TY - GEN
T1 - Partial tests, universal tests and decomposability
AU - Fischer, Eldar
AU - Goldhirsh, Yonatan
AU - Lachish, Oded
PY - 2014
Y1 - 2014
N2 - For a property P and a sub-property P′, we say that P is P′ -partially testable with q queries if there exists an algorithm that distinguishes, with high probability, inputs in P′ from inputs e-far from P, using q queries. Some natural properties require many queries to test, but can be partitioned into a small number of subsets for which they are partially testable with very few queries, sometimes even a number independent of the input size. For properties over 0,1, the notion of being thus partition-able ties in closely with Merlin-Arthur proofs of Proximity (MAPs) as defined independently in [14]; a partition into r partially-testable properties is the same as a Merlin-Arthur system where the proof consists of the identity of one of the r partially-testable properties, giving a 2-way translation to an O(logr) size proof. Our main result is that for some low complexity properties a partition as above cannot exist, and moreover that for each of our properties there does not exist even a single sub-property featuring both a large size and a query-efficient partial test, in particular improving the lower bound set in [14]. For this we use neither the traditional Yao-type arguments nor the more recent communication complexity method, but open up a new approach for proving lower bounds. First, we use entropy analysis, which allows us to apply our arguments directly to 2-sided tests, thus avoiding the cost of the conversion in [14] from 2-sided to 1-sided tests. Broadly speaking we use "distinguishing instances" of a supposed test to show that a uniformly random choice of a member of the sub-property has "low entropy areas", ultimately leading to it having a low total entropy and hence having a small base set. Additionally, to have our arguments apply to adaptive tests, we use a mechanism of "rearranging" the input bits (through a decision tree that adaptively reads the entire input) to expose the low entropy that would otherwise not be apparent. We also explore the possibility of a connection in the other direction, namely whether the existence of a good partition (or MAP) can lead to a relatively query-efficient standard property test. We provide some preliminary results concerning this question, including a simple lower bound on the possible trade-off. Our second major result is a positive trade-off result for the restricted framework of 1-sided proximity oblivious tests. This is achieved through the construction of a "universal tester" that works the same for all properties admitting the restricted test. Our tester is very related to the notion of sample-based testing (for a non-constant number of queries) as defined by Goldreich and Ron in [13]. In particular it partially resolves an open problem raised by [13].
AB - For a property P and a sub-property P′, we say that P is P′ -partially testable with q queries if there exists an algorithm that distinguishes, with high probability, inputs in P′ from inputs e-far from P, using q queries. Some natural properties require many queries to test, but can be partitioned into a small number of subsets for which they are partially testable with very few queries, sometimes even a number independent of the input size. For properties over 0,1, the notion of being thus partition-able ties in closely with Merlin-Arthur proofs of Proximity (MAPs) as defined independently in [14]; a partition into r partially-testable properties is the same as a Merlin-Arthur system where the proof consists of the identity of one of the r partially-testable properties, giving a 2-way translation to an O(logr) size proof. Our main result is that for some low complexity properties a partition as above cannot exist, and moreover that for each of our properties there does not exist even a single sub-property featuring both a large size and a query-efficient partial test, in particular improving the lower bound set in [14]. For this we use neither the traditional Yao-type arguments nor the more recent communication complexity method, but open up a new approach for proving lower bounds. First, we use entropy analysis, which allows us to apply our arguments directly to 2-sided tests, thus avoiding the cost of the conversion in [14] from 2-sided to 1-sided tests. Broadly speaking we use "distinguishing instances" of a supposed test to show that a uniformly random choice of a member of the sub-property has "low entropy areas", ultimately leading to it having a low total entropy and hence having a small base set. Additionally, to have our arguments apply to adaptive tests, we use a mechanism of "rearranging" the input bits (through a decision tree that adaptively reads the entire input) to expose the low entropy that would otherwise not be apparent. We also explore the possibility of a connection in the other direction, namely whether the existence of a good partition (or MAP) can lead to a relatively query-efficient standard property test. We provide some preliminary results concerning this question, including a simple lower bound on the possible trade-off. Our second major result is a positive trade-off result for the restricted framework of 1-sided proximity oblivious tests. This is achieved through the construction of a "universal tester" that works the same for all properties admitting the restricted test. Our tester is very related to the notion of sample-based testing (for a non-constant number of queries) as defined by Goldreich and Ron in [13]. In particular it partially resolves an open problem raised by [13].
KW - Information theoretic lower bounds
KW - Partial testing
KW - Property testing
KW - Sunflower theorems
KW - Universal testing
UR - http://www.scopus.com/inward/record.url?scp=84893290530&partnerID=8YFLogxK
U2 - 10.1145/2554797.2554841
DO - 10.1145/2554797.2554841
M3 - منشور من مؤتمر
SN - 9781450322430
T3 - ITCS 2014 - Proceedings of the 2014 Conference on Innovations in Theoretical Computer Science
SP - 483
EP - 500
BT - ITCS 2014 - Proceedings of the 2014 Conference on Innovations in Theoretical Computer Science
T2 - 2014 5th Conference on Innovations in Theoretical Computer Science, ITCS 2014
Y2 - 12 January 2014 through 14 January 2014
ER -