Dyer, M, Gärtner, B, Megiddo, N et al. (1 more author) (2017) Linear Programming. In: Handbook of Discrete and Computational Geometry, 3rd Edition. , pp. 1291-1309. ISBN 9781498711395
Abstract
Linear programming has many important practical applications, and has also given rise to a wide body of theory. See Section 49.9 for recommended sources. Here we consider the linear programming problem in the form of maximizing a linear function of d variables subject to n linear inequalities. We focus on the relationship of the problem to computational geometry, i.e., we consider the problem in small dimension. More precisely, we concentrate on the case where d « n $ d\ll n $, i.e., d = d (n) $ d = d(n) $ is a function that grows very slowly with n. By linear programming duality, this also includes the case n « d $ n \ll d $. This has been called fixed-dimensional linear programming, though our viewpoint here will not treat d as constant. In this case there are strongly polynomial algorithms, provided the rate of growth of d with n is small enough.
Metadata
Item Type: | Book Section |
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Authors/Creators: |
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Dates: |
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Institution: | The University of Leeds |
Academic Units: | The University of Leeds > Faculty of Engineering & Physical Sciences (Leeds) > School of Computing (Leeds) |
Depositing User: | Symplectic Publications |
Date Deposited: | 09 Aug 2019 10:16 |
Last Modified: | 12 Aug 2019 08:27 |
Status: | Published |
Identification Number: | 10.1201/9781315119601 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:149522 |