Pushing the online matrix-vector conjecture off-line and identifying its easy cases

Leszek Ga̧sieniec, Jesper Andreas Jansson, Christos Levcopoulos, Andrzej Lingas, Mia Persson

Research output: Chapter in book / Conference proceedingConference article published in proceeding or bookAcademic researchpeer-review


Henzinger et al. posed the so called Online Boolean Matrix-vector Multiplication (OMv) conjecture and showed that it implies tight hardness results for several basic partially dynamic or dynamic problems [STOC’15]. We show that the OMv conjecture is implied by a simple off-line conjecture. If a not uniform (i.e., it might be different for different matrices) polynomial-time preprocessing of the matrix in the OMv conjecture is allowed then we can show such a variant of the OMv conjecture to be equivalent to our off-line conjecture. On the other hand, we show that the OMV conjecture does not hold in the restricted cases when the rows of the matrix or the input vectors are clustered.

Original languageEnglish
Title of host publicationFrontiers in Algorithmics - 13th International Workshop, FAW 2019, Proceedings
EditorsYijia Chen, Xiaotie Deng, Mei Lu
Number of pages14
ISBN (Print)9783030181253
Publication statusPublished - 1 Jan 2019
Event13th International Workshop on Frontiers in Algorithmics, FAW 2019 - Sanya, China
Duration: 29 Apr 20193 May 2019

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume11458 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Conference13th International Workshop on Frontiers in Algorithmics, FAW 2019

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

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