Linear Programming

Ding-Zhu Du (), Ker-I Ko () and Xiaodong Hu ()
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Ding-Zhu Du: University of Texas at Dallas
Ker-I Ko: State University of New York at Stony Brook
Xiaodong Hu: Academy of Mathematics and Systems Science Chinese Academy of Sciences

Chapter 7 in Design and Analysis of Approximation Algorithms, 2012, pp 245-296 from Springer

Abstract: Abstract A widely used relaxation technique for approximation algorithms is to convert an optimization problem into an integer linear program and then relax the constraints on the solutions allowing them to assume real, noninteger values. As the optimal solution to a (real-valued) linear program can be found in polynomial time, we can then solve the linear program and round the solutions to integers as the solutions for the original problem. In this chapter, we give a brief introduction to the theory of linear programming and discuss various rounding techniques.

Keywords: Integer Linear Program; Feasible Region; Simplex Method; Conjunctive Normal Form; Satisfying Assignment (search for similar items in EconPapers)
Date: 2012
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DOI: 10.1007/978-1-4614-1701-9_7

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