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 Bruce A. McCarl, Thomas H. Spreen - Texas A&M University
This book is intended to both serve as a reference guide and a text for a course on Applied Mathematical Programming. The text concentrates upon conceptual issues, problem formulation, computerized problem solution, and results interpretation.
by P.-A. Absil, R. Mahony, R. Sepulchre - Princeton University Press
Many science and engineering problems can be rephrased as optimization problems on matrix search spaces endowed with a manifold structure. This book shows how to exploit the structure of such problems to develop efficient numerical algorithms.
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 Thomas S. Ferguson - UCLA
From the table of contents: Stopping Rule Problems; Finite Horizon Problems; The Existence of Optimal Rules; Applications. Markov Models; Monotone Stopping Rule Problems; Maximizing the Rate of Return; Bandit Problems; Solutions to the Exercises.