Applied Mathematical Programming
by S. Bradley, A. Hax, T. Magnanti
Publisher: Addison-Wesley 1977
Number of pages: 716
This book shows you how to model a wide array of problems, and explains the mathematical algorithms and techniques behind the modeling. Covered are topics such as linear programming, duality theory, sensitivity analysis, network/dynamic programming, integer programming, non-linear programming, and my favorite, large-scale problems modeling/solving, etc.
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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.
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 U. Helmke, J. B. Moore - Springer
Aimed at mathematics and engineering graduate students and researchers in the areas of optimization, dynamical systems, control systems, signal processing, and linear algebra. The problems solved are those of linear algebra and linear systems theory.
by Ian Craw - University of Aberdeen
The book describes the simplex algorithm and shows how it can be used to solve real problems. It shows how previous results in linear algebra give a framework for understanding the simplex algorithm and describes other optimization algorithms.