Lectures on Stochastic Programming : Modeling and Theory

Alexander Shapiro

ISBN 10: 1611976588 / ISBN 13: 9781611976588

Verlag: Society For Industrial & Applied Mathematics,U.S. Dez 2021, 2021

Beschreibung

Neuware.

Bestandsnummer des Verkäufers 9781611976588

Bibliografische Details

• Titel: Lectures on Stochastic Programming : Modeling and Theory
• Verlag: Society For Industrial & Applied Mathematics,U.S. Dez 2021
• Erscheinungsjahr: 2021
• Sprache: Englisch
• ISBN-10: 1611976588
• ISBN-13: 9781611976588
• Einband: Buch
• Auflage: 3. Auflage
• Zustand: Neu

Über diesen Titel

Inhaltsangabe

Lectures on Stochastic Programming: Modeling and Theory, Third Edition covers optimization problems involving uncertain parameters for which stochastic models are available. These problems occur in almost all areas of science and engineering.

This substantial revision of the previous edition presents a modern theory of stochastic programming, including expanded coverage of sample complexity, risk measures, and distributionally robust optimization:

• Chapter 6 is updated and the interchangeability principle for risk measures is discussed in detail.
• Two new chapters, 'Distributionally Robust Stochastic Programming' (DRSP) and 'Computational Methods' provide readers with a solid understanding of emerging topics.
• Chapter 8 presents new material on formulation and numerical approaches to solving periodical multistage stochastic programs.

This book is written for researchers and graduate students working on theory and applications of optimization.

Über die Autorin bzw. den Autor

Alexander Shapiro is Professor in the School of Industrial and Systems Engineering at Georgia Institute of Technology. He was Editor-in-Chief of the Mathematical Programming, Series A (2012-2017). He has given numerous invited keynote and plenary talks, including invited section talk (section Control Theory and Optimization) at the International Congress of Mathematicians in 2010. In 2013, he was a recipient of the Khachiyan prize awarded by the INFORMS Optimization Society, and in 2018, he was a recipient of the Dantzig Prize awarded by the Mathematical Optimization Society and SIAM. He was elected to the National Academy of Engineering in 2020.

Darinka Dentcheva has contributed substantially to the theory and methods of optimization under uncertainty and risk in numerous papers and presentations at prominent international events. She is passionate about education and has developed new graduate curricula and courses. Dr. Dentcheva has served the community as an Associate Editor of SIAM Journal on Optimization, SIAM Review, and the Journal on Control, Optimisation and Calculus of Variations of the French Society of Applied Mathematics (ESAIM) among others. She is the recipient of a DAAD award, Davis Memorial Research Award, and Research Recognition Award of the board of trustees of Stevens Institute of Technology.

Andrzej Ruszczy?ski has worked at Warsaw University of Technology, University of Zurich, International Institute of Applied Systems Analysis, Princeton University, University of Wisconsin-Madison, and Rutgers University. He is one of the creators of and main contributors to the field of risk-averse optimization, author of Nonlinear Optimization (Princeton University Press, 2006), co-author of Stochastic Programming (Elsevier, 2003), and author of more than 100 articles in the area of optimization. He is the recipient of the 2018 Dantzig Prize of SIAM and the Mathematical Optimization Society, and an INFORMS fellow.