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20th IEEE International Conference on Data Mining (ICDM)

作者： Liang, Guannan Tong, Qianqian Ding, Jiahao Pan, Miao Bi, Jinbo Univ Connecticut Storrs CT 06269 USA Univ Houston Houston TX 77004 USA

ISBN: (纸本)9781728183169

Sparse learning is a very important tool for mining useful information and patterns from high dimensional data. Non-convex non-smooth regularized learning problems play essential roles in sparse learning, and have drawn extensive attentions recently. We design a family of stochastic proximal gradient methods by applying arbitrary sampling to solve the empirical risk minimization problem with a non-convex and non-smooth regularizer. These methods draw mini-batches of training examples according to an arbitrary probability distribution when computing stochastic gradients. A unified analytic approach is developed to examine the convergence and computational complexity of these methods, allowing us to compare the different sampling schemes. We show that the independent sampling scheme tends to improve performance over the commonly-used uniform sampling scheme. Our new analysis also derives a tighter bound on convergence speed for the uniform sampling than the best one available so far. Empirical evaluations demonstrate that the proposed algorithms converge faster than the state of the art.

关键词： stochastic algorithm proximal methods arbitrary sampling

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Randomly Activated Proximal Methods for Nonsmooth Convex Minimization 32

Randomly Activated Proximal Methods for Nonsmooth Convex Min...

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32nd European Signal Processing Conference (EUSIPCO)

作者： Combettes, Patrick L. Madariaga, Javier I. North Carolina State Univ Dept Math Raleigh NC 27695 USA

ISBN: (纸本)9789464593617;9798331519773

We propose stochastic algorithms for solving large scale nonsmooth convex composite minimization problems. They activate at each iteration blocks of randomly selected proximity operators and achieve almost sure convergence of the iterates to a solution without any regularity assumptions. Numerical applications to data analysis problems are provided.

关键词： Convex optimization data analysis proximal splitting stochastic algorithm

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Gibbs sampling in power grid analysis 05

Gibbs sampling in power grid analysis

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10th Asia and South Pacific Design Automation Conference

作者： Tian, Zhixin Yang, Huazhong Luo, Rong Tsinghua Univ Dept Elect Engn Beijing 100084 Peoples R China

ISBN: (纸本)0780387368

Power grid plays an important role in determining circuit performance, and the accuracy and efficiency of power grid analysis algorithm has become critical in timing, power and noise estimation of modern integrated circuits. In this paper a stochastic algorithm based on Gibbs sampling is proposed to solve the problem of power grid analysis, and the test results shows that it reaches a good accuracy with linear complexity. The method has incremental property of localizing computation, a desirable property favoring in modern CAD. Therefore it can be embedded at all the design and verification levels of integrated circuits.

关键词： integrated circuit design circuit CAD circuit complexity stochastic processes Gibbs sampling power grid analysis circuit performance power estimation noise estimation integrated circuits stochastic algorithm linear complexity CAD

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Unbalanced multi-drawing urn with random addition matrix

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Arab Journal of Mathematical Sciences 2019年第1-2期26卷 57-74页

作者： Rafik, Aguech Olfa, Selmi Department of Statistics and Operation Research King Saud University Riyadh Saudi Arabia University of Monastir Monastir Tunisia

In this paper, we consider a two color multi-drawing urn model. At each discrete time step, we draw uniformly at random a sample of (Formula presented.) balls (Formula presented.) and note their color, they will be returned to the urn together with a random number of balls depending on the sample’s composition. The replacement rule is a 2 × 2 matrix depending on bounded discrete positive random variables. Using a stochastic approximation algorithm and martingales methods, we investigate the asymptotic behavior of the urn after many draws. © 2019, Aguech Rafik and Selmi Olfa.

关键词： Central limit theorem Martingale stochastic algorithm Unbalanced urn

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Multi-Objective Optimization of Additive Manufacturing Process

Multi-Objective Optimization of Additive Manufacturing Proce...

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16th IFAC Symposium on Information Control Problems in Manufacturing (INCOM)

作者： Asadollahi-Yazdi, Elnaz Gardan, Julien Lafon, Pascal Univ Technol Troyes CNRS UMR 6281 ICDLASMIS Troyes France EPF Engn Sch Rosiere Pres Troyes France

This paper presents an approach to analyze the critical drawbacks and attributes of Additive Manufacturing (AM) simultaneously to find the best manufacturing parameters to fabricate the AM products. In this study, Fused Deposition Modeling (FDM) is investigated as a common AM technology. For this purpose, a multi-optimization problem is formulated according to the analysis of FDM technology. In this problem, layer thickness and part orientation are determined as the decision variables which are the important parameters of manufacturing. As objective functions, production time and material mass are considered and the surface roughness of FDM products and mechanical behavior of material are defined as the constraint functions. Different methodologies are developed to model the AM criteria according to these decision variables. To find the optimal solutions for manufacturing, Non-Dominated Sorting Genetic algorithm-II (NSGA-II) is used. Finally, a case study highlighted the reliability of the proposed approach. (C) 2018, IFAC (International Federation of Automatic Control) Hosting by Elsevier Ltd. All rights reserved.

关键词： Multi-Objective Optimization Problem Fused Deposition Modeling Additive Manufacturing Non-Dominated Sorting Genetic algorithm-II stochastic algorithm

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An improved Tabu search for the global optimizations of electromagnetic devices

An improved Tabu search for the global optimizations of elec...

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9th Biennial IEEE Conference on Electromagnetic Field Computation (CEFC)

作者： Ho, SL Yang, SY Ni, GZ Wong, HC Hong Kong Polytech Univ EE Dept Hong Kong Hong Kong Peoples R China Hong Kong Polytech Univ Ind Ctr Hong Kong Hong Kong Peoples R China

An extended Tabu algorithm with an aspiration factor is proposed. The algorithm is based on the success of techniques such as the memorization of the previously visited subspaces, the systematic diversification as well as the intensification process for neighborhood creations. The numerical results obtained by solving a mathematical test function and the benchmark problem 22 of the TEAM Workshop reported in this paper will demonstrate the usefulness of the proposed method.

关键词： global optimization inverse problem optimal design stochastic algorithm tabu search method

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Recognition of binary images from three signatures

Recognition of binary images from three signatures

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Conference on Mathematical Modeling and Estimation Techniques in Computer Vision

作者： Herman, GT Vardi, A Matej, S Univ Penn Dept Radiol Med Image Proc Grp Philadelphia PA 19104 USA

ISBN: (纸本)0819429120

A signature of a binary image contains, for each of a set of parallel lines, the total number of 1s (as opposed to 0s) along that line. We have been studying the recognition of binary images from three signatures. Typically a very large number of binary images would satisfy three signatures. To overcome this difficulty, we have investigated the possibility of modelling the class of binary images of a particular application area as a Markov random field (MRF) and using a stochastic algorithm which seeks to optimize a functional which, in addition to a penalty term for the violation of the signatures, contains a regularization term indicating the likelihood provided by the MRF. We have found that for some MRFs (specified by small regions of the image, which are either uniform or contain edges or corners), the method works remarkably well: binary images randomly selected from the MRF were recovered within one location in nearly all cases we have tried. The time-consuming nature of the stochastic algorithm is ameliorated by a preprocessing step which discovers locations at which the value is the same in all images having the given signatures;this reduces the search space considerably We discuss, in particular, a linear-programming approach to finding such "invariant" locations.

关键词： recognition Markov random field regularization stochastic algorithm

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A Novel CMA-ES with an Eigen Coordinates Framework for Parameter Identification in Photovoltaic Models

A Novel CMA-ES with an Eigen Coordinates Framework for Param...

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IEEE Congress on Evolutionary Computation (IEEE CEC)

作者： Wang, Xiaofei Zhao, Hui Wei, Zhenglei Liang, Yajun Li, Yintong Han, Tong Air Force Engn Univ Xian Shaanxi Peoples R China

ISBN: (纸本)9781728121536

The parameter estimation of a photovoltaic (PV) model that measures the change of the current-voltage dataset is a key issue for PV system applications. It is regarded as a complex multimodal optimization problem, and there have been several attempts to solve the problem in this field of research. To further address this problem in a faster and more accurate manner, we demonstrate an improved covariance matrix adaptive evolutionary strategy (CMA-ES) with novel modifications in an eigen coordinates framework named EC-CMA-ES. The original CMA-ES suffers from premature convergence and has poor exploration performance. Thus, we propose an eigenvalue adjustment strategy on a covariance matrix in order to drive evolution towards the dominant domain by adjusting its eigenvalues. Moreover, we perform a local search strategy in a later stage by utilizing the neglected inferior solutions to enrich the population diversity. We apply EC-CMAES to address the parameter identification of three commonly used PV models. The statistical results and comparisons with other state-of-the-art algorithms demonstrate the competitive performance of our modified CMA-ES in terms of efficiency and accuracy.

关键词： stochastic algorithm parameters estimation photovoltaic model CMA-ES eigen coordinate

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APC-SCA: A Fully-Parallel Annealing algorithm with Autonomous Pinning Effect Control 36

APC-SCA: A Fully-Parallel Annealing Algorithm with Autonomou...

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36th IEEE International Parallel and Distributed Processing Symposium (IEEE IPDPS)

作者： Okonogi, Daiki Jimbo, Satoru Ando, Kota Thiem Van Chu Yu, Jaehoon Motomura, Masato Kawamura, Kazushi Tokyo Inst Technol AI Comp Res Unit Yokohama Kanagawa Japan

ISBN: (纸本)9781665497473

Annealing computation has recently attracted attention as it can efficiently solve various combinatorial optimization problems using an Ising model. stochastic cellular automata annealing (SCA) is a promising algorithm that can realize fast spin-update by utilizing its parallel computing capability. However, in SCA, preparing an appropriate control of the pinning parameter is a hard task, which degrades its usability. This paper proposes a novel approach called APC-SCA (Autonomous Pinning effect Control SCA) where the spin pinning parameter can be controlled autonomously by observing individual spin flips. The evaluation results using max-cut and N-queen problems demonstrate that the proposed approach can obtain better solutions than the conventional approach with a grid search of optimal pinning parameter control.

关键词： combinatorial optimization cellular automata stochastic algorithm parallel annealing Ising model

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Large-Scale Joint Topic, Sentiment & User Preference Analysis for Online Reviews 5

Large-Scale Joint Topic, Sentiment & User Preference Analysi...

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IEEE International Conference on Big Data (IEEE Big Data)

作者： Yu, Xinli Chen, Zheng Yang, Wei-Shih Hu, Xiaohua Yan, Erjia Li, Guangrong Temple Univ Dept Math Philadelphia PA 19122 USA Drexel Univ Coll Comp & Informat Philadelphia PA 19104 USA Hunan Univ Sch Business Changsha Hunan Peoples R China

ISBN: (纸本)9781538627150

This paper presents a non-trivial reconstruction of a previous joint topic-sentiment-preference review model TSPRA with stick-breaking representation under the framework of variational inference (VI) and stochastic variational inference (SVI). TSPRA is a Gibbs Sampling based model that solves topics, word sentiments and user preferences altogether and has been shown to achieve good performance, but for large dataset it can only learn from a relatively small sample. We develop the variational models vTSPRA and svTSPRA to improve the time use, and our new approach is capable of processing millions of reviews. We rebuild the generative process, improve the rating regression, solve and present the coordinate-ascent updates of variational parameters, and show the time complexity of each iteration is theoretically linear to the corpus size, and the experiments on Amazon datasets show it converges faster than TSPRA and attains better results given the same amount of time. In addition, we tune svTSPRA into an online algorithm ovTSPRA that can monitor oscillations of sentiment and preference overtime. Some interesting fluctuations are captured and possible explanations are provided. The results give strong visual evidence that user preference is better treated as an independent factor from sentiment.

关键词： topic model online reviews variational inference stochastic algorithm sentiment user preference

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