Algorithms and Data Structures
Volume 3608 of the series Lecture Notes in Computer Science pp 396-408
Derandomization of Dimensionality Reduction and SDP Based Algorithms
- Ankur BhargavaAffiliated withDept. of Computer Science, Johns Hopkins University
- , S. Rao KosarajuAffiliated withDept. of Computer Science, Johns Hopkins University
Abstract
We present two results on derandomization of randomized algorithms. The first result is a derandomization of the Johnson-Lindenstrauss (JL) lemma based randomized dimensionality reduction algorithm. Our algorithm is simpler and faster than known algorithms. It is based on deriving a pessimistic estimator of the probability of failure. The second result is a general technique for derandomizing semidefinite programming (SDP) based approximation algorithms. We apply this technique to the randomized algorithm for Max Cut. Once again the algorithm is faster than known deterministic algorithms for the same approximation ratio. For this problem we present a technique to approximate probabilities with bounded error.
- Title
- Derandomization of Dimensionality Reduction and SDP Based Algorithms
- Book Title
- Algorithms and Data Structures
- Book Subtitle
- 9th International Workshop, WADS 2005, Waterloo, Canada, August 15-17, 2005. Proceedings
- Pages
- pp 396-408
- Copyright
- 2005
- DOI
- 10.1007/11534273_35
- Print ISBN
- 978-3-540-28101-6
- Online ISBN
- 978-3-540-31711-1
- Series Title
- Lecture Notes in Computer Science
- Series Volume
- 3608
- Series ISSN
- 0302-9743
- Publisher
- Springer Berlin Heidelberg
- Copyright Holder
- Springer-Verlag Berlin Heidelberg
- Additional Links
- Topics
- Industry Sectors
- eBook Packages
- Editors
-
- Frank Dehne (16)
- Alejandro López-Ortiz (17)
- Jörg-Rüdiger Sack (18)
- Editor Affiliations
-
- 16. Carleton University
- 17. Cheriton School of Computer Science, University of Waterloo
- 18. School of Computer Science, Carleton University
- Authors
-
- Ankur Bhargava (19)
- S. Rao Kosaraju (19)
- Author Affiliations
-
- 19. Dept. of Computer Science, Johns Hopkins University, Baltimore, MD, 21218
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