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International Conference on Machine Learning and Cybernetics

作者： Li, QH Wang, H He, Y Jiang, SY Huazhong Univ Sci & Technol Comp Sch Wuhan 430074 Peoples R China

ISBN: (纸本)0780378652

The discovery of frequent itemsets is a very computational and I/O intensive task, and beyond a certain database size, it is crucial to leverage and the combined computational power of multiple processors for fast response and scalability. In this paper we present new scalable algorithm for maximal frequent itemset mining. It decomposes the search space by prefix-based equivalence classes, distributes work among the processors and selectively duplicates databases in such a way that each processor can compute the maximal frequent itemsets independently. It utilizes multiple level backtrack pruning strategy, along with vertical database format, counting frequency by simple tid-list intersection operation. These techniques eliminate the need for synchronization, drastically cutting down the communication cost. The analysis and experimental results demonstrate that our approach is well scalable in speedup and sizeup.

关键词： maximal frequent itemset scalable algorithm equivalence class parallel mining

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A scalable algorithm for the decomposition of minimally rigid graph

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MEASUREMENT & CONTROL 2020年第5-6期53卷 807-813页

作者： Zhu, Yadong Wang, Qin Li, Shengquan Yang, Yuequan Yangzhou Univ Yangzhou Jiangsu Peoples R China Yangzhou Polytech Univ Yangzhou Jiangsu Peoples R China

The rigid graph needs to be decomposed to solve the multi-equilibrium problem of the multi-agent formation control based on navigation function method. In this paper, a theorem and a scalable algorithm based on the Henneberg sequence of graphs are proposed for the decomposition of minimally rigid graph. The theorem demonstrates that if graphG(V,E) is a minimally rigid graph, then it can be decomposed asG=G(t)?G(c), whereG(t)is a spanning tree ofGandG(c)contains the remaining edges and their vertices. Moreover, the scalable algorithm is given to constructG(t)= (V-t,E-t) andG(c)= (V-c,E-c), and assign the edges inE(c)ton-- 2 distinct vertices in a scalable way via communication. Furthermore, a lemma is given to show when the number of vertices is less than eight, any edge can be chosen as the initialized edge, and the scalable algorithm mentioned above is always feasible. Finally, the effectiveness of the scalable algorithm is verified by numerical simulation.

关键词： Minimally rigid graph rigid formation control global stability graph decomposition scalable algorithm

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A fast scalable algorithm for discontinuous optical flow estimation

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IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE 1996年第2期18卷 181-194页

作者： Ghosal, S Vanek, P UNIV COLORADO CTR COMPUTAT MATHDENVERCO 80217

Multiple moving objects, partially occluded objects, or even a single object moving against the background gives rise to discontinuities in the optical flow field in corresponding image sequences. While uniform global regularization based moderately fast techniques cannot provide accurate estimates of the discontinuous flow field, statistical optimization based accurate techniques suffer from excessive solution time. A 'weighted anisotropic' smoothness based numerically robust algorithm is proposed that can generate discontinuous optical flow field with high speed and linear computational complexity. Weighted sum of the first-order spatial derivatives of the flow field is used for regularization. Less regularization is performed where strong gradient information is available. The flow field at any point is interpolated more from those at neighboring points along the weaker intensity gradient-component. Such intensity gradient weighted regularization leads to Euler-Lagrange equations with strong anisotropies coupled with discontinuities in their coefficients. A robust multilevel iterative technique, that recursively generates coarse-level problems based on intensity gradient weighted smoothing weights, is employed to estimate discontinuous optical flow field. Experimental results are presented to demonstrate the efficacy of the proposed technique.

关键词： discontinuous optical flow estimation weighted anisotropic smoothness partial differential equation (PDE) scalable algorithm multilevel iterative methods

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A parallel and scalable algorithm for ADI method with pre-propagation and message vectorization

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PARALLEL COMPUTING 2004年第12期30卷 1345-1359页

作者： Wakatani, A Konan Univ Fac Sci & Engn Kobe Hyogo 6588501 Japan

It is hard to implement the ADI method in an efficient way on distributed-memory parallel computers. We propose "P-scheme" which parallelizes a tridiagonal linear system of equations for the ADI method, but its effectiveness is limited to the cases where the problem size is large enough mainly because of the communication cost of the propagation phase of the scheme. In order to overcome this difficulty, we propose an improved version of the P-scheme with "message vectorization" which aggregates several communication messages into one and alleviates the communication cost. Also we evaluate the effectiveness of message vectorization for the ADI method and show that the improved version of the P-scheme works well even for smaller problems and linear and super-linear speedups can be achieved for 8194 x 8194 and 16,386 x 16,386 problems, respectively. (C) 2004 Elsevier B.V. All rights reserved.

关键词： tridiagonal matrix message vectorization super-linear speedup scalable algorithm ADI

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A scalable algorithm to order and annotate continuous observations reveals the metastable states visited by dynamical systems

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COMPUTER PHYSICS COMMUNICATIONS 2013年第11期184卷 2446-2453页

作者： Bloechliger, Nicolas Vitalis, Andreas Caflisch, Amedeo Univ Zurich Dept Biochem CH-8057 Zurich Switzerland

Advances in IT infrastructure have enabled the generation and storage of very large data sets describing complex systems continuously in time. These can derive from both simulations and measurements. Analysis of such data requires the availability of scalable algorithms. In this contribution, we propose a scalable algorithm that partitions instantaneous observations (snapshots) of a complex system into kinetically distinct sets (termed basins). To do so, we use a combination of ordering snapshots employing the method's only essential parameter, i.e., a definition of pairwise distance, and annotating the resultant sequence, the so-called progress index, in different ways. Specifically, we propose a combination of cut-based and structural annotations with the former responsible for the kinetic grouping and the latter for diagnostics and interpretation. The method is applied to an illustrative test case, and the scaling of an approximate version is demonstrated to be O (N log N) with N being the number of snapshots. Two real-world data sets from river hydrology measurements and protein folding simulations are then used to highlight the utility of the method in finding basins for complex systems. Both limitations and benefits of the approach are discussed along with routes for future research. (C) 2013 Elsevier B.V. All rights reserved.

关键词： Complex system Trajectory analysis scalable algorithm Minimum spanning tree Free energy basins

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A scalable algorithm for Constructing Frequent Pattern Tree

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INTERNATIONAL JOURNAL OF INTELLIGENT INFORMATION TECHNOLOGIES 2014年第1期10卷 42-56页

作者： Abdullah, Zailani Herawan, Tutut Noraziah, A. Deris, Mustafa Univ Malaysia Dept Comp Sci Terengganu Malaysia Univ Ahmad Dahlan Dept Math Educ Yogyakarta Indonesia Univ Malaysia Comp Syst & Software Engn Kuantan Malaysia Univ Tun Hussein Onn Malaysia Comp Sci & Informat Technol Johor Baharu Malaysia

Frequent Pattern Tree (FP-Tree) is a compact data structure of representing frequent itemsets. The construction of FP-Tree is very important prior to frequent patterns mining. However, there have been too limited efforts specifically focused on constructing FP-Tree data structure beyond from its original database. In typical FP-Tree construction, besides the prior knowledge on support threshold, it also requires two database scans;first to build and sort the frequent patterns and second to build its prefix paths. Thus, twice database scanning is a key and major limitation in completing the construction of FP-Tree. Therefore, this paper suggests scalable Trie Transformation Technique algorithm (T3A) to convert our predefined tree data structure, Disorder Support Trie Itemset (DOSTrieIT) into FP-Tree. Experiment results through two UCI benchmark datasets show that the proposed T3A generates FP-Tree up to 3 magnitudes faster than that the benchmarked FP-Growth.

关键词： Association Rules Data Mining Frequent patterns Frequent Pattern Tree (FP-Tree) scalable algorithm

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Battlefield Optimization algorithm☆

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EXPERT SYSTEMS WITH APPLICATIONS 2025年 266卷

作者： Setiawan, Dadang Suyanto, Suyanto Erfianto, Bayu Gozali, Alfian Akbar Telkom Univ Sch Comp Bandung Indonesia Telkom Univ Ctr Excellence Artificial Intelligence Learning & Bandung Indonesia

This paper proposes anew metaheuristic optimization algorithm named Battlefield Optimization algorithm (BfOA). As the name implies, it is inspired by the simulation of a battlefield, where two opposing squads battle each other. The squad with the stronger position defends its position and the weaker squad attacks using Supit Urang formation (an Indonesian traditional battle formation). Each squad is led by a commander. The commander leads three types of soldiers (cavaliers, special forces, and builders). Before the battle started, in the first phase each squad employs airplanes to scan the best position to begin the battle. The airplanes' movements are highly explorative. In the second phase, the defending squad's movement is highly exploitative, while the attacking squad is in the mid-explorative movement. The proposed algorithm is examined using twentythree benchmark functions and compared with one classic and five newer algorithms. To further examine the capability of the algorithm, CEC 2021 Single Objective Bound Constrained Numerical Optimization benchmark functions are used. Then the algorithm is tested in a real-world engineering optimization problem by solving the Three-Bar Truss problem.

关键词： Battlefield Optimization algorithm Battle-inspired algorithm Metaheuristic optimization Swarm intelligence scalable algorithm Supit Urang formation

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scalable algorithms for signal reconstruction by leveraging similarity joins

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VLDB JOURNAL 2020年第2-3期29卷 681-707页

作者： Asudeh, Abolfazl Augustine, Jees Nazi, Azade Thirumuruganathan, Saravanan Zhang, Nan Das, Gautam Srivastava, Divesh Univ Illinois Chicago IL 60607 USA Univ Texas Arlington Arlington TX 76019 USA Google AI Mountain View CA USA HBKU QCRI Ar Rayyan Qatar Penn State Univ State Coll PA USA AT&T Labs Res Florham Pk NJ USA

Signal reconstruction problem (SRP) is an important optimization problem where the objective is to identify a solution to an underdetermined system of linear equations that is closest to a given prior. It has a substantial number of applications in diverse areas including network traffic engineering, medical image reconstruction, acoustics, astronomy and many more. Most common approaches for SRP do not scale to large problem sizes. In this paper, we propose multiple optimization steps, developing scalable algorithms for the problem. We first propose a dual formulation of the problem and develop the Direct algorithm that is significantly more efficient than the state of the art. Second, we show how adapting database techniques developed for scalable similarity joins provides a significant speedup over Direct, scaling our proposal up to large-scale settings. Third, we describe a number of practical techniques that allow our algorithm to scale to settings of size in the order of a million by a billion. We also adapt our proposal to identify the top-k components of the solved system of linear equations. Finally, we consider the dynamic setting where the inputs to the linear system change and propose efficient algorithms inspired by the database techniques of materialization and reuse. Extensive experiments on real-world and synthetic data confirm the efficiency, effectiveness and scalability of our proposal.

关键词： Signal reconstruction Traffic reconstruction Underdetermined systems scalable algorithm

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A scalable FETI-DP algorithm for a semi-coercive variational inequality

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COMPUTER METHODS IN APPLIED MECHANICS AND ENGINEERING 2007年第8期196卷 1369-1379页

作者： Dostal, Zdenek Horak, David Stefanica, Dan Tech Univ Ostrava FEI VSB CZ-70833 Ostrava Czech Republic CUNY Baruch Coll New York NY 10010 USA

We develop an optimal algorithm for the numerical solution of semi-coercive variational inequalities by combining dual-primal FETI algorithms with recent results for bound and equality constrained quadratic programming problems. The discretized version of the model problem, obtained by using the FETI-DP methodology, is reduced by the duality theory of convex optimization to a quadratic programming problem with bound and equality constraints, which is solved by a new algorithm with a known rate of convergence given in terms of the spectral condition number of the quadratic problem. We present convergence bounds that guarantee the scalability of the algorithm. These results are confirmed by numerical experiments. (c) 2006 Elsevier B.V. All rights reserved.

关键词： domain decomposition semicoercive contact problem scalable algorithm FETI-DP

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MOTION ESTIMATION algorithm FOR scalable HARDWARE IMPLEMENTATION

MOTION ESTIMATION ALGORITHM FOR SCALABLE HARDWARE IMPLEMENTA...

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Picture Coding Symposium 2009

作者： Konieczny, Jacek Luczak, Adam Poznan Univ Tech Chair Multimedia Telecommun & Microelect Poznan Poland

ISBN: (纸本)9781424445936

Motion estimation is an indispensable stage of motion pictures encoding used in all common video coding standards, such as MPEG-2 [3] or AVC [4]. At the same time it is the most complex operation both in software and hardware implementation of an encoder that makes the choice of the appropriate motion estimation algorithm crucial in terms of increasing the entire coding process efficiency. This should be taken into consideration especially ill case of real time processing. In this paper, a novel scalable multi-level algorithm for motion estimation is proposed. It is a fast motion estimation algorithm, using the block-matching mechanism, provided the motion vectors accuracy equal 1/4 of the sampling period. Proposed algorithm is modular and scalable. First feature enables structure scalability, second provides mechanism to control of clock cycles number spend for motion estimation process. As a result the global clock-rate required for real-time processing can be adjusted.

关键词： motion estimation scalable algorithm real-time processing

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