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An incremental algorithm for Algebraic Program Analysis

PROCEEDINGS OF THE ACM ON PROGRAMMING LANGUAGES-PACMPL 2025年第POPL期9卷 1934-1961页

作者： Zhou, Chenyu Fang, Yuzhou Wang, Jingbo Wang, Chao Univ Southern Calif Los Angeles CA 90007 USA Purdue Univ W Lafayette IN USA

We propose a method for conducting algebraic program analysis (APA) incrementally in response to changes of the program under analysis. APA is a program analysis paradigm that consists of two distinct steps: computing a path expression that succinctly summarizes the set of program paths of interest, and interpreting the path expression using a properly-defined semantic algebra to obtain program properties of interest. In this context, the goal of an incremental algorithm is to reduce the analysis time by leveraging the intermediate results computed before the program changes. We have made two main contributions. First, we propose a data structure for efficiently representing path expression as a tree together with a tree-based interpreting method. Second, we propose techniques for efficiently updating the program properties in response to changes of the path expression. We have implemented our method and evaluated it on thirteen Java applications from the DaCapo benchmark suite. The experimental results show that both our method for incrementally computing path expression and our method for incrementally interpreting path expression are effective in speeding up the analysis. Compared to the baseline APA and two state-of-the-art APA methods, the speedup of our method ranges from 160x to 4761x depending on the types of program analyses performed.

关键词： Algebraic Program Analysis Data-flow Analysis Side-channel Analysis incremental algorithm

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incremental algorithm for Discovering Frequent Subsequences in Multiple Data Streams

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INTERNATIONAL JOURNAL OF DATA WAREHOUSING AND MINING 2011年第4期7卷 1-20页

作者： Al-Mulla, Reem Al Aghbari, Zaher Univ Sharjah Dept Comp Sci Sharjah U Arab Emirates

In recent years, new applications emerged that produce data streams, such as stock data and sensor networks. Therefore, finding frequent subsequences, or clusters of subsequences, in data streams is an essential task in data mining. Data streams are continuous in nature, unbounded in size and have a high arrival rate. Due to these characteristics, traditional clustering algorithms fail to effectively find clusters in data streams. Thus, an efficient incremental algorithm is proposed to find frequent subsequences in multiple data streams. The described approach for finding frequent subsequences is by clustering subsequences of a data stream. The proposed algorithm uses a window model to buffer the continuous data streams. Further, it does not recompute the clustering results for the whole data stream at every window, but rather it builds on clustering results of previous windows. The proposed approach also employs a decay value for each discovered cluster to determine when to remove old clusters and retain recent ones. In addition, the proposed algorithm is efficient as it scans the data streams once and it is considered an Any-time algorithm since the frequent subsequences are ready at the end of every window.

关键词： Any-Time algorithm Clustering Subsequences Data Streams Frequent Subsequences incremental algorithm

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incremental algorithm for acoustoelastic theory of large static pre-deformed fluid-saturated porous media

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PROGRESS IN COMPUTATIONAL FLUID DYNAMICS 2017年第1期17卷 42-51页

作者： Ma, Hongfei Tian, Jiayong Wang, Mi China Earthquake Adm Inst Crustal Dynam 1 Anningzhuang Rd Beijing 100085 Peoples R China China Nucl Power Engn Co Ltd Hebei Branch 56 Yuhuadong Rd Shijiazhuang 050000 Hebei Peoples R China

The incremental acoustoelastic equations for fluid-saturated porous media (FSPM) under the large static pre-deformation are derived in this paper by incremental loading method based on classic acoustoelastic theory of FSPM, which provides quantitative acoustoelastic relation of FSPM with arbitrary constitutive equation. Isotropic FSPM with third-order constitutive equation are taken as an example to give the relation between wave velocity and confining pressure and discuss the effect of loading step on acoustoelastic relations of isotropic FSPM under closed-pore jacketed condition and opened-pore jacketed condition.

关键词： acoustoelasticity large static pre-deformation fluid-saturated porous media FSPM incremental algorithm

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An incremental algorithm for parallel training of the size and the weights in a feedforward neural network

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NEURAL PROCESSING LETTERS 2000年第2期11卷 131-138页

作者： Hlavácková-Schindler, K Fischer, MM Austrian Acad Sci Inst Urban & Reg Res A-1010 Vienna Austria Acad Sci Czech Republ Inst Comp Sci Prague 18207 8 Czech Republic Wirtschaftsuniv Wien Dept Econ & Social Geog A-1090 Vienna Austria

An algorithm of incremental approximation of functions in a normed linear space by feedforward neural networks is presented. The concept of variation of a function with respect to a set is used to estimate the approximation error together with the weight decay method, for optimizing the size and weights of a network in each iteration step of the algorithm. Two alternatives, recursively incremental and generally incremental, are proposed. In the generally incremental case, the algorithm optimizes parameters of all units in the hidden layer at each step. Tn the recursively incremental case, the algorithm optimizes the parameters corresponding to only one unit in the hidden layer at each step. In this case, an optimization problem with a smaller number of parameters is being solved at each step.

关键词： approximation of a function feedforward network incremental algorithm variation of a function with respect to a set weight decay

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incremental algorithm for finding principal curves

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IET SIGNAL PROCESSING 2015年第7期9卷 521-528页

作者： Ghassabeh, Youness Aliyari Rudzicz, Frank Toronto Rehabil Inst UHN Toronto ON Canada Univ Toronto Dept Comp Sci Toronto ON Canada

Principal curves are a non-linear generalisation of principal components. They are smooth curves that pass through the middle of a data set to provide a new representation of those data to make tasks, such as visualisation and dimensionality reduction easier and more accurate. The subspace constrained mean shift (SCMS) algorithm is a recently proposed technique to find principal curves. The algorithm assumes that the complete data set is available in advance and that new data points cannot be added to the data set during the process. The algorithm finds the points on the principal curves by using the complete data set. In this paper, the authors investigate the situation where the entire data set is not available in advance and instead are sampled sequentially. They propose an incremental version of the SCMS algorithm that trains using a sequence of observations. Simulation results show the effectiveness of the proposed algorithm to find a principal curve using a stream of observations.

关键词： computational geometry principal component analysis data structures incremental algorithm principal curves nonlinear generalisation principal components smooth curves data set data representation subspace constrained mean shift SCMS algorithm

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incremental algorithm Based on Wedge Sampling for Estimating Clustering Coefficient with MapReduce 8

Incremental Algorithm Based on Wedge Sampling for Estimating...

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8th IEEE International Conference on Software Engineering and Service Science (ICSESS)

作者： Liao, Qun Yang, Yulu Nankai Univ Coll Comp & Control Engn Tianjin Peoples R China

ISBN: (纸本)9781538604977

Clustering coefficient is widely used in many real world applications, such as social network analysis and community mining. However, it is expensive to compute clustering coefficient for the large and dynamic networks. To improve the performance of clustering coefficient computing for these dynamic graphs, we propose an incremental algorithm based on random wedge sampling and implement the proposed algorithm upon MapReduce. The proposed algorithm reuses previous result and updates the estimate incrementally, instead of computing the whole dynamic graph from scratch. Experiments on real-world graphs demonstrate that the proposed algorithm is accurate and efficient. Compared with a state-of-the-art MapReduce algorithm, the proposed algorithm runs faster without scarifying accuracy of estimate.

关键词： component Clustering coefficient wedge sampling incremental algorithm MapReduce

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An incremental algorithm to Feature Selection in Decision Systems with the Variation of Feature Set

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Chinese Journal of Electronics 2015年第1期24卷 128-133页

作者： QIAN Wenbin SHU Wenhao YANG Bingru ZHANG Changsheng School of Software Jiangxi Agriculture University School of Computer and Information Technology Beijing Jiaotong University

Feature selection is a challenging problem in pattern recognition and machine learning. In real-life applications, feature set in the decision systems may vary over time. There are few studies on feature selection with the variation of feature set. This paper focuses on this issue, an incremental feature selection algorithm in dynamic decision systems is developed based on dependency function. The incremental algorithm avoids some recomputations, rather than retrain the dynamic decision system as new one to compute the feature subset from scratch. We firstly employ an incremental manner to update the new dependency function, then we incorporate the calculated dependency function into the incremental feature selection algorithm. Compared with the direct(non-incremental) algorithm, the computational efficiency of the proposed algorithm is improved. The experimental results on different data sets from UCI show that the proposed algorithm is effective and efficient.

关键词： Rough sets Feature selection Attribute reduction incremental algorithm Dynamic data set.

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Force-Based incremental algorithm for Mining Community Structure in Dynamic Network

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Journal of Computer Science & Technology 2006年第3期21卷 393-400页

作者：杨博刘大有 College of Computer Science and Technology & Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin University Changchun 130012 P.R. China

Community structure is an important property of network. Being able to identify communities can provide invaluable help in exploiting and understanding both social and non-social networks. Several algorithms have been developed up till now. However, all these algorithms can work well only with small or moderate networks with vertexes of order 104. Besides, all the existing algorithms are off-line and cannot work well with highly dynamic networks such as web, in which web pages are updated frequently. When an already clustered network is updated, the entire network including original and incremental parts has to be recalculated, even though only slight changes are involved. To address this problem, an incremental algorithm is proposed, which allows for mining community structure in large-scale and dynamic networks. Based on the community structure detected previously, the algorithm takes little time to reclassify the entire network including both the original and incremental parts. Furthermore, the algorithm is faster than most of the existing algorithms such as Girvan and Newman＇s algorithm and its improved versions. Also, the algorithm can help to visualize these community structures in network and provide a new approach to research on the evolving process of dynamic networks.

关键词： incremental algorithm community structure dynamic network

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Nash equilibrium computation in two-network zero-sum games: An incremental algorithm

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NEUROCOMPUTING 2019年 359卷 114-121页

作者： Shi, Chong-Xiao Yang, Guang-Hong Northeastern Univ Coll Informat Sci & Engn Shenyang 110819 Liaoning Peoples R China Northeastern Univ State Key Lab Synthet Automat Proc Ind Shenyang 110819 Liaoning Peoples R China

In this paper, the problem of distributed Nash equilibrium computation in two-network zero-sum games is studied. Based on a sequential communication strategy, a novel incremental algorithm is developed to compute a Nash equilibrium. Different from the existing algorithms, the agents in two different subnet-works perform their updates in an asynchronous way, and the square-summable assumption of step sizes adopted in the existing methods is removed in our algorithm. In the convergence analysis of the proposed algorithm, two important relations of the agents' equilibrium estimates are firstly provided based on the properties of projection operator. Then by combining the methods of contradiction and mathematical induction, it is proven that the agents' estimates achieve a Nash equilibrium even without the square-summable requirement of step sizes. Finally, simulations are provided to verify the validity of our method. (C) 2019 Elsevier B.V. All rights reserved.

关键词： Distributed optimization incremental algorithm More general step sizes Two-network zero-sum games

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An incremental algorithm to construct a lattice of set intersections

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SCIENCE OF COMPUTER PROGRAMMING 2009年第3期74卷 128-142页

作者： Kourie, Derrick G. Obiedkov, Sergei Watson, Bruce W. van der Merwe, Dean Univ Pretoria Dept Comp Sci ZA-0002 Pretoria South Africa Higher Sch Econ Dept Appl Math Moscow Russia

An incremental algorithm to construct a lattice from a collection of sets is derived, refined, analyzed, and related to a similar previously published algorithm for constructing concept lattices. The lattice constructed by the algorithm is the one obtained by closing the collection of sets with respect to set intersection. The analysis explains the empirical efficiency of the related concept lattice construction algorithm that had been observed in previous studies. The derivation highlights the effectiveness of a correctness-by-construction approach to algorithm development. (c) 2008 Elsevier B.V. All rights reserved.

关键词： Concept lattice Closure system incremental algorithm Correctness by construction

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