, operation, and c- trol of engineering systems. This is a book on optimization that considers particular cases of optimization problems, those with a decomposable str- ture that can be advantageously exploited. Those decomposable optimization problems are ubiquitous in engineering and science applications. The book considers problems with both complicating constraints and complicating va- ables, and analyzes linear and nonlinear problems, with and without in- ger variables. The decomposition techniques analyzed include Dantzig-Wolfe, Benders, Lagrangian relaxation, Augmented Lagrangian decomposition, and others. Heuristic techniques are also considered. Additionally, a comprehensive sensitivity analysis for characterizing the solution of optimization problems is carried out. This material is particularly novel and of high practical interest. This book is built based on many clarifying, illustrative, and compu- tional examples, which facilitate the learning procedure. For the sake of cl- ity, theoretical concepts and computational algorithms are assembled based on these examples. The results are simplicity, clarity, and easy-learning. We feel that this book is needed by the engineering community that has to tackle complex optimization problems, particularly by practitioners and researchersinEngineering,OperationsResearch,andAppliedEconomics.The descriptions of most decomposition techniques are available only in complex and specialized mathematical journals, di?cult to understand by engineers. A book describing a wide range of decomposition techniques, emphasizing problem-solving, and appropriately blending theory and application, was not previously available.

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Über die Autorin bzw. den Autor

Juan M. Morales received his M.Sc. degree in Industrial Engineering from the University of Málaga and his Ph.D. in Electrical Engineering from the University of Castilla - La Mancha, Spain. Since 2013 he is an associate professor in Stochastic Optimization in Energy Systems in the Department of Applied Mathematics and Computer Science at the Technical University of Denmark. His research interests include mathematical programming and techniques of optimization under uncertainty, decision making, hierarchical optimization, renewable energies and energy economics. Antonio J. Conejo received the M.S. degree from Massachusetts Institute of Technology, Cambridge, MA, in 1987 and the Ph.D. degree from the Royal Institute of Technology, Stockholm, Sweden, in 1990. He is currently Professor of Electrical Engineering at the Universidad de Castilla - La Mancha, Ciudad Real, Spain. Henrik Madsen received his M.Sc. (1982) and PhD (1986) in Statistics from the Technical University of Denmark (DTU). His research interests include forecasting of wind and solar power, time series analysis, and estimation of parameters in stochastic differential equations for physical modeling. Since 1999 he has been a full professor in Stochastic Dynamical Systems. Pierre Pinson received his M.Sc. in Applied Mathematics from the National Institute of Applied Sciences, Toulouse, and his Ph.D. in Energetics from the Ecole des Mines de Paris. He is the Professor in Modelling of Electricity Markets at the Technical University of Denmark, Dpt. of Electrical Engineering. His research interests include statistical modelling; forecasting; stochastic optimization; decision making under uncertainty; renewable energies; meteorology; energy management; and energy trading. Marco Zugno received the M.Sc. degree in Electrical Engineering from the Technical University of Denmark (DTU) and the M.Sc. degree in Automation Engineering from the University of Padua, Italy. He holdsa Ph.D. degree obtained from the department of Applied Mathematics and Computer Science at DTU. He is currently a postdoctoral researcher with the same department. His research interests include electricity market modeling; stochastic programming; robust optimization; and hierarchical optimization.

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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.

Details zur Ausgabe

• Verlag: Springer
• Erscheinungsdatum: 2010
• Sprache: Englisch
• ISBN 10: 3642066070
• ISBN 13: 9783642066078
• Einband: Taschenbuch
• Anzahl der Seiten: 560
• Kontakt zum Hersteller: GPSR Kontakt; ProductSafety@springernature.com; ; Tiergartenstr. 17; Heidelberg; 69121; Deutschland