by A. Ben-Tal, L. El Ghaoui, A. Nemirovski
Publisher: Princeton University Press 2009
Number of pages: 564
Robust optimization is still a relatively new approach to optimization problems affected by uncertainty, but it has already proved so useful in real applications that it is difficult to tackle such problems today without considering this powerful methodology. Written by the principal developers of robust optimization, and describing the main achievements of a decade of research, this is the first book to provide a comprehensive and up-to-date account of the subject.
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by Bram L. Gorissen, Ihsan Yanıkoğlu, Dick den Hertog - arXiv
The aim of this paper is to help practitioners to understand robust optimization and to successfully apply it in practice. We provide a brief introduction to robust optimization, and also describe important do's and don'ts for using it in practice.
by Katta G. Murty - Springer
This is a Junior level book on some versatile optimization models for decision making in common use. The aim of this book is to develop skills in mathematical modeling, and in algorithms and computational methods to solve and analyze these models.
by Jim Burke - University of Washington
These are notes for an introductory course in linear programming. The four basic components of the course are modeling, solution methodology, duality theory, and sensitivity analysis. We focus on the simplex algorithm due to George Dantzig.
by Stephen Boyd, Lieven Vandenberghe - Cambridge University Press
A comprehensive introduction to the subject for students and practitioners in engineering, computer science, mathematics, statistics, finance, etc. The book shows in detail how optimization problems can be solved numerically with great efficiency.