TY - CHAP
T1 - On the average-case complexity of property testing
AU - Goldreich, Oded
PY - 2011
Y1 - 2011
N2 - Motivated by a study of Zimand (22nd CCC, 2007), we consider the average-case complexity of property testing (focusing, for clarity, on testing properties of Boolean strings). We make two observations: 1 In the context of average-case analysis with respect to the uniform distribution (on all strings of a fixed length), property testing is trivial. Specifically, either the yes-instances (i.e., instances having the property) or the no-instances (i.e., instances that are far from having the property) are exponentially rare, and thus the tester may just reject (resp., accept) obliviously of the input. 2 Turning to average-case derandomization with respect to distributions that assigns noticeable probability mass to both yes-instances and no-instances, we identify a natural class of distributions and testers for which average-case derandomization results can be obtained directly (i.e., without using randomness extractors). Furthermore, the resulting deterministic algorithm may preserve the non-adaptivity of the original tester. (In contrast, Zimand's argument utilizes a strong type of randomness extractors and introduces adaptivity into the testing process.)
AB - Motivated by a study of Zimand (22nd CCC, 2007), we consider the average-case complexity of property testing (focusing, for clarity, on testing properties of Boolean strings). We make two observations: 1 In the context of average-case analysis with respect to the uniform distribution (on all strings of a fixed length), property testing is trivial. Specifically, either the yes-instances (i.e., instances having the property) or the no-instances (i.e., instances that are far from having the property) are exponentially rare, and thus the tester may just reject (resp., accept) obliviously of the input. 2 Turning to average-case derandomization with respect to distributions that assigns noticeable probability mass to both yes-instances and no-instances, we identify a natural class of distributions and testers for which average-case derandomization results can be obtained directly (i.e., without using randomness extractors). Furthermore, the resulting deterministic algorithm may preserve the non-adaptivity of the original tester. (In contrast, Zimand's argument utilizes a strong type of randomness extractors and introduces adaptivity into the testing process.)
UR - http://www.scopus.com/inward/record.url?scp=84857559843&partnerID=8YFLogxK
U2 - 10.1007/978-3-642-22670-0_15
DO - 10.1007/978-3-642-22670-0_15
M3 - فصل
SN - 9783642226694
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 124
EP - 135
BT - Studies in Complexity and Cryptography
A2 - Goldreich, Oded
ER -