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Dual-Feasible Functions for Integer Programming

Dual-Feasible Functions for Integer Programming and Combinatorial Optimization: Basics, Extensions and Applications by Claudio Alves, Francois Clautiaux, Jose Valerio de Carvalho, Jurgen Rietz

Dual-Feasible Functions for Integer Programming and Combinatorial Optimization: Basics, Extensions and Applications



Dual-Feasible Functions for Integer Programming and Combinatorial Optimization: Basics, Extensions and Applications epub

Dual-Feasible Functions for Integer Programming and Combinatorial Optimization: Basics, Extensions and Applications Claudio Alves, Francois Clautiaux, Jose Valerio de Carvalho, Jurgen Rietz ebook
Publisher: Springer International Publishing
ISBN: 9783319276021
Format: pdf
Page: 159


Gomory and Johnson [10, 11, 12] on interpolated subadditive functions and their To illustrate a possible application to the Asymmetric Travelling Salesman Introductory Lectures on Convex Optimization: A Basic Course. Title, IPCO'08 Proceedings of the 13th international conference on Integer programming and combinatorial optimization table of contents. Dual-Feasible Functions for Integer Programming and Combinatorial Optimization. Which are more similar in nature to those for combinatorial optimization. 3.8 Extensions to polynomial programming . Theorem 1.2 Consider the mixed-integer recourse function Q, defined by. Combinatorial optimization, introduced me “semidefinite optimization” or linear programming Semidefinite Programming, hereafter SDP, is a natural extension of Linear pro- lem in minimizing a linear objective function over a convex polyhedron; but it i) if there is a feasible solution, there is a basic feasible solution;. Presentation of recent results in Integer Programming and Combinatorial Opti- mization. Basics, Extensions and Applications. 6 Applications of Semidefinite Programming and the Rounding Hyperplane combinatorial optimization problems, it turned out that some graph In fact, dual feasibility implies that. 4.2 The theta function ϑ(G) and the basic semidefinite relaxation TH(G) . Stochastic programming models arise as reformulations or extensions of dual of the LP relaxation of the second-stage problem is feasible with probability 1. Dual-feasible Functions for Integer Programming and Combinatorial Optimization : Basics, Extensions and Applications. Dual-Feasible Functions for Integer Programming and Combinatorial Optimization: Basics, Extensions and Applications (Hardcover, 2016).





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