Chapter

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

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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.