Derandomization of dimensionality reduction and SDP based algorithms

Authors:
Ankur Bhargava;S. Rao Kosaraju
Affiliations:
Dept. of Computer Science, Johns Hopkins University, Baltimore, MD;Dept. of Computer Science, Johns Hopkins University, Baltimore, MD
Venue:
WADS'05 Proceedings of the 9th international conference on Algorithms and Data Structures
Year:
2005

Citing 19
Cited 0

Asymptotic theory of finite dimensional normed spaces

Asymptotic theory of finite dimensional normed spaces
Randomized rounding: a technique for provably good algorithms and algorithmic proofs

Combinatorica - Theory of Computing
The Johnson-Lindenstrauss Lemma and the sphericity of some graphs

Journal of Combinatorial Theory Series A
Probabilistic construction of deterministic algorithms: approximating packing integer programs

Journal of Computer and System Sciences - 27th IEEE Conference on Foundations of Computer Science October 27-29, 1986
The fourth moment method

SODA '91 Proceedings of the second annual ACM-SIAM symposium on Discrete algorithms
.879-approximation algorithms for MAX CUT and MAX 2SAT

STOC '94 Proceedings of the twenty-sixth annual ACM symposium on Theory of computing
Randomized algorithms

Randomized algorithms
Solving a Polynomial Equation: Some History and Recent Progress

SIAM Review
Approximate nearest neighbors: towards removing the curse of dimensionality

STOC '98 Proceedings of the thirtieth annual ACM symposium on Theory of computing
Approximating the independence number via the j -function

Mathematical Programming: Series A and B
Database-friendly random projections

PODS '01 Proceedings of the twentieth ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems
Algorithmic derandomization via complexity theory

STOC '02 Proceedings of the thiry-fourth annual ACM symposium on Theory of computing
Derandomized dimensionality reduction with applications

SODA '02 Proceedings of the thirteenth annual ACM-SIAM symposium on Discrete algorithms
An elementary proof of a theorem of Johnson and Lindenstrauss

Random Structures & Algorithms
Improved Approximation Algorithms for MAX k-CUT and MAX BISECTION

Proceedings of the 4th International IPCO Conference on Integer Programming and Combinatorial Optimization
Derandomizing semidefinite programming based approximation algorithms

FOCS '95 Proceedings of the 36th Annual Symposium on Foundations of Computer Science
An Algorithmic Theory of Learning: Robust Concepts and Random Projection

FOCS '99 Proceedings of the 40th Annual Symposium on Foundations of Computer Science
Approximate graph coloring by semidefinite programming

SFCS '94 Proceedings of the 35th Annual Symposium on Foundations of Computer Science
The geometry of graphs and some of its algorithmic applications

SFCS '94 Proceedings of the 35th Annual Symposium on Foundations of Computer Science

Quantified Score

Hi-index	0.00

Visualization

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.