Approximating the influence of monotone boolean functions in O(√n) query complexity

• Main Authors: Ron, Dana (Author), Rubinfeld, Ronitt (Contributor), Safra, Muli (Author), Weinstein, Omri (Author)
• Other Authors: Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory (Contributor), Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science (Contributor)
• Format: Article
• Language: English
• Published: Springer Berlin / Heidelberg, 2012-10-12T14:26:36Z.
• Subjects: Article
• Online Access: Get fulltext

Author Manuscript received 27 Jan 2011. 14th International Workshop, APPROX 2011, and 15th International Workshop, RANDOM 2011, Princeton, NJ, USA, August 17-19, 2011. Proceedings
The Total Influence (Average Sensitivity) of a discrete function is one of its fundamental measures. We study the problem of approximating the total influence of a monotone Boolean function f : {0, 1}[superscript n] → {0, 1}, which we denote by I[f]. We present a randomized algorithm that approximates the influence of such functions to within a multiplicative factor of (1 ± ) by performing O([√n log n[over]I[f]] poly(1/Є ))queries. We also prove a lower bound of Ω ([√ n [over] log n·I[f]])on the query complexity of any constant-factor approximation algorithm for this problem (which holds for I[f] = Ω(1)), hence showing that our algorithm is almost optimal in terms of its dependence on n. For general functions we give a lower bound of Ω ([n [over] I[f]]), which matches the complexity of a simple sampling algorithm.