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 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.
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 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 Marius Durea, Radu Strugariu - De Gruyter Open
Starting with the case of differentiable data and the classical results on constrained optimization problems, continuing with the topic of nonsmooth objects involved in optimization, the book concentrates on both theoretical and practical aspects.