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Electronic Colloquium on Computational Complexity

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Reports tagged with Monotonicity testing:
TR13-062 | 18th April 2013
Deeparnab Chakrabarty, C. Seshadhri

An optimal lower bound for monotonicity testing over hypergrids

For positive integers $n, d$, consider the hypergrid $[n]^d$ with the coordinate-wise product partial ordering denoted by $\prec$.
A function $f: [n]^d \mapsto \mathbb{N}$ is monotone if $\forall x \prec y$, $f(x) \leq f(y)$.
A function $f$ is $\varepsilon$-far from monotone if at least an $\varepsilon$-fraction of values must ... more >>>

TR15-011 | 22nd January 2015
Subhash Khot, Dor Minzer, Muli Safra

On Monotonicity Testing and Boolean Isoperimetric type Theorems

We show a directed and robust analogue of a boolean isoperimetric type theorem of Talagrand. As an application, we
give a monotonicity testing algorithm that makes $\tilde{O}(\sqrt{n}/\epsilon^2)$ non-adaptive queries to a function
$f:\{0,1\}^n \mapsto \{0,1\}$, always accepts a monotone function and rejects a function that is $\epsilon$-far from
being monotone ... more >>>

TR17-159 | 28th October 2017
Hadley Black, Deeparnab Chakrabarty, C. Seshadhri

A $o(d) \cdot \text{polylog}~n$ Monotonicity Tester for Boolean Functions over the Hypergrid $[n]^d$

We study monotonicity testing of Boolean functions over the hypergrid $[n]^d$ and design a non-adaptive tester with $1$-sided error whose query complexity is $\tilde{O}(d^{5/6})\cdot \text{poly}(\log n,1/\epsilon)$. Previous to our work, the best known testers had query complexity linear in $d$ but independent of $n$. We improve upon these testers as ... more >>>

TR18-005 | 9th January 2018
Deeparnab Chakrabarty, C. Seshadhri

Adaptive Boolean Monotonicity Testing in Total Influence Time

The problem of testing monotonicity
of a Boolean function $f:\{0,1\}^n \to \{0,1\}$ has received much attention
recently. Denoting the proximity parameter by $\varepsilon$, the best tester is the non-adaptive $\widetilde{O}(\sqrt{n}/\varepsilon^2)$ tester
of Khot-Minzer-Safra (FOCS 2015). Let $I(f)$ denote the total influence
of $f$. We give an adaptive tester whose running ... more >>>

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