Klimm, Max ; Pfetsch, Marc E. ; Raber, Rico ; Skutella, Martin (2024)
Packing under convex quadratic constraints.
In: Mathematical Programming: Series A, Series B, 2022, 192 (1-2)
doi: 10.26083/tuprints-00023454
Article, Secondary publication, Publisher's Version
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Item Type: | Article |
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Type of entry: | Secondary publication |
Title: | Packing under convex quadratic constraints |
Language: | English |
Date: | 19 March 2024 |
Place of Publication: | Darmstadt |
Year of primary publication: | March 2022 |
Place of primary publication: | Berlin ; Heidelberg |
Publisher: | Springer |
Journal or Publication Title: | Mathematical Programming: Series A, Series B |
Volume of the journal: | 192 |
Issue Number: | 1-2 |
DOI: | 10.26083/tuprints-00023454 |
Corresponding Links: | |
Origin: | Secondary publication DeepGreen |
Abstract: | We consider a general class of binary packing problems with a convex quadratic knapsack constraint. We prove that these problems are APX-hard to approximate and present constant-factor approximation algorithms based upon two different algorithmic techniques: a rounding technique tailored to a convex relaxation in conjunction with a non-convex relaxation, and a greedy strategy. We further show that a combination of these techniques can be used to yield a monotone algorithm leading to a strategyproof mechanism for a game-theoretic variant of the problem. Finally, we present a computational study of the empirical approximation of these algorithms for problem instances arising in the context of real-world gas transport networks. |
Status: | Publisher's Version |
URN: | urn:nbn:de:tuda-tuprints-234544 |
Additional Information: | Mathematics Subject Classification: 90C27, 90C35, 68Q25 Special Issue: Integer Programming and Combinatorial Optimization (IPCO) 2020 |
Classification DDC: | 500 Science and mathematics > 510 Mathematics |
Divisions: | 04 Department of Mathematics > Optimization |
Date Deposited: | 19 Mar 2024 13:51 |
Last Modified: | 30 Apr 2024 07:09 |
SWORD Depositor: | Deep Green |
URI: | https://tuprints.ulb.tu-darmstadt.de/id/eprint/23454 |
PPN: | 517330091 |
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