Learning-augmented algorithms for online subset sum

Chenyang Xu () and Guochuan Zhang ()
Additional contact information
Chenyang Xu: Zhejiang University
Guochuan Zhang: Zhejiang University

Journal of Global Optimization, 2023, vol. 87, issue 2, No 28, 989-1008

Abstract: Abstract As one of Karp’s 21 NP-complete problems, the subset sum problem, as well as its generalization, has been well studied. Among the rich literature, there is little work on the online version, where items arrive over list and irrevocable decisions on packing them or not must be made immediately. Under the online setting, no deterministic algorithms are competitive, while for randomized algorithms the best competitive ratio is 1/2. It is thus of great interest to improve the performance bounds for both deterministic and randomized algorithms, assuming predicted information is available in the learning-augmented model. Along this line, we revisit online subset sum by showing that, with learnable predictions, there exist learning-augmented algorithms to break through the worst-case bounds on competitive ratio. The theoretical results are also experimentally verified, where we come up with a new idea in designing experiments. Namely, we design neural networks to serve as adversaries, verifying the robustness of online algorithms. Under this framework, several networks are trained to select adversarial instances and the results show that our algorithms are competitive and robust.

Keywords: Competitive analysis; Subset sum; Learning-augmented algorithms; Untrusted predictions; 68W01 (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
http://link.springer.com/10.1007/s10898-022-01156-w Abstract (text/html)
Access to the full text of the articles in this series is restricted.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:jglopt:v:87:y:2023:i:2:d:10.1007_s10898-022-01156-w

Ordering information: This journal article can be ordered from
http://www.springer. ... search/journal/10898

DOI: 10.1007/s10898-022-01156-w

Access Statistics for this article

Journal of Global Optimization is currently edited by Sergiy Butenko

More articles in Journal of Global Optimization from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().