Pushing the online matrix-vector conjecture off-line and identifying its easy cases
- Leszek Gąsieniec,
- Jesper Jansson,
- Christos Levcopoulos,
- Andrzej Lingas(corresponding author),
- Mia Persson
- University of Liverpool,
- Hong Kong Polytechnic University,
- Lund University,
- Malmö University
Scholary Output:
Chapter in Book/Report/Conference proceeding
Conference contribution
Related Event
Title
13th International Workshop on Frontiers in Algorithmics, FAW 2019
Event type
ConferenceDate
04/29/2019 - 05/03/2019Location
SanyaChina
Abstract
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.
Publication Information
Output type
Scholary Output:
Chapter in Book/Report/Conference proceeding
Conference contribution
Original language
English (US)Pages from-to (Number of pages)
Pages 156-169 (14 pages)Publication milestones
- Published - 2019
Publication status
Published - 2019
Publisher
Springer VerlagPublication series
- Publication series name: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
ISSN (Print): 0302-9743
ISSN (Electronic): 1611-3349
Volume: 11458 LNCS
ISBN (Print)
9783030181253Publication IDs
- Scopus: 85065315076
Host publication title
Frontiers in Algorithmics - 13th International Workshop, FAW 2019, ProceedingsHost publication editors
- Mei Lu
- Xiaotie Deng
- Yijia Chen
Publication metrics
Metrics
SciVal
Author count
5
SciVal
Paper percentile
33
Fractional count
1
Fractional count
0.20
Fractional count
4
Fractional count
0.80
Fractional count
1
Fractional count
1
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Captures
1
Funding Details
Acknowledgements. CL, JJ and MP were supported in part by Swedish Research Council grant 621-2017-03750.
FunderFunding number
VR
621-2017-03750
