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 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 Guido Schaefer - Utrecht University
From the table of contents: Preliminaries (Optimization Problems); Minimum Spanning Trees; Matroids; Shortest Paths; Maximum Flows; Minimum Cost Flows; Matchings; Integrality of Polyhedra; Complexity Theory; Approximation Algorithms.
by John Cea - Tata Institute of Fundamental Research
Contents: Differential Calculus in Normed Linear Spaces; Minimization of Functionals; Minimization Without Constraints; Minimization with Constraints; Duality and Its Applications; Elements of the Theory of Control and Elements of Optimal Design.