Approximating the Influence of Monotone Boolean Functions in O(√n) Query Complexity
Dana Ron, Ronitt Rubinfeld, Muli Safra, Alex Samorodnitsky, Omri Weinstein · ACM Transactions on Computation Theory · 2012
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, 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 ± ε ) by performing O (√ n I [ f ] poly(1/ ε )) queries. We also prove a lower bound of Ω (√ n 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 I [ f ]), which matches the complexity of a simple sampling algorithm.