Efficiently decodable non-adaptive group testing

We consider the following "efficiently decodable" non-adaptive group testing problem. There is an unknown string x 2 f0; 1gn [x is an element of set {0,1} superscript n] with at most d ones in it. We are allowed to test any subset S [n] [S subset [n] ]of the indices. The answer to the test tells whether xi = 0 [x subscript i = 0] for all i 2 S [i is an element of S] or not. The objective is to design as few tests as possible (say, t tests) such that x can be identifi ed as fast as possible (say, poly(t)-time). Efficiently decodable non-adaptive group testing has applications in many areas, including data stream algorithms and data forensics. A non-adaptive group testing strategy can be represented by a t x n matrix, which is the stacking of all the characteristic vectors of the tests. It is well-known that if this matrix is d-disjunct, then any test outcome corresponds uniquely to an unknown input string. Furthermore, we know how to construct d-disjunct matrices with t = O(d2 [d superscript 2] log n) efficiently. However, these matrices so far only allow for a "decoding" time of O(nt), which can be exponentially larger than poly(t) for relatively small values of d. This paper presents a randomness efficient construction of d-disjunct matrices with t = O(d2 [d superscript 2] log n) that can be decoded in time poly(d) [function composed of] t log2 t + O(t2) [t log superscript 2 t and O (t superscript 2)]. To the best of our knowledge, this is the first result that achieves an efficient decoding time and matches the best known O(d2 log n) [O (d superscript 2 log n)] bound on the number of tests. We also derandomize the construction, which results in a polynomial time deterministic construction of such matrices when d = O(log n= log log n). A crucial building block in our construction is the notion of (d,l)-list disjunct matrices, which represent the more general "list group testing" problem whose goal is to output less than d + l positions in x, including all the (at most d) positions that have a one in them. List disjunct matrices turn out to be interesting objects in their own right and were also considered independently by [Cheraghchi, FCT 2009]. We present connections between list disjunct matrices, expanders, dispersers and disjunct matrices. List disjunct matrices have applications in constructing (d,l)- sparsity separator structures [Ganguly, ISAAC 2008] and in constructing tolerant testers for Reed-Solomon codes in the data stream model. 1 IntroductionDavid & Lucile Packard FoundationCenter for Massive Data Algorithmics (MADALGO)National Science Foundation (U.S.) (Grant CCF-0728645)National Science Foundation (U.S.) (Grant CCF-0347565)National Science Foundation (U.S.) (CAREER Award CCF-0844796