Monthly Archives: February 2015

Open problem for February 2015

Today’s post by Clément Canonne.

Following the Boolean monotonicity testing bonanza, here’s an open problem. In short, does adaptivity help for monotonicity testing of Boolean functions?

Problem: Consider the problem of monotonicity testing for Boolean functions on the hypercube. Given oracle access to \(f\colon \{0,1\}^n \to \{0,1\}\), we wish to decide if \(f\) is (i) monotone vs. (ii) \(\epsilon\)-far from monotone (in Hamming distance). For either the one-sided or two-sided version of the problem, what is the exact status of adaptive testers?

State of the art:
Fischer et al. [FLN+02] showed one-sided non-adaptive testers require \(\sqrt{n}\) queries. This implies an \(\Omega(\log n)\) lower bound for one-sided adaptive testers.
Chen et al. [CDST15] proved that two-sided non-adaptive testers require (essentially) \(\Omega(\sqrt{n})\) queries. This implies an \(\Omega(\log n)\) lower bound for 2-sided adaptive testers.
Khot et al. [KMS15] recently gave a one-sided non-adaptive tester making \(\tilde{O}(\sqrt{n}/\epsilon^2)\) queries. The story is essentially complete for non-adaptive testing.

Comments: As of now, it is not clear whether adaptivity can help. Berman et al. [BRY14] showed the benefit of adaptivity for Boolean monotonicity testing over the domain \([n]^2\) (switch the \(2\) and the \(n\) from the hypercube). A gap provably exists between adaptive and non-adaptive testers: \(O(1/\epsilon)\) vs. \(\Omega(\log(1/\epsilon)/\epsilon)\).


[FLN+02] E. Fischer, E. Lehman, I. Newman, S. Raskhodnikova, R. Rubinfeld, and A. Samorodnitsky. Monotonicity testing over general poset domains. Symposium on Theory of Computing, 2002

[BRY14] P. Berman, S. Raskhodnikova, and G. Yaroslavtsev. \(L_p\) testing. Symposium on Theory of Computing, 2014

[CDST15] X. Chen, A. De, R. Servedio, L.-Y. Tang. Boolean function monotonicity testing requires (almost) \(n^{1/2}\) non-adaptive queries. Symposium on Theory of Computing, 2015

[KMS15] S. Khot, D. Minzer, and S. Safra. On monotonicity testing and Boolean Isoperimetric type theorems. ECCC, 2015

Erratum: a previous version of this post stated (incorrectly)  lower bound of \(\Omega(\sqrt{n}/\epsilon^2)\). This has been corrected to \(\Omega(\sqrt{n})\).

News for January 2015

We ended our last post discussing results from 2014 by discussing the monotonicity testing conjecture that \(O(\sqrt{n})\) queries suffice to test whether a Boolean function is monotone.  As it turns out, a proof of this conjecture was just around the corner…

On Monotonicity Testing and Boolean Isoperimetric type Theorems by Subhash Khot, Dor Minzer, and Muli Safra (ECCC). This remarkable paper shows that, indeed, we can test whether \(f : \{0,1\}^n \to \{0,1\}\) is monotone with \(O(\sqrt{n})\) queries using a natural tester. The authors establish this result by building on the connection between monotonicity testers and directed analogues of isoperimetric inequalities on the Boolean hypercube first established by Chakrabarty and Seshadhri. Specifically, the authors establish a directed analogue of a classic inequality of Talagrand and combine it with a number of other interesting innovations to analyze their monotonicity tester.

Big Data on the Rise: Testing monotonicity of distributions by Clément Canonne (arXiv). Another setting in which monotonicity testing has received a lot of attention recently is in the setting of testing properties of distributions. A distribution \(D\) on \(\{1,2,\ldots,n\}\) is monotone (non-increasing) if its probability mass function is non-increasing: \(D(1) \ge D(2) \ge \cdots \ge D(n)\). When we must test whether a function is monotone by drawing samples from the distribution, \(\tilde{\Theta}(\sqrt{n})\) samples are both necessary and sufficient. This work shows that, in contrast, testers with stronger query access (such as conditional sampling) to the distribution can test monotonicity much more efficiently.