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We consider the stochastic graph model where the location of each vertex is a random point in a given metric space. We study the problems of computing the expected lengths of the minimum spanning tree, the minimum perfect matching and the minimum cycle cover on such a stochastic graph and obtain an FPRAS (Fully Polynomial Randomized Approximation Scheme)(More)
We revisit the pairwise kidney exchange problem established by Roth Sonmez and Unver [23]. Our goal, explained in terms of graph theory, is to find a maximum fractional matching on an undirected graph, that Lorenz-dominates any other fractional matching. The Lorenz-dominant fractional matching , which can be implemented as a lottery of integral match-ings,(More)
With the dramatic growth in the number of application domains that generate probabilistic, noisy and uncertain data, there has been an increasing interest in designing algorithms for geometric or combinatorial optimization problems over such data. In this paper, we initiate the study of constructing ε-kernel coresets for uncertain points. We consider(More)
We study the minimum connected sensor cover problem (MIN-CSC) and the budgeted connected sensor cover (Budgeted-CSC) problem, both motivated by important applications in wireless sensor networks. In both problems, we are given a set of sensors and a set of target points in the Euclidean plane. In MIN-CSC, our goal is to find a set of sensors of minimum(More)
We consider the <i>multi-shop ski rental</i> problem. This problem generalizes the classic ski rental problem to a multi-shop setting, in which each shop has different prices for renting and purchasing a pair of skis, and a <i>consumer</i> has to make decisions on when and where to buy. We are interested in the <i>optimal online (competitive-ratio(More)
It is known that certain structures of the signal in addition to the standard notion of sparsity (called structured sparsity) can improve the sample complexity in several compressive sensing applications. Recently, Hegde et al. [17] proposed a framework, called approximation-tolerant model-based compressive sensing, for recovering signals with structured(More)
Proof. It is easy to verify the communication cost, and thus we focus on the proof for the approximation ratio below. Similar to the proof of Theorem 1, the grid G is rewritten as {g 1 , · · · , g m } where m = (k + z) T , and for each g j , its corresponding intersection T l=1 M l i l is rewritten as S j. Meanwhile, we denote the index-set indicating the(More)