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Adaptive Lower Bound for Testing Monotonicity on the Line

Author Aleksandrs Belovs

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Aleksandrs Belovs
  • Faculty of Computing, University of Latvia, Raina bulvaris 19, Riga, Latvia.

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Aleksandrs Belovs. Adaptive Lower Bound for Testing Monotonicity on the Line. In Approximation, Randomization, and Combinatorial Optimization. Algorithms and Techniques (APPROX/RANDOM 2018). Leibniz International Proceedings in Informatics (LIPIcs), Volume 116, pp. 31:1-31:10, Schloss Dagstuhl - Leibniz-Zentrum für Informatik (2018)


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. This is an active area of research. In this paper we study query complexity of epsilon-testing monotonicity of a function f : [n]->[r]. All our lower bounds are for adaptive two-sided testers. - We prove a nearly tight lower bound for this problem in terms of r. The bound is Omega((log r)/(log log r)) when epsilon = 1/2. No previous satisfactory lower bound in terms of r was known. - We completely characterise query complexity of this problem in terms of n for smaller values of epsilon. The complexity is Theta(epsilon^{-1} log (epsilon n)). Apart from giving the lower bound, this improves on the best known upper bound. Finally, we give an alternative proof of the Omega(epsilon^{-1}d log n - epsilon^{-1}log epsilon^{-1}) lower bound for testing monotonicity on the hypergrid [n]^d due to Chakrabarty and Seshadhri (RANDOM'13).

Subject Classification

ACM Subject Classification
  • Theory of computation → Lower bounds and information complexity
  • property testing
  • monotonicity on the line
  • monotonicity on the hypergrid


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