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Relevant bibliographies by topics / Penalty Decomposition

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Journal articles

Academic literature on the topic 'Penalty Decomposition'

Author: Grafiati

Published: 10 March 2023

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Journal articles on the topic "Penalty Decomposition"

1

Dong, Zhengshan, Geng Lin, and Niandong Chen. "An Inexact Penalty Decomposition Method for Sparse Optimization." Computational Intelligence and Neuroscience 2021 (July 14, 2021): 1–8. http://dx.doi.org/10.1155/2021/9943519.

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The penalty decomposition method is an effective and versatile method for sparse optimization and has been successfully applied to solve compressed sensing, sparse logistic regression, sparse inverse covariance selection, low rank minimization, image restoration, and so on. With increase in the penalty parameters, a sequence of penalty subproblems required being solved by the penalty decomposition method may be time consuming. In this paper, an acceleration of the penalty decomposition method is proposed for the sparse optimization problem. For each penalty parameter, this method just finds some inexact solutions to those subproblems. Computational experiments on a number of test instances demonstrate the effectiveness and efficiency of the proposed method in accurately generating sparse and redundant representations of one-dimensional random signals.

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2

Wang, Yuanxin. "An Adaptive Variational Mode Decomposition Technique with Differential Evolution Algorithm and Its Application Analysis." Shock and Vibration 2021 (November 11, 2021): 1–5. http://dx.doi.org/10.1155/2021/2030128.

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Variational mode decomposition is an adaptive nonrecursive signal decomposition and time-frequency distribution estimation method. The improper selection of the decomposition number will cause under decomposition or over decomposition, and the improper selection of the penalty factor will affect the bandwidth of modal components, so it is very necessary to look for the optimal parameter combination of the decomposition number and the penalty factor of variational mode decomposition. Hence, differential evolution algorithm is used to look for the optimization combination of the decomposition number and the penalty factor of variational mode decomposition because differential evolution algorithm has a good ability at global searching. The method is called adaptive variational mode decomposition technique with differential evolution algorithm. Application analysis and discussion of the adaptive variational mode decomposition technique with differential evolution algorithm are employed by combining with the experiment. The conclusions of the experiment are that the decomposition performance of the adaptive variational mode decomposition technique with differential evolution algorithm is better than that of variational mode decomposition.

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3

Lu, Zhaosong, and Yong Zhang. "Sparse Approximation via Penalty Decomposition Methods." SIAM Journal on Optimization 23, no. 4 (2013): 2448–78. http://dx.doi.org/10.1137/100808071.

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4

Lu, Zhaosong, Yong Zhang, and Xiaorui Li. "Penalty decomposition methods for rank minimization." Optimization Methods and Software 30, no. 3 (2014): 531–58. http://dx.doi.org/10.1080/10556788.2014.936438.

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5

Xu, Huaqing, Tieding Lu, Jean-Philippe Montillet, and Xiaoxing He. "An Improved Adaptive IVMD-WPT-Based Noise Reduction Algorithm on GPS Height Time Series." Sensors 21, no. 24 (2021): 8295. http://dx.doi.org/10.3390/s21248295.

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To improve the reliability of Global Positioning System (GPS) signal extraction, the traditional variational mode decomposition (VMD) method cannot determine the number of intrinsic modal functions or the value of the penalty factor in the process of noise reduction, which leads to inadequate or over-decomposition in time series analysis and will cause problems. Therefore, in this paper, a new approach using improved variational mode decomposition and wavelet packet transform (IVMD-WPT) was proposed, which takes the energy entropy mutual information as the objective function and uses the grasshopper optimisation algorithm to optimise the objective function to adaptively determine the number of modal decompositions and the value of the penalty factor to verify the validity of the IVMD-WPT algorithm. We performed a test experiment with two groups of simulation time series and three indicators: root mean square error (RMSE), correlation coefficient (CC) and signal-to-noise ratio (SNR). These indicators were used to evaluate the noise reduction effect. The simulation results showed that IVMD-WPT was better than the traditional empirical mode decomposition and improved variational mode decomposition (IVMD) methods and that the RMSE decreased by 0.084 and 0.0715 mm; CC and SNR increased by 0.0005 and 0.0004 dB, and 862.28 and 6.17 dB, respectively. The simulation experiments verify the effectiveness of the proposed algorithm. Finally, we performed an analysis with 100 real GPS height time series from the Crustal Movement Observation Network of China (CMONOC). The results showed that the RMSE decreased by 11.4648 and 6.7322 mm, and CC and SNR increased by 0.1458 and 0.0588 dB, and 32.6773 and 26.3918 dB, respectively. In summary, the IVMD-WPT algorithm can adaptively determine the number of decomposition modal functions of VMD and the optimal combination of penalty factors; it helps to further extract effective information for noise and can perfectly retain useful information in the original time series.

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6

Lee, Ju Hwan and 윤자영. "Wage Penalty and Decomposition of Care Employment." Korean Journal of Social Welfare Studies 46, no. 4 (2015): 33–57. http://dx.doi.org/10.16999/kasws.2015.46.4.33.

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7

Wang, Caihua, Juan Liu, Wenwen Min, and Aiping Qu. "A Novel Sparse Penalty for Singular Value Decomposition." Chinese Journal of Electronics 26, no. 2 (2017): 306–12. http://dx.doi.org/10.1049/cje.2017.01.025.

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8

Lazarov, Raytcho D., Stanimire Z. Tomov, and Panayot S. Vassilevski. "Interior Penalty Discontinuous Approximations of Elliptic Problems." Computational Methods in Applied Mathematics 1, no. 4 (2001): 367–82. http://dx.doi.org/10.2478/cmam-2001-0024.

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AbstractThis paper studies an interior penalty discontinuous approximation of elliptic problems on nonmatching grids. Error analysis, interface domain decomposition type preconditioners, as well as numerical results illustrating both discretization errors and condition number estimates of the problem and reduced forms of it are presented.

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9

Ouellet, Yanick, and Claude-Guy Quimper. "The SoftCumulative Constraint with Quadratic Penalty." Proceedings of the AAAI Conference on Artificial Intelligence 36, no. 4 (2022): 3813–20. http://dx.doi.org/10.1609/aaai.v36i4.20296.

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The Cumulative constraint greatly contributes to the success of constraint programming at solving scheduling problems. The SoftCumulative, a version of the Cumulative where overloading the resource incurs a penalty is, however, less studied. We introduce a checker and a filtering algorithm for the SoftCumulative, which are inspired by the powerful energetic reasoning rule for the Cumulative. Both algorithms can be used with classic linear penalty function, but also with a quadratic penalty function, where the penalty of overloading the resource increases quadratically with the amount of the overload. We show that these algorithms are more general than existing algorithms and vastly outperform a decomposition of the SoftCumulative in practice.

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10

Zhao, Ming-Min, Qingjiang Shi, Yunlong Cai, Min-Jian Zhao, and Quan Yu. "Decoding Binary Linear Codes Using Penalty Dual Decomposition Method." IEEE Communications Letters 23, no. 6 (2019): 958–62. http://dx.doi.org/10.1109/lcomm.2019.2911277.

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