Adaptive Lower Bound for Testing Monotonicity on the Line
نویسنده
چکیده
In the property testing model, the task is to distinguish objects possessing some property from the objects that are far from it. One of such properties is monotonicity, when the objects are functions from one poset to another. It is an active area of research. Recently, Pallavoor, Raskhodnikova and Varma (ITCS’17) proposed an ε-tester for monotonicity of a function f : [n] → [r], whose complexity depends on the size of the range as O ( log r ε ) . In this paper, we prove a nearly matching lower bound of Ω ( log r log log r ) for adaptive two-sided testers. Additionally, we give an alternative proof of the Ω(εd logn−ε log ε) lower bound for testing monotonicity on the hypergrid [n] due to Chackrabarty and Seshadhri (RANDOM’13).
منابع مشابه
D ec 2 01 4 Boolean function monotonicity testing requires ( almost ) n 1 / 2 non - adaptive queries Xi
We prove a lower bound of Ω(n), for all c > 0, on the query complexity of (two-sided error) non-adaptive algorithms for testing whether an n-variable Boolean function is monotone versus constant-far from monotone. This improves a Ω̃(n) lower bound for the same problem that was recently given in [CST14] and is very close to Ω(n), which we conjecture is the optimal lower bound for this model.
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عنوان ژورنال:
- CoRR
دوره abs/1801.08709 شماره
صفحات -
تاریخ انتشار 2018