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569
Bayesian Optimization Algorithm: From Single Level to Hierarchy, Ph
, 2002
"... There are four primary goals of this dissertation. First, design a competent optimization algorithm capable of learning and exploiting appropriate problem decomposition by sampling and evaluating candidate solutions. Second, extend the proposed algorithm to enable the use of hierarchical decompositi ..."
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Cited by 84 (17 self)
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There are four primary goals of this dissertation. First, design a competent optimization algorithm capable of learning and exploiting appropriate problem decomposition by sampling and evaluating candidate solutions. Second, extend the proposed algorithm to enable the use of hierarchical decomposition as opposed to decomposition on only a single level. Third, design a class of difficult hierarchical problems that can be used to test the algorithms that attempt to exploit hierarchical decomposition. Fourth, test the developed algorithms on the designed class of problems and several realworld applications. The dissertation proposes the Bayesian optimization algorithm (BOA), which uses Bayesian networks to model the promising solutions found so far and sample new candidate solutions. BOA is theoretically and empirically shown to be capable of both learning a proper decomposition of the problem and exploiting the learned decomposition to ensure robust and scalable search for the optimum across a wide range of problems. The dissertation then identifies important features that must be incorporated into the basic BOA to solve problems that are not decomposable on a single level, but that can still be solved by decomposition over multiple levels of difficulty. Hierarchical
A Comparative Study of Language Support for Generic Programming
, 2003
"... Many modern programming languages support basic generic programming, sufficient to implement typesafe polymorphic containers. Some languages have moved beyond this basic support to a broader, more powerful interpretation of generic programming, and their extensions have proven valuable in practice. ..."
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Cited by 82 (14 self)
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Many modern programming languages support basic generic programming, sufficient to implement typesafe polymorphic containers. Some languages have moved beyond this basic support to a broader, more powerful interpretation of generic programming, and their extensions have proven valuable in practice. This paper reports on a comprehensive comparison of generics in six programming languages: C , Standard ML, Haskell, Eiffel, Java (with its proposed generics extension), and Generic C#. By implementing a substantial example in each of these languages, we identify eight language features that support this broader view of generic programming. We find these features are necessary to avoid awkward designs, poor maintainability, unnecessary runtime checks, and painfully verbose code. As languages increasingly support generics, it is important that language designers understand the features necessary to provide powerful generics and that their absence causes serious difficulties for programmers.
Parallel Algorithms for Hierarchical Clustering
 Parallel Computing
, 1995
"... Hierarchical clustering is a common method used to determine clusters of similar data points in multidimensional spaces. O(n 2 ) algorithms are known for this problem [3, 4, 10, 18]. This paper reviews important results for sequential algorithms and describes previous work on parallel algorithms f ..."
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Cited by 81 (1 self)
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Hierarchical clustering is a common method used to determine clusters of similar data points in multidimensional spaces. O(n 2 ) algorithms are known for this problem [3, 4, 10, 18]. This paper reviews important results for sequential algorithms and describes previous work on parallel algorithms for hierarchical clustering. Parallel algorithms to perform hierarchical clustering using several distance metrics are then described. Optimal PRAM algorithms using n log n processors are given for the average link, complete link, centroid, median, and minimum variance metrics. Optimal butterfly and tree algorithms using n log n processors are given for the centroid, median, and minimum variance metrics. Optimal asymptotic speedups are achieved for the best practical algorithm to perform clustering using the single link metric on a n log n processor PRAM, butterfly, or tree. Keywords. Hierarchical clustering, pattern analysis, parallel algorithm, butterfly network, PRAM algorithm. 1 In...
Titan: A high performance remotesensing database
 In Proceedings of the 1997 International Conference on Data Engineering
, 1997
"... There are two major challenges for a highperformance remotesensing database. First, it must provide lowlatency retrieval of very large volumes of spatiotemporal data. This requires eective declustering and placement of a multidimensional dataset onto a large disk farm. Second, the order of magn ..."
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Cited by 80 (31 self)
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There are two major challenges for a highperformance remotesensing database. First, it must provide lowlatency retrieval of very large volumes of spatiotemporal data. This requires eective declustering and placement of a multidimensional dataset onto a large disk farm. Second, the order of magnitude reduction in datasize due to postprocessing makes it imperative, from a performance perspective, that the postprocessing be done on the machine that holds the data. This requires careful coordination of computation and data retrieval. This paper describes the design, implementation and evaluation of Titan, a parallel sharednothing database designed for handling remotesensing data. The computational platform for Titan is a 16processor IBM SP2 with four fast disks attached to each processor. Titan is currently operational and contains about 24 GB of data from the Advanced Very High Resolution Radiometer (AVHRR) on the NOAA7 satellite. The experimental results show that Titan provides good performance for global queries, and interactive response times for local queries. 1
Evaluation of Multicast Routing Algorithms for RealTime Communication on HighSpeed Networks
 IEEE Journal on Selected Areas in Communications
, 1997
"... Abstract—Multicast (MC) routing algorithms capable of satisfying the quality of service (QoS) requirements of realtime applications will be essential for future highspeed networks. We compare the performance of all of the important MC routing algorithms when applied to networks with asymmetric li ..."
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Cited by 80 (4 self)
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Abstract—Multicast (MC) routing algorithms capable of satisfying the quality of service (QoS) requirements of realtime applications will be essential for future highspeed networks. We compare the performance of all of the important MC routing algorithms when applied to networks with asymmetric link loads. Each algorithm is judged based on the quality of the MC trees it generates and its efficiency in managing the network resources. Simulation results over random networks show that unconstrained algorithms are not capable of fulfilling the QoS requirements of realtime applications in widearea networks. Simulations also reveal that one of the unconstrained algorithms, reverse path multicasting (RPM), is quite inefficient when applied to asymmetric networks. We study how combining routing with resource reservation and admission control improves RPM’s efficiency in managing the network resources. The performance of one semiconstrained heuristic, MSC, three constrained Steiner tree (CST) heuristics, Kompella, Pasquale, and Polyzos (KPP), constrained adaptive ordering (CAO), and bounded shortest multicast algorithm (BSMA), and one constrained shortest path tree (CSPT) heuristic, the constrained Dijkstra heuristic (CDKS) are also studied. Simulations show that the semiconstrained and constrained heuristics are capable of successfully constructing MC trees which satisfy the QoS requirements of realtime traffic. However, the cost performance of the heuristics varies. BSMA’s MC trees are lower in cost than all other constrained heuristics. Finally, we compare the execution times of all algorithms, unconstrained, semiconstrained, and constrained. Index Terms—Admission control, multicast routing, quality of service, reverse path multicasting. I.
Optimum communication spanning trees
 SIAM J. Comput
, 1974
"... Abstract. Given a set of nodes N (i 1, 2,..., n) which may represent cities and a set of requirements ria which may represent the number of telephone calls between N and N j, the problem is to build a spanning tree connecting these n nodes such that the total cost of communication of the spanning tr ..."
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Cited by 71 (1 self)
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Abstract. Given a set of nodes N (i 1, 2,..., n) which may represent cities and a set of requirements ria which may represent the number of telephone calls between N and N j, the problem is to build a spanning tree connecting these n nodes such that the total cost of communication of the spanning tree is a minimum among all spanning trees. The cost of communication for a pair of nodes is r;a multiplied by the sum of the distances of arcs which form the unique path connecting Ni and N in the spanning tree. Summing over all () pairs of nodes, we have the total cost of communication of the spanning tree. Note that the problem is different from the minimum spanning tree problem solved by Kruskal and Prim. Key words, communication spanning trees, cuttree
Multicast routing with endtoend delay and delay variation constraints
 in Proc. IEEE INFOCOM’96
"... ..."
Cacheoblivious priority queue and graph algorithm applications
 In Proc. 34th Annual ACM Symposium on Theory of Computing
, 2002
"... In this paper we develop an optimal cacheoblivious priority queue data structure, supporting insertion, deletion, and deletemin operations in O ( 1 B logM/B N) amortized memory B transfers, where M and B are the memory and block transfer sizes of any two consecutive levels of a multilevel memory hi ..."
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Cited by 68 (11 self)
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In this paper we develop an optimal cacheoblivious priority queue data structure, supporting insertion, deletion, and deletemin operations in O ( 1 B logM/B N) amortized memory B transfers, where M and B are the memory and block transfer sizes of any two consecutive levels of a multilevel memory hierarchy. In a cacheoblivious data structure, M and B are not used in the description of the structure. The bounds match the bounds of several previously developed externalmemory (cacheaware) priority queue data structures, which all rely crucially on knowledge about M and B. Priority queues are a critical component in many of the best known externalmemory graph algorithms, and using our cacheoblivious priority queue we develop several cacheoblivious graph algorithms.
Spanning Trees Short Or Small
 SIAM JOURNAL ON DISCRETE MATHEMATICS
"... We study the problem of finding small trees. Classical network design problems are considered with the additional constraint that only a specified number k of nodes are required to be connected in the solution. A prototypical example is the kMST problem in which we require a tree of minimum weight s ..."
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Cited by 66 (2 self)
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We study the problem of finding small trees. Classical network design problems are considered with the additional constraint that only a specified number k of nodes are required to be connected in the solution. A prototypical example is the kMST problem in which we require a tree of minimum weight spanning at least k nodes in an edgeweighted graph. We show that the kMST problem is NPhard even for points in the Euclidean plane. We provide approximation algorithms with performance ratio 2 p k for the general edgeweighted case and O(k 1=4 ) for the case of points in the plane. Polynomialtime exact solutions are also presented for the class of treewidthbounded graphs which includes trees, seriesparallel graphs, and bounded bandwidth graphs, and for points on the boundary of a convex region in the Euclidean plane. We also investigate the problem of finding short trees, and more generally, that of finding networks with minimum diameter. A simple technique is used to prov...
Balancing Minimum Spanning and Shortest Path Trees
, 1993
"... Efficient algorithms are known for computing a minimum spann.ing tree, or a shortest path. tree (with a fixed vertex as the root). The weight of a shortest path tree can be much more than the weight of a minimum spa,nning tree. Conversely, the distance bet,ween the root, and any vertex in a minimum ..."
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Cited by 64 (1 self)
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Efficient algorithms are known for computing a minimum spann.ing tree, or a shortest path. tree (with a fixed vertex as the root). The weight of a shortest path tree can be much more than the weight of a minimum spa,nning tree. Conversely, the distance bet,ween the root, and any vertex in a minimum spanning tree may be much more than the distance bet#ween the two vertices in the graph. Consider the problem of balancing between the two kinds of trees: Does every graph contain a tree that is “light ” (at most a constant times heavier than the minimum spanning t,ree), such that the distance from the root to any vertex in t,he tree is no more than a constant times the true distance? This paper answers the question in the affirmative. It is shown that there is a continuous tradeoff between the two parameters. For every y> 0, there is a tree in the graph whose total weight is at most 1 + $? times the weight of a minimum spanning tree, such that the di&nce in the tree between the root, and any vertex is at, most 1 + &y times the true distance. Efficient sequential and parallel algorithms achieving these factors are provided. The algorithms are shown to be optimal in two ways. First, it is shown that no algorithm can achieve better factors in all graphs, because there a.re graphs that do not have better trees. Second, it is shown that even on a pergraph basis, finding trees that achieve better factors is NPhard.