Sampling Methods Applied to Dense Instances of Non-Boolean Optimization Problems

Authors:
Gunnar Andersson;Lars Engebretsen
Affiliations:
-;-
Venue:
RANDOM '98 Proceedings of the Second International Workshop on Randomization and Approximation Techniques in Computer Science
Year:
1998

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Abstract

We study dense instances of optimization problems with variables taking values in Zp. Specifically, we study systems of functions from Zpk to Zp where the objective is to make as many functions as possible attain the value zero. We generalize earlier sampling methods and thereby construct a randomized polynomial time approximation scheme for instances with Θ(nk) functions where n is the number of variables occurring in the functions.