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 K.J.H. Law, A.M. Stuart, K.C. Zygalakis - arXiv.org
This book provides a systematic treatment of the mathematical underpinnings of work in data assimilation. Authors develop a framework in which a Bayesian formulation of the problem provides the bedrock for the derivation and analysis of algorithms.
by S. Bradley, A. Hax, T. Magnanti - Addison-Wesley
This book shows you how to model a wide array of problems. Covered are topics such as linear programming, duality theory, sensitivity analysis, network/dynamic programming, integer programming, non-linear programming, and my favorite, etc.
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The author presents the main concepts mathematical programming and optimal control to students having diverse technical backgrounds. A reasonable knowledge of advanced calculus, linear algebra, and linear differential equations is required.
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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.