Abstract :
We give a new and improved proof that the shrinkage exponent of De Morgan formulae is 2. Namely, we show that for any Boolean function f : {0, 1}n → {0, 1}, setting each variable out of x1,.. . , xn with probability 1 - p to a randomly chosen constant, reduces the expected formula size of the function by a factor of O(p2). This result is tight and improves the work of Hastad [SIAM J. C., 1998] by removing logarithmic factors. As a consequence of our results, the function defined by Andreev [MUMB., 1987], A : {0, 1}n → {0, 1}, which is in P, has formula size at least Ω(n3/log2 n log3 log n). This lower bound is tight (for the function A) up to the log3 log n factor, and is the best known lower bound for functions in P. In addition, we strengthen the average-case hardness result of Komargodski et al.; we show that the functions defined by Komargodski et al., hr : {0, 1}n → {0, 1}, which are also in P, cannot be computed correctly on a fraction greater than 1/2 + 2-r of the inputs, by De n3 Morgan formulae of size at most n3/r2poly logn, for any parameter r ≤ n1/3. The proof relies on a result from quantum query complexity by Laplante et al. [CC, 2006], Høyer et al. [STOC, 2007] and Reichardt [SODA, 2011]: for any ´/´ Boolean function f, Q2(f) ≤ O( L(f)), where Q2(f) is the bounded-error quantum query complexity of f, and L(f) is the minimal size De Morgan formula computing f.
Keywords :
Boolean functions; computational complexity; probability; quantum computing; query processing; Boolean function; De Morgan formula shrinkage exponent; average-case hardness; bounded-error quantum query complexity; formula size reduction; log3 log n factor; logarithmic factor removal; probability; spectral techniques; tight lower bound; Approximation methods; Boolean functions; Complexity theory; Logic gates; Polynomials; Switches; De Morgan formulas; Fourier analysis; random restrictions; shrinkage;