Parameterized Property Testing of Functions

We investigate the parameters in terms of which the complexity of sublinear-time algorithms should be expressed. Our goal is to find input parameters that are tailored to the combinatorics of the specific problem being studied and design algorithms that run faster when these parameters are small. This direction enables us to surpass the (worst-case) lower bounds, expressed in terms of the input size, for several problems. Our aim is to develop a similar level of understanding of the complexity of sublinear-time algorithms to the one that was enabled by research in parameterized complexity for classical algorithms. Specifically, we focus on testing properties of functions. By parameterizing the query complexity in terms of the size r of the image of the input function, we obtain testers for monotonicity and convexity of functions of the form f:[n]to mathbb{R} with query complexity O(log r), with no dependence on n. The result for monotonicity circumvents the Omega(log n) lower bound by Fischer (Inf. Comput., 2004) for this problem. We present several other parameterized testers, providing compelling evidence that expressing the query complexity of property testers in terms of the input size is not always the best choice

Dynamic tree shortcut with constant degree

LNCS v.9188 entitled: Computing and Combinatorics: 21st International Conference, COCOON 2015, Beijing, China, August 4-6, 2015, ProceedingsGiven a rooted tree with n nodes, the tree shortcut problem is to add a set of shortcut edges to the tree such that the shortest path from each node to any of its ancestors is of length O(log n) and the degree increment of each node is constant. We consider in this paper the dynamic version of the problem, which supports node insertion and deletion. For insertion, a node can be inserted as a leaf node or an internal node by sub-dividing an existing edge. For deletion, a leaf node can be deleted, or an internal node can be merged with its single child. We propose an algorithm that maintains a set of shortcut edges in O(log n) time for an insertion or deletion.postprin

Erasure-Resilient Sublinear-Time Graph Algorithms

We investigate sublinear-time algorithms that take partially erased graphs represented by adjacency lists as input. Our algorithms make degree and neighbor queries to the input graph and work with a specified fraction of adversarial erasures in adjacency entries. We focus on two computational tasks: testing if a graph is connected or ?-far from connected and estimating the average degree. For testing connectedness, we discover a threshold phenomenon: when the fraction of erasures is less than ?, this property can be tested efficiently (in time independent of the size of the graph); when the fraction of erasures is at least ?, then a number of queries linear in the size of the graph representation is required. Our erasure-resilient algorithm (for the special case with no erasures) is an improvement over the previously known algorithm for connectedness in the standard property testing model and has optimal dependence on the proximity parameter ?. For estimating the average degree, our results provide an "interpolation" between the query complexity for this computational task in the model with no erasures in two different settings: with only degree queries, investigated by Feige (SIAM J. Comput. `06), and with degree queries and neighbor queries, investigated by Goldreich and Ron (Random Struct. Algorithms `08) and Eden et al. (ICALP `17). We conclude with a discussion of our model and open questions raised by our work

Optimal Unateness Testers for Real-Valued Functions: Adaptivity Helps

We study the problem of testing unateness of functions f:{0,1}^d -> R. We give an O(d/epsilon . log(d/epsilon))-query nonadaptive tester and an O(d/epsilon)-query adaptive tester and show that both testers are optimal for a fixed distance parameter epsilon. Previously known unateness testers worked only for Boolean functions, and their query complexity had worse dependence on the dimension both for the adaptive and the nonadaptive case. Moreover, no lower bounds for testing unateness were known. We generalize our results to obtain optimal unateness testers for functions f:[n]^d -> R. Our results establish that adaptivity helps with testing unateness of real-valued functions on domains of the form {0,1}^d and, more generally, [n]^d. This stands in contrast to the situation for monotonicity testing where there is no adaptivity gap for functions f:[n]^d -> R

Sublinear Algorithms for Approximating String Compressibility

We raise the question of approximating the compressibility of a string with respect to a fixed compression scheme, in sublinear time. We study this question in detail for two popular lossless compression schemes: run-length encoding (RLE) and a variant of Lempel-Ziv (LZ77), and present sublinear algorithms for approximating compressibility with respect to both schemes. We also give several lower bounds that show that our algorithms for both schemes cannot be improved significantly. Our investigation of LZ77 yields results whose interest goes beyond the initial questions we set out to study. In particular, we prove combinatorial structural lemmas that relate the compressibility of a string with respect to LZ77 to the number of distinct short substrings contained in it (its ℓth subword complexity , for small ℓ). In addition, we show that approximating the compressibility with respect to LZ77 is related to approximating the support size of a distribution.National Science Foundation (U.S.) (Award CCF-1065125)National Science Foundation (U.S.) (Award CCF-0728645)Marie Curie International Reintegration Grant PIRG03-GA-2008-231077Israel Science Foundation (Grant 1147/09)Israel Science Foundation (Grant 1675/09

Towards a Definitive Measure of Repetitiveness

Unlike in statistical compression, where Shannon’s entropy is a definitive lower bound, no such clear measure exists for the compressibility of repetitive sequences. Since statistical entropy does not capture repetitiveness, ad-hoc measures like the size z of the Lempel–Ziv parse are frequently used to estimate repetitiveness. Recently, a more principled measure, the size γ of the smallest string attractor, was introduced. The measure γ lower bounds all the previous relevant ones (including z), yet length-n strings can be represented and efficiently indexed within space O(γlognγ), which also upper bounds most measures (including z). While γ is certainly a better measure of repetitiveness than z, it is NP-complete to compute, and no o(γlog n) -space representation of strings is known. In this paper, we study a smaller measure, δ≤ γ, which can be computed in linear time. We show that δ better captures the compressibility of repetitive strings. For every length n and every value δ≥ 2, we construct a string such that γ=Ω(δlognδ). Still, we show a representation of any string S in O(δlognδ) space that supports direct access to any character S[i] in time O(lognδ) and finds the occ occurrences of any pattern P[1.m] in time O(mlog n+ occlogεn) for any constant ε> 0. Further, we prove that no o(δlog n) -space representation exists: for every length n and every value 2 ≤ δ≤ n1-ε, we exhibit a string family whose elements can only be encoded in Ω(δlognδ) space. We complete our characterization of δ by showing that, although γ, z, and other repetitiveness measures are always O(δlognδ), for strings of any length n, the smallest context-free grammar can be of size Ω(δlog2n/ log log n). No such separation is known for γ

Lower bounds for embedding edit distance into normed spaces

Computer Science Departmen