An optimal lower bound for monotonicity testing over hypergrids

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Abstract

For positive integers n, d, consider the hypergrid [n]d with the coordinate-wise product partial ordering denoted by ≺. A function f: [n]d → ℕ is monotone if ∀x ≺ y, f(x) ≤ f(y). A function f is ε-far from monotone if at least an ε-fraction of values must be changed to make f monotone. Given a parameter ε, a monotonicity tester must distinguish with high probability a monotone function from one that is ε-far. We prove that any (adaptive, two-sided) monotonicity tester for functions f : [n]d → ℕ must make Ω(ε -1 d log n - ε-1 log ε-1) queries. Recent upper bounds show the existence of O(ε-1 d log n) query monotonicity testers for hypergrids. This closes the question of monotonicity testing for hypergrids over arbitrary ranges. The previous best lower bound for general hypergrids was a non-adaptive bound of Ω(d log n). © 2013 Springer-Verlag.

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APA

Chakrabarty, D., & Seshadhri, C. (2013). An optimal lower bound for monotonicity testing over hypergrids. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 8096 LNCS, pp. 425–435). https://doi.org/10.1007/978-3-642-40328-6_30

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