by Jim Burke
Publisher: University of Washington 2012
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 since it offers a complete framework for discussing both the geometry and duality theory for linear programs.
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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 D. P. Williamson, D. B. Shmoys - Cambridge University Press
This book shows how to design approximation algorithms: efficient algorithms that find provably near-optimal solutions. It is organized around techniques for designing approximation algorithms, including greedy and local search algorithms.
by Ozgur Baskan (ed.) - InTech
This book covers state-of-the-art optimization methods and their applications in wide range especially for researchers and practitioners who wish to improve their knowledge in this field. It covers applications in engineering and various other areas.
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.