Estimating the distance to a monotone function

Nir Ailon, Bernard Chazelle, Seshadhri Comandur, Ding Liu

Research output: Contribution to journalArticlepeer-review

64 Scopus citations


In standard property testing, the task is to distinguish between objects that have a property P and those that are ε-far from P, for some ε > 0. In this setting, it is perfectly acceptable for the tester to provide a negative answer for every input object that does not satisfy P. This implies that property testing in and of itself cannot be expected to yield any information whatsoever about the distance from the object to the property. We address this problem in this paper, restricting our attention to monotonicity testing. A function f : {1, . . . , n] R is at distance εf from being monotone if it can (and must) be modified at εfn places to become monotone. For any fixed δ > 0, we compute, with probability at least 2/3, an interval [(1/2 - 8)ε, ε] that encloses εf. The running time of our algorithm is O(ε f-1 log log εf-1 log n), which is optimal within a factor of log log εf-1 and represents a substantial improvement over previous work. We give a second algorithm with an expected running time of O(εf-1 log n log log log n). Finally, we extend our results to multivariate functions.

Original languageEnglish (US)
Pages (from-to)371-383
Number of pages13
JournalRandom Structures and Algorithms
Issue number3
StatePublished - Oct 2007
Externally publishedYes

All Science Journal Classification (ASJC) codes

  • Software
  • General Mathematics
  • Computer Graphics and Computer-Aided Design
  • Applied Mathematics


  • Distance estimation
  • Monotone functions
  • Property testing
  • Sublinear algorithms
  • Tolerant property testing


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