Improved algorithms via approximations of probability distributions (extended abstract)

@inproceedings{Chari1994ImprovedAV,
  title={Improved algorithms via approximations of probability distributions (extended abstract)},
  author={Suresh Chari and Pankaj Rohatgi and Aravind Srinivasan},
  booktitle={STOC},
  year={1994}
}
We present two techniques for approximating probability distributions. The first is a simple method for constructing the small-bias probability spaces introduced in [21]. This construction can be efficiently combined with the method of conditional probabilities to yield improved NC algorithms for many problems such as set cover, set discrepancy, finding large cuts in graphs etc. The second is a construction of small probability spaces approximating general independent distributions, which is of… CONTINUE READING

From This Paper

Topics from this paper.

References

Publications referenced by this paper.
Showing 1-6 of 6 references

Removing Randomness in Parallel Computation without a Processor Penalty

J. Comput. Syst. Sci. • 1993
View 9 Excerpts
Highly Influenced

J

N. Alon, O. Goldreich
H&stad, and R. Peralta. Simple constructions of almost k–wise independent random variables. Random Structures and Algorithms, 3(3):289–303 • 1992
View 9 Excerpts
Highly Influenced

and B

G. Even, O. Goldreich, M. Luby, N. Nisan
Velitkovi6. Approximations of general independent distributions. In Proc. ACM Symposium on Theory of Computing, pages 10-16 • 1992
View 10 Excerpts
Highly Influenced

Simulating (log c n)-Wise Independence in NC

J. ACM • 1991
View 9 Excerpts
Highly Influenced

Matou6ek. On linear-time deterministic algorithms for optimization problems in fixed dimension

J. B. Chazelle
In Proc. A CM\SIAM Symposium on Discrete Algorithms, • 1993
View 3 Excerpts
Highly Influenced

Similar Papers

Loading similar papers…