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Massachusetts Institute of Technology

Tolerant Testing of Regular Languages in Sublinear Time

Abstract

dc:description.abstract

A classic problem in property testing is to test whether a binary input word ๐‘ค is in regular language ๐ฟ. Such testers distinguish the case that ๐‘ค is in ๐ฟ from the case where ๐‘ค is ๐œ–-far from ๐ฟ (๐œ–-far means that at least ๐œ– fraction of the bits in ๐‘ค must be modified to change ๐‘ค into a word in ๐ฟ. Otherwise, ๐‘ค is ๐œ–-close). When it is known that ๐‘ค is noisy, it can be useful to provide tolerant testers: algorithms that accept when ๐‘ค is ๐›ฟ-close and reject when ๐‘ค is ๐œ–-far, for ๐›ฟ < ๐œ–. We build on the work of Alon, Krivelevich, Newman and Szegedy [1] to provide a tolerant, constant time property tester for regular languages. Our main result is that given a regular language ๐ฟ โˆˆ {0, 1} * and an integer ๐‘›, there exists a randomized algorithm which accepts a word ๐‘ค of length ๐‘› if it is ๐›ฟ-close (๐›ฟ < ๐œ–) to a word in ๐ฟ and rejects with high probability if ๐‘ค is ๐œ–-far from a word in ๐ฟ. The algorithm queries polynomial in 1 ๐œ– bits in ๐‘ค.

Degree

thesis:*
Name thesis:degree_name
Master
Department dc:contributor.department
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
Grantor dc:publisher
Massachusetts Institute of Technology
Year dc:date.issued
2021

Author and committee

dc:creator, dc:contributor.*
Author dc:creator
  • Gong, Linda
Advisor dc:contributor.advisor
  • Rubinfeld, Ronitt

Rights

dc:rights
Statement dc:rights
  • In Copyright - Educational Use Permitted
  • Copyright MIT

Identifiers

dc:identifier.*
Handle dc:identifier.uri
https://hdl.handle.net/1721.1/139249
OAI identifier oai:identifier
oai:dspace.mit.edu:1721.1/139249

Chain of custody

source
Harvested from
MIT
Base URL
dspace.mit.edu/oai/request
Last updated
2026-07-22
Source record
OAI-PMH GetRecord
related terms
citation

Gong, Linda. Tolerant Testing of Regular Languages in Sublinear Time. Massachusetts Institute of Technology, 2021. https://hdl.handle.net/1721.1/139249