133 research outputs found

    Linear time Constructions of some dd-Restriction Problems

    Full text link
    We give new linear time globally explicit constructions for perfect hash families, cover-free families and separating hash functions

    Optimal Query Complexity for Reconstructing Hypergraphs

    Get PDF
    In this paper we consider the problem of reconstructing a hidden weighted hypergraph of constant rank using additive queries. We prove the following: Let GG be a weighted hidden hypergraph of constant rank with n vertices and mm hyperedges. For any mm there exists a non-adaptive algorithm that finds the edges of the graph and their weights using O(mlognlogm) O(\frac{m\log n}{\log m}) additive queries. This solves the open problem in [S. Choi, J. H. Kim. Optimal Query Complexity Bounds for Finding Graphs. {\em STOC}, 749--758,~2008]. When the weights of the hypergraph are integers that are less than O(poly(nd/m))O(poly(n^d/m)) where dd is the rank of the hypergraph (and therefore for unweighted hypergraphs) there exists a non-adaptive algorithm that finds the edges of the graph and their weights using O(mlogndmlogm). O(\frac{m\log \frac{n^d}{m}}{\log m}). additive queries. Using the information theoretic bound the above query complexities are tight

    A Simple Algorithm for Hamiltonicity

    Full text link
    We develop a new algebraic technique that solves the following problem: Given a black box that contains an arithmetic circuit ff over a field of characteristic 22 of degree~dd. Decide whether ff, expressed as an equivalent multivariate polynomial, contains a multilinear monomial of degree dd. This problem was solved by Williams \cite{W} and Bj\"orklund et. al. \cite{BHKK} for a white box (the circuit is given as an input) that contains arithmetic circuit. We show a simple black box algorithm that solves the problem with the same time complexity. This gives a simple randomized algorithm for the simple kk-path problem for directed graphs of the same time complexity\footnote{O(f(k))O^*(f(k)) is O(poly(n)f(k))O(poly(n)\cdot f(k))} O(2k)O^*(2^k) as in \cite{W} and with reusing the same ideas from \cite{BHKK} with the above gives another algorithm (probably not simpler) for undirected graphs of the same time complexity O(1.657k)O^*(1.657^k) as in \cite{B10,BHKK}

    Superpolynomial Lower Bounds for Learning Monotone Classes

    Get PDF

    Almost Optimal Distribution-Free Junta Testing

    Get PDF
    We consider the problem of testing whether an unknown n-variable Boolean function is a k-junta in the distribution-free property testing model, where the distance between functions is measured with respect to an arbitrary and unknown probability distribution over {0,1}^n. Chen, Liu, Servedio, Sheng and Xie [Zhengyang Liu et al., 2018] showed that the distribution-free k-junta testing can be performed, with one-sided error, by an adaptive algorithm that makes O~(k^2)/epsilon queries. In this paper, we give a simple two-sided error adaptive algorithm that makes O~(k/epsilon) queries

    Improved Lower Bound for Estimating the Number of Defective Items

    Full text link
    Let XX be a set of items of size nn that contains some defective items, denoted by II, where IXI \subseteq X. In group testing, a {\it test} refers to a subset of items QXQ \subset X. The outcome of a test is 11 if QQ contains at least one defective item, i.e., QIQ\cap I \neq \emptyset, and 00 otherwise. We give a novel approach to obtaining lower bounds in non-adaptive randomized group testing. The technique produced lower bounds that are within a factor of 1/loglogklogn1/{\log\log\stackrel{k}{\cdots}\log n} of the existing upper bounds for any constant~kk. Employing this new method, we can prove the following result. For any fixed constants kk, any non-adaptive randomized algorithm that, for any set of defective items II, with probability at least 2/32/3, returns an estimate of the number of defective items I|I| to within a constant factor requires at least Ω(lognloglogklogn)\Omega\left(\frac{\log n}{\log\log\stackrel{k}{\cdots}\log n}\right) tests. Our result almost matches the upper bound of O(logn)O(\log n) and solves the open problem posed by Damaschke and Sheikh Muhammad [COCOA 2010 and Discrete Math., Alg. and Appl., 2010]. Additionally, it improves upon the lower bound of Ω(logn/loglogn)\Omega(\log n/\log\log n) previously established by Bshouty [ISAAC 2019]
    corecore