Directed Isoperimetric Theorems for Boolean Functions on the Hypergrid and an $\widetilde{O}(n\sqrt{d})$ Monotonicity Tester
Hadley Black, Deeparnab Chakrabarty, C. Seshadhri

TL;DR
This paper introduces a new non-adaptive monotonicity tester for Boolean functions on hypergrids, achieving near-optimal query complexity by establishing generalized directed isoperimetric theorems.
Contribution
The authors develop the first non-adaptive, one-sided monotonicity tester for all constant n with query complexity near the lower bound, extending directed isoperimetric inequalities to hypergrids.
Findings
Achieved a $ ilde{O}(rac{n ext{d}}{ ext{varepsilon}^2})$ query complexity for monotonicity testing.
Generalized directed Talagrand inequalities from hypercube to hypergrid.
Provided new isoperimetric bounds that underpin the improved testing algorithm.
Abstract
The problem of testing monotonicity for Boolean functions on the hypergrid, is a classic topic in property testing. When , the domain is the hypercube. For the hypercube case, a breakthrough result of Khot-Minzer-Safra (FOCS 2015) gave a non-adaptive, one-sided tester making queries. Up to polylog and factors, this bound matches the -query non-adaptive lower bound (Chen-De-Servedio-Tan (STOC 2015), Chen-Waingarten-Xie (STOC 2017)). For any , the optimal non-adaptive complexity was unknown. A previous result of the authors achieves a -query upper bound (SODA 2020), quite far from the bound for the hypercube. In this paper, we resolve the non-adaptive complexity of monotonicity testing for all constant , up to…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsComplexity and Algorithms in Graphs · Machine Learning and Algorithms · Optimization and Search Problems
