We present a distributed algorithm that constructs an O(log n)-approximate minimum spanning tree (MST) in any arbitrary network. This algorithm runs in time (O) over tilde (D(G)+L(G, w)) where L(G, w) is a parameter c...
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We present a distributed algorithm that constructs an O(log n)-approximate minimum spanning tree (MST) in any arbitrary network. This algorithm runs in time (O) over tilde (D(G)+L(G, w)) where L(G, w) is a parameter called the local shortest path diameter and D(G) is the (unweighted) diameter of the graph. Our algorithm is existentially optimal (up to polylogarithmic factors), i.e., there exist graphs which need Omega(D(G) + L(G, w)) time to compute an H-approximation to the MST for any H is an element of [1, Theta(log n)]. Our result also shows that there can be a significant time gap between exact and approximate MST computation: there exists graphs in which the running time of our approximation algorithm is exponentially faster than the time-optimal distributed algorithm that computes the MST. Finally, we show that our algorithm can be used to find an approximate MST in wireless networks and in random weighted networks in almost optimal (O) over tilde (D(G)) time.
The Grid is a new type of resource sharing infrastructure. Due to software and hardware limitations, the service that a certain Grid can offer is finite, and so is the number of users it can accommodate. If the number...
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The Grid is a new type of resource sharing infrastructure. Due to software and hardware limitations, the service that a certain Grid can offer is finite, and so is the number of users it can accommodate. If the number of users is too small, much of the planned resources would be wasted. On the other hand, excessive loading due to too many users could substantially reduce the benefit enjoyed by each user and also the efficiency of the Grid service. Therefore, there are two main problems for Grid design. (1) How many users should the Grid serve so that each user can receive the maximum benefit? (2) To a certain group of users, how much resources should be invested so that the construction and maintenance of the Grid become viable? Based on the economic theory of clubs, this paper gives a quantitative analysis of the quasi-optimal number of users and amount of each resource by regarding Grid services and resources as club goods. Based on our assumptions on the system model, we deduce two preliminary results and verify them by experiments using GridFTP. These two results allow the users to run randomized algorithms to achieve better system performance. Copyright (c) 2006 John Wiley & Sons, Ltd.
We present a simple, efficient, and stable method for computing-with any desired precision-the medial axis of simply connected planar domains. The domain boundaries are assumed to be given as polynomial spline curves....
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We present a simple, efficient, and stable method for computing-with any desired precision-the medial axis of simply connected planar domains. The domain boundaries are assumed to be given as polynomial spline curves. Our approach combines known results from the field of geometric approximation theory with a new algorithm from the field of computational geometry. Challenging steps are (1) the approximation of the boundary spline such that the medial axis is geometrically stable, and (2) the efficient decomposition of the domain into base cases where the medial axis can be computed directly and exactly. We solve these problems via spiral biarc approximation and a randomized divide & conquer algorithm. (C) 2008 Elsevier Ltd. All rights reserved.
A butterfly-based direct combined-field integralequation (CFIE) solver for analyzing scattering from electrically large, perfect electrically conducting objects is presented. The proposed solver leverages the butterfl...
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A butterfly-based direct combined-field integralequation (CFIE) solver for analyzing scattering from electrically large, perfect electrically conducting objects is presented. The proposed solver leverages the butterfly scheme to compress blocks of the hierarchical LU-factorized discretized CFIE operator and uses randomized butterfly reconstruction schemes to expedite the factorization. The memory requirement and computational cost of the direct butterfly-CFIE solver scale as O(N log(2) N) and O(N-1.5 log N), respectively. These scaling estimates permit significant memory and CPU savings when compared to those realized by low-rank decomposition-based solvers. The efficacy and accuracy of the proposed solver are demonstrated through its application to the analysis of scattering from canonical and realistic objects involving up to 14 million unknowns.
In order to describe or estimate different quantities related to a specific random variable, it is of prime interest to numerically generate such a variate. In specific situations, the exact generation of random varia...
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In order to describe or estimate different quantities related to a specific random variable, it is of prime interest to numerically generate such a variate. In specific situations, the exact generation of random variables might be either momentarily unavailable or too expensive in terms of computation time. It therefore needs to be replaced by an approximation procedure. As was previously the case, the ambitious exact simulation of first exit times for diffusion processes was unreachable though it concerns many applications in different fields like mathematical finance, neuroscience or reliability. The usual way to describe first exit times was to use discretization schemes, that are of course approximation procedures. Recently, Herrmann and Zucca (Herrmann and Zucca, 2020) proposed a new algorithm, the so-called GDET-algorithm (General Diffusion Exit Time), which permits to simulate exactly the first exit time for one-dimensional diffusions. The only drawback of exact simulation methods using an acceptance-rejection sampling is their time consumption. In this paper the authors highlight an acceleration procedure for the GDET-algorithm based on a multi-armed bandit model. The efficiency of this acceleration is pointed out through numerical examples.
In the rendezvous problem, two computing entities (called agents) located at different vertices in a graph have to meet at the same vertex. In this paper, we consider the synchronous neighborhood rendezvous problem, w...
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In the rendezvous problem, two computing entities (called agents) located at different vertices in a graph have to meet at the same vertex. In this paper, we consider the synchronous neighborhood rendezvous problem, where the agents are initially located at two adjacent vertices. While this problem can be trivially solved in O(Delta) rounds (Delta is the maximum degree of the graph), it is highly challenging to reveal whether that problem can be solved in o(Delta) rounds, even assuming the rich computational capability of agents. The only known result is that the time complexity of O(root n) rounds is achievable if the graph is complete and agents are probabilistic, asymmetric, and can use whiteboards placed at vertices. Our main contribution is to clarify the situation (with respect to computational models and graph classes) admitting such a sublinear-time rendezvous algorithm. More precisely, we present two algorithms achieving fast rendezvous additionally assuming bounded minimum degree, unique vertex identifier, accessibility to neighborhood IDs, and randomization. The first algorithm runs within (O) over tilde( root n Delta/delta + n/delta) rounds for graphs of the minimum degree larger than root n, where n is the number of vertices in the graph, and delta is the minimum degree of the graph. The second algorithm assumes that the largest vertex ID is O(n), and achieves (O) over tilde (n/root delta)-round time complexity without using whiteboards. These algorithms attain o(Delta)-round complexity in the case of delta = omega(root n log n) and delta = omega(n(2/3) log(4/3) n) respectively. We also prove that four unconventional assumptions of our algorithm, bounded minimum degree, accessibility to neighborhood IDs, initial distance one, and randomization are all inherently necessary for attaining fast rendezvous. That is, one can obtain the Omega(n)-round lower bound if either one of them is removed.
We propose an efficient and accurate randomized approximation algorithm for computing the price of European-Asian options. Our algorithm can be seen as a modification of the approximation algorithm developed by Aingwo...
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We propose an efficient and accurate randomized approximation algorithm for computing the price of European-Asian options. Our algorithm can be seen as a modification of the approximation algorithm developed by Aingworth et al. (2000) into a randomized algorithm, which improves the accuracy theoretically as well as practically. We also propose a new option named the Saving-Asian option which enjoys advantages of both the European-Asian and American-Asian options. It is shown that our approximation algorithm also works for pricing Saving-Asian options.
The Fisher scoring method is widely used for likelihood maximization, but its application can be difficult in situations where the expected information matrix is not available in closed form or when parameters have co...
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The Fisher scoring method is widely used for likelihood maximization, but its application can be difficult in situations where the expected information matrix is not available in closed form or when parameters have constraints In this paper, we describe an interpolation family that generalizes the Fisher scoring method and propose a general Monte Carlo approach that makes these generalized methods also applicable in such situations. With this approach, random samples are generated from the iteratively estimated models and used to provide estimates of the expected information As a result, the likelihood function can be optimized by repeatedly solving weighted linear regression problems Specific extensions of this general approach to fitting multivariate normal mixtures and to fitting mixed-effects models with a single discrete random effect are also described. Numerical studies show that the proposed algorithms are fast and reliable to use, as compared with the classical expectation-maximization algorithm (C) 2010 Elsevier B V All rights reserved
The popular fully-connected tensor network (FCTN) decomposition has achieved successful applications in many fields. A standard method to this decomposition is the alternating least squares. However, it often converge...
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The popular fully-connected tensor network (FCTN) decomposition has achieved successful applications in many fields. A standard method to this decomposition is the alternating least squares. However, it often converges slowly and suffers from issues of numerical stability. In this work, we investigate the SVD-based algorithms for FCTN decomposition to tackle the aforementioned deficiencies. On the basis of a result about FCTN-ranks, a deterministic algorithm, namely FCTN-SVD, is first proposed, which can approximate the FCTN decomposition under a fixed accuracy. Then, we present the randomized version of the algorithm. Both synthetic and real data are used to test our algorithms. Numerical results show that they perform much better than the existing methods, and the randomized algorithm can indeed yield acceleration on FCTN-SVD. Moreover, we also apply our algorithms to tensor-on-vector regression and achieve quite decent performance.
For a given graph and an integer t, the Min-Max 2-Clustering problem asks if there exists a modification of a given graph into two maximal disjoint cliques by inserting or deleting edges such that the number of the ed...
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For a given graph and an integer t, the Min-Max 2-Clustering problem asks if there exists a modification of a given graph into two maximal disjoint cliques by inserting or deleting edges such that the number of the editing edges incident to each vertex is at most t. It has been shown that the problem can be solved in polynomial time for , where n is the number of vertices. In this paper, we design parameterized algorithms for different ranges of t. Let . We show that the problem is polynomial-time solvable when roughly . When , we design a randomized and a deterministic algorithm with sub-exponential time parameterized complexity, i.e., the problem is in SUBEPT. We also show that the problem can be solved in time for and in time for , where .
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