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 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.
by Guido Schaefer - Utrecht University
From the table of contents: Preliminaries (Optimization Problems); Minimum Spanning Trees; Matroids; Shortest Paths; Maximum Flows; Minimum Cost Flows; Matchings; Integrality of Polyhedra; Complexity Theory; Approximation Algorithms.
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.