License: Creative Commons Attribution 3.0 Unported license (CC BY 3.0)
When quoting this document, please refer to the following
DOI: 10.4230/LIPIcs.ICALP.2020.56
URN: urn:nbn:de:0030-drops-124632
URL: http://dagstuhl.sunsite.rwth-aachen.de/volltexte/2020/12463/
Go to the corresponding LIPIcs Volume Portal

Garg, Paritosh ; Kale, Sagar ; Rohwedder, Lars ; Svensson, Ola

Robust Algorithms Under Adversarial Injections

pdf-format:
LIPIcs-ICALP-2020-56.pdf (0.6 MB)

Abstract

In this paper, we study streaming and online algorithms in the context of randomness in the input. For several problems, a random order of the input sequence - as opposed to the worst-case order - appears to be a necessary evil in order to prove satisfying guarantees. However, algorithmic techniques that work under this assumption tend to be vulnerable to even small changes in the distribution. For this reason, we propose a new adversarial injections model, in which the input is ordered randomly, but an adversary may inject misleading elements at arbitrary positions. We believe that studying algorithms under this much weaker assumption can lead to new insights and, in particular, more robust algorithms. We investigate two classical combinatorial-optimization problems in this model: Maximum matching and cardinality constrained monotone submodular function maximization. Our main technical contribution is a novel streaming algorithm for the latter that computes a 0.55-approximation. While the algorithm itself is clean and simple, an involved analysis shows that it emulates a subdivision of the input stream which can be used to greatly limit the power of the adversary.

BibTeX - Entry

@InProceedings{garg_et_al:LIPIcs:2020:12463,
  author =	{Paritosh Garg and Sagar Kale and Lars Rohwedder and Ola Svensson},
  title =	{{Robust Algorithms Under Adversarial Injections}},
  booktitle =	{47th International Colloquium on Automata, Languages, and Programming (ICALP 2020)},
  pages =	{56:1--56:15},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-138-2},
  ISSN =	{1868-8969},
  year =	{2020},
  volume =	{168},
  editor =	{Artur Czumaj and Anuj Dawar and Emanuela Merelli},
  publisher =	{Schloss Dagstuhl--Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/opus/volltexte/2020/12463},
  URN =		{urn:nbn:de:0030-drops-124632},
  doi =		{10.4230/LIPIcs.ICALP.2020.56},
  annote =	{Keywords: Streaming algorithm, adversary, submodular maximization, matching}
}

Keywords: Streaming algorithm, adversary, submodular maximization, matching
Collection: 47th International Colloquium on Automata, Languages, and Programming (ICALP 2020)
Issue Date: 2020
Date of publication: 29.06.2020

DROPS-Home | Fulltext Search | Imprint | Privacy Published by LZI