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Faster ShortestPath Algorithms for Planar Graphs
 STOC 94
, 1994
"... We give a lineartime algorithm for singlesource shortest paths in planar graphs with nonnegative edgelengths. Our algorithm also yields a lineartime algorithm for maximum flow in a planar graph with the source and sink on the same face. The previous best algorithms for these problems required\O ..."
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Cited by 200 (15 self)
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We give a lineartime algorithm for singlesource shortest paths in planar graphs with nonnegative edgelengths. Our algorithm also yields a lineartime algorithm for maximum flow in a planar graph with the source and sink on the same face. The previous best algorithms for these problems required\Omega
Resilient distributed datasets: A faulttolerant abstraction for inmemory cluster computing
, 2011
"... We present Resilient Distributed Datasets (RDDs), a distributed memory abstraction that lets programmers perform inmemory computations on large clusters in a faulttolerant manner. RDDs are motivated by two types of applications that current computing frameworks handle inefficiently: iterative algo ..."
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Cited by 239 (27 self)
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We present Resilient Distributed Datasets (RDDs), a distributed memory abstraction that lets programmers perform inmemory computations on large clusters in a faulttolerant manner. RDDs are motivated by two types of applications that current computing frameworks handle inefficiently: iterative
Fast Monte Carlo Algorithms for Matrices II: Computing a LowRank Approximation to a Matrix
 SIAM JOURNAL ON COMPUTING
, 2004
"... ... matrix A. It is often of interest to find a lowrank approximation to A, i.e., an approximation D to the matrix A of rank not greater than a specified rank k, where k is much smaller than m and n. Methods such as the Singular Value Decomposition (SVD) may be used to find an approximation to A ..."
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Cited by 216 (20 self)
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description of a lowrank approximation D to A, and which are qualitatively faster than the SVD. Both algorithms have provable bounds for the error matrix A D . For any matrix X , let kXk and kXk 2 denote its Frobenius norm and its spectral norm, respectively. In the rst algorithm, c = O(1
A tutorial on ReedSolomon coding for faulttolerance in RAIDlike systems
 Software – Practice & Experience
, 1997
"... It is wellknown that ReedSolomon codes may be used to provide error correction for multiple failures in RAIDlike systems. The coding technique itself, however, is not as wellknown. To the coding theorist, this technique is a straightforward extension to a basic coding paradigm and needs no speci ..."
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Cited by 234 (37 self)
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for the systems programmer. It presents a complete specification of the coding algorithm plus details on how it may be implemented. This specification assumes no prior knowledge of algebra or coding theory. The goal of this paper is for a systems programmer to be able to implement ReedSolomon coding
On finding the maxima of a set of vectors
 Journal of the ACM
, 1975
"... ASSTRACT. Let U1, U2,..., Ud be totally ordered sets and let V be a set of n ddimensional vectors In U ~ X Us.. X Ud. A partial ordering is defined on V in a natural way The problem of finding all maximal elements of V with respect to the partial ordering ~s considered The computational complexity ..."
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Cited by 230 (2 self)
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ASSTRACT. Let U1, U2,..., Ud be totally ordered sets and let V be a set of n ddimensional vectors In U ~ X Us.. X Ud. A partial ordering is defined on V in a natural way The problem of finding all maximal elements of V with respect to the partial ordering ~s considered The computational com
Typedirected partial evaluation
 Proceedings of the TwentyThird Annual ACM Symposium on Principles of Programming Languages
, 1996
"... Abstract. Typedirected partial evaluation stems from the residualization of arbitrary static values in dynamic contexts, given their type. Its algorithm coincides with the one for coercing asubtype value into a supertype value, which itself coincides with the one of normalization in thecalculus. T ..."
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Cited by 219 (38 self)
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Abstract. Typedirected partial evaluation stems from the residualization of arbitrary static values in dynamic contexts, given their type. Its algorithm coincides with the one for coercing asubtype value into a supertype value, which itself coincides with the one of normalization in the
Let
, 2008
"... Relating decision and search algorithms for rational points on curves of higher genus ..."
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Relating decision and search algorithms for rational points on curves of higher genus
Nonlinear Equality Constraints in Feasible Sequential Quadratic Programming
 Optimization Methods and Software
, 1996
"... this paper we investigate incorporating the Mayne and Polak scheme, modified along the lines of this second alternative, into the algorithm of [9]. The balance of this paper is organized as follows. In Section 2 we present the algorithm (a few of the details are deferred to Section 4 in order to avo ..."
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Cited by 19 (3 self)
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to avoid any loss of continuity). Section 3 is devoted to establishing convergence. In Section 4 we discuss an implementation and some numerical results. Finally, we offer some concluding remarks in Section 5. 2 ALGORITHM Let \Omega
Let
"... Fast Fourier Transform (FFT) is an efficient algorithm to compute the Discrete Fourier Transform (DFT) and its inverse. In this paper, we pay special attention to the description of complexdata FFT. We analyze two common descriptions of FFT and propose a new presentation. Our heuristic description ..."
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Fast Fourier Transform (FFT) is an efficient algorithm to compute the Discrete Fourier Transform (DFT) and its inverse. In this paper, we pay special attention to the description of complexdata FFT. We analyze two common descriptions of FFT and propose a new presentation. Our heuristic description
Results 21  30
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