Majority Is Asymptotically the Most Stable Resilient Function

The result that `Majority is Stablest, proven with O'Donnell and Oleszkiewicz (2005), states that, asymptotically, among all Boolean functions with sufficiently low influences and mean $0$, a simple majority function is most stable as the number of variables goes to infinity. It is natural to ask if the condition of low influences can be relaxed to the condition that the function has vanishing Fourier coefficients. Here we answer this question affirmatively. In fact, we prove a stronger statement showing that any Boolean function $f : \{0,1\}^n \to [0,1]$ with $E[f] = 1/2$ which is $\epsilon > 0$ more stable than the asymptotic stability of a balanced majority has correlation at least $\alpha(\epsilon) > 0$ with a Boolean function $g$ which depends on a bounded number, $r(\epsilon)$, of variables

[1]  Prasad Raghavendra,et al.  Beating the Random Ordering is Hard: Inapproximability of Maximum Acyclic Subgraph , 2008, 2008 49th Annual IEEE Symposium on Foundations of Computer Science.

[2]  Elchanan Mossel,et al.  Maximally stable Gaussian partitions with discrete applications , 2009, 0903.3362.

[3]  Eric Blais Testing juntas nearly optimally , 2009, STOC '09.

[4]  Noam Nisan,et al.  A Quantitative Version of the Gibbard-Satterthwaite Theorem for Three Alternatives , 2011, SIAM J. Comput..

[5]  Rocco A. Servedio,et al.  New Algorithms and Lower Bounds for Monotonicity Testing , 2014, 2014 IEEE 55th Annual Symposium on Foundations of Computer Science.

[6]  Elchanan Mossel,et al.  Noise correlation bounds for uniform low degree functions , 2009, 0904.0157.

[7]  Johan Håstad,et al.  Some optimal inapproximability results , 1997, STOC '97.

[8]  Chris Jones A Noisy-Influence Regularity Lemma for Boolean Functions , 2016, ArXiv.

[9]  PER AUSTRIN,et al.  Towards Sharp Inapproximability For Any 2-CSP , 2007, 48th Annual IEEE Symposium on Foundations of Computer Science (FOCS'07).

[10]  Rocco A. Servedio,et al.  Boolean Function Monotonicity Testing Requires (Almost) n 1/2 Non-adaptive Queries , 2014, STOC.

[11]  Subhash Khot,et al.  Inapproximability of Hypergraph Vertex Cover and Applications to Scheduling Problems , 2010, ICALP.

[12]  Gesine Reinert,et al.  Invariance Principles for Homogeneous Sums: Universality of Gaussian Wiener Chaos , 2009, 0904.1153.

[13]  Elchanan Mossel,et al.  A quantitative Arrow theorem , 2009, 0903.2574.

[14]  Prasad Raghavendra,et al.  Bypassing UGC from Some Optimal Geometric Inapproximability Results , 2016, TALG.

[15]  C. Borell Geometric bounds on the Ornstein-Uhlenbeck velocity process , 1985 .

[16]  Prasad Raghavendra,et al.  Beating the Random Ordering Is Hard: Every Ordering CSP Is Approximation Resistant , 2011, SIAM J. Comput..

[17]  Johan Håstad,et al.  On the Usefulness of Predicates , 2012, 2012 IEEE 27th Conference on Computational Complexity.

[18]  Elchanan Mossel Gaussian Bounds for Noise Correlation of Functions , 2007, FOCS 2007.

[19]  Subhash Khot On the power of unique 2-prover 1-round games , 2002, STOC '02.

[20]  Prasad Raghavendra,et al.  Optimal algorithms and inapproximability results for every CSP? , 2008, STOC.

[21]  Elchanan Mossel,et al.  Gaussian Bounds for Noise Correlation of Functions and Tight Analysis of Long Codes , 2008, 2008 49th Annual IEEE Symposium on Foundations of Computer Science.

[22]  Prasad Raghavendra,et al.  Agnostic Learning of Monomials by Halfspaces Is Hard , 2012, SIAM J. Comput..

[23]  Ryan O'Donnell,et al.  Optimal Inapproximability Results for MAX-CUT and Other 2-Variable CSPs? , 2007, SIAM J. Comput..

[24]  Elchanan Mossel,et al.  Real Analysis in Computer Science: A collection of Open Problems , 2014 .

[25]  Johan Håstad,et al.  Randomly Supported Independence and Resistance , 2011, SIAM J. Comput..

[26]  Daniel M. Kane,et al.  Bounded Independence Fools Degree-2 Threshold Functions , 2010, 2010 IEEE 51st Annual Symposium on Foundations of Computer Science.