Relevant bibliographies by topics / Penalty Decomposition

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Book chapters
  4. Conference papers
  5. Reports

Academic literature on the topic 'Penalty Decomposition'

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Published: 10 March 2023

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Journal articles on the topic "Penalty Decomposition"

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

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Lu, Zhaosong, Yong Zhang, and Xiaorui Li. "Penalty decomposition methods for rank minimization." Optimization Methods and Software 30, no. 3 (August 8, 2014): 531–58. http://dx.doi.org/10.1080/10556788.2014.936438.

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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 (December 11, 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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Lee, Ju Hwan and 윤자영. "Wage Penalty and Decomposition of Care Employment." Korean Journal of Social Welfare Studies 46, no. 4 (December 2015): 33–57. http://dx.doi.org/10.16999/kasws.2015.46.4.33.

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Wang, Caihua, Juan Liu, Wenwen Min, and Aiping Qu. "A Novel Sparse Penalty for Singular Value Decomposition." Chinese Journal of Electronics 26, no. 2 (March 1, 2017): 306–12. http://dx.doi.org/10.1049/cje.2017.01.025.

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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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Ouellet, Yanick, and Claude-Guy Quimper. "The SoftCumulative Constraint with Quadratic Penalty." Proceedings of the AAAI Conference on Artificial Intelligence 36, no. 4 (June 28, 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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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 (June 2019): 958–62. http://dx.doi.org/10.1109/lcomm.2019.2911277.

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Dissertations / Theses on the topic "Penalty Decomposition"

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Lapucci, Matteo, and Marco Sciandrone. "Theory and algorithms for sparsity constrained optimization problems." Doctoral thesis, 2022. http://hdl.handle.net/2158/1258429.

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This dissertation is concerned with mathematical optimization problems where a sparsity constraint appears. The sparsity of the solution is a valu- able requirement in many applications of operations research. Several classes of very different approaches have been proposed in the literature for this sort of problems; when the objective function is nonconvex, in presence of difficult additional constraints or in the high-dimensional case, the problem shall be addressed as a continuous optimization task, even though it naturally has an intrinsic combinatorial nature. Within this setting, we first review the existing knowledge and the theoretical tools concerning the considered problem; we try to provide a unified view of parallel streams of research and we propose a new general stationarity condition, based on the concept of neighborhood, which somehow allows to take into account both the continuous and the combinatorial aspects of the problem. Then, after a brief overview of the main algorithmic approaches in the related literature, we propose suitable variants of some of these schemes that can be effectively employed in complex settings, such as the nonconvex one, the derivative-free one or the multi-objective one. For each of the proposed algorithms we provide a detailed convergence analysis showing that these methods enjoy important theoretical guarantees, in line with the state-of-the-art algorithms. Afterwards, exploiting the newly introduced concept of stationarity, we propose a completely novel algorithmic scheme that, combining continuous local searches and discrete moves, can be proved to guarantee stronger theoretical properties than most approaches from the literature and to exhibit strong exploration capabilities in a global optimization perspective. All the proposed algorithms have finally been experimentally tested on a benchmark of relevant problems from machine learning and decision science applications. The computational results show the actual quality of the proposed methods when practically employed.
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Zehtabian, Shohre. "Development of new scenario decomposition techniques for linear and nonlinear stochastic programming." Thèse, 2016. http://hdl.handle.net/1866/16182.

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Une approche classique pour traiter les problèmes d’optimisation avec incertitude à deux- et multi-étapes est d’utiliser l’analyse par scénario. Pour ce faire, l’incertitude de certaines données du problème est modélisée par vecteurs aléatoires avec des supports finis spécifiques aux étapes. Chacune de ces réalisations représente un scénario. En utilisant des scénarios, il est possible d’étudier des versions plus simples (sous-problèmes) du problème original. Comme technique de décomposition par scénario, l’algorithme de recouvrement progressif est une des méthodes les plus populaires pour résoudre les problèmes de programmation stochastique multi-étapes. Malgré la décomposition complète par scénario, l’efficacité de la méthode du recouvrement progressif est très sensible à certains aspects pratiques, tels que le choix du paramètre de pénalisation et la manipulation du terme quadratique dans la fonction objectif du lagrangien augmenté. Pour le choix du paramètre de pénalisation, nous examinons quelques-unes des méthodes populaires, et nous proposons une nouvelle stratégie adaptive qui vise à mieux suivre le processus de l’algorithme. Des expériences numériques sur des exemples de problèmes stochastiques linéaires multi-étapes suggèrent que la plupart des techniques existantes peuvent présenter une convergence prématurée à une solution sous-optimale ou converger vers la solution optimale, mais avec un taux très lent. En revanche, la nouvelle stratégie paraît robuste et efficace. Elle a convergé vers l’optimalité dans toutes nos expériences et a été la plus rapide dans la plupart des cas. Pour la question de la manipulation du terme quadratique, nous faisons une revue des techniques existantes et nous proposons l’idée de remplacer le terme quadratique par un terme linéaire. Bien que qu’il nous reste encore à tester notre méthode, nous avons l’intuition qu’elle réduira certaines difficultés numériques et théoriques de la méthode de recouvrement progressif.
In the literature of optimization problems under uncertainty a common approach of dealing with two- and multi-stage problems is to use scenario analysis. To do so, the uncertainty of some data in the problem is modeled by stage specific random vectors with finite supports. Each realization is called a scenario. By using scenarios, it is possible to study smaller versions (subproblems) of the underlying problem. As a scenario decomposition technique, the progressive hedging algorithm is one of the most popular methods in multi-stage stochastic programming problems. In spite of full decomposition over scenarios, progressive hedging efficiency is greatly sensitive to some practical aspects, such as the choice of the penalty parameter and handling the quadratic term in the augmented Lagrangian objective function. For the choice of the penalty parameter, we review some of the popular methods, and design a novel adaptive strategy that aims to better follow the algorithm process. Numerical experiments on linear multistage stochastic test problems suggest that most of the existing techniques may exhibit premature convergence to a sub-optimal solution or converge to the optimal solution, but at a very slow rate. In contrast, the new strategy appears to be robust and efficient, converging to optimality in all our experiments and being the fastest in most of them. For the question of handling the quadratic term, we review some existing techniques and we suggest to replace the quadratic term with a linear one. Although this method has yet to be tested, we have the intuition that it will reduce some numerical and theoretical difficulties of progressive hedging in linear problems.
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Kundu, Madan Gopal. "Advanced Modeling of Longitudinal Spectroscopy Data." Thesis, 2014. http://hdl.handle.net/1805/5454.

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Indiana University-Purdue University Indianapolis (IUPUI)
Magnetic resonance (MR) spectroscopy is a neuroimaging technique. It is widely used to quantify the concentration of important metabolites in a brain tissue. Imbalance in concentration of brain metabolites has been found to be associated with development of neurological impairment. There has been increasing trend of using MR spectroscopy as a diagnosis tool for neurological disorders. We established statistical methodology to analyze data obtained from the MR spectroscopy in the context of the HIV associated neurological disorder. First, we have developed novel methodology to study the association of marker of neurological disorder with MR spectrum from brain and how this association evolves with time. The entire problem fits into the framework of scalar-on-function regression model with individual spectrum being the functional predictor. We have extended one of the existing cross-sectional scalar-on-function regression techniques to longitudinal set-up. Advantage of proposed method includes: 1) ability to model flexible time-varying association between response and functional predictor and (2) ability to incorporate prior information. Second part of research attempts to study the influence of the clinical and demographic factors on the progression of brain metabolites over time. In order to understand the influence of these factors in fully non-parametric way, we proposed LongCART algorithm to construct regression tree with longitudinal data. Such a regression tree helps to identify smaller subpopulations (characterized by baseline factors) with differential longitudinal profile and hence helps us to identify influence of baseline factors. Advantage of LongCART algorithm includes: (1) it maintains of type-I error in determining best split, (2) substantially reduces computation time and (2) applicable even observations are taken at subject-specific time-points. Finally, we carried out an in-depth analysis of longitudinal changes in the brain metabolite concentrations in three brain regions, namely, white matter, gray matter and basal ganglia in chronically infected HIV patients enrolled in HIV Neuroimaging Consortium study. We studied the influence of important baseline factors (clinical and demographic) on these longitudinal profiles of brain metabolites using LongCART algorithm in order to identify subgroup of patients at higher risk of neurological impairment.
Partial research support was provided by the National Institutes of Health grants U01-MH083545, R01-CA126205 and U01-CA086368
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Book chapters on the topic "Penalty Decomposition"

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Spiridonov, Kirill, Sergei Sidorov, and Michael Pleshakov. "Weak Penalty Decomposition Algorithm for Sparse Optimization in High Dimensional Space." In Communications in Computer and Information Science, 215–26. Cham: Springer Nature Switzerland, 2022. http://dx.doi.org/10.1007/978-3-031-24145-1_18.

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Lee, Chang-Ock, and Eun-Hee Park. "A Domain Decomposition Method Based on Augmented Lagrangian with a Penalty Term." In Lecture Notes in Computational Science and Engineering, 339–46. Berlin, Heidelberg: Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-02677-5_38.

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Prokopyshyn, Ihor I., Ivan I. Dyyak, Rostyslav M. Martynyak, and Ivan A. Prokopyshyn. "Penalty Robin-Robin Domain Decomposition Schemes for Contact Problems of Nonlinear Elasticity." In Lecture Notes in Computational Science and Engineering, 647–54. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-35275-1_77.

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Brenner, Susanne C., Eun-Hee Park, Li-Yeng Sung, and Kening Wang. "A Balancing Domain Decomposition by Constraints Preconditioner for a C0 Interior Penalty Method." In Lecture Notes in Computational Science and Engineering, 342–49. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-56750-7_39.

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Lee, Chang-Ock, and Eun-Hee Park. "A Domain Decomposition Method Based on Augmented Lagrangian with an Optimized Penalty Parameter." In Lecture Notes in Computational Science and Engineering, 567–75. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-18827-0_58.

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Parker, David, Martin Walker, Luís Silva Azevedo, Yiannis Papadopoulos, and Rui Esteves Araújo. "Automatic Decomposition and Allocation of Safety Integrity Levels Using a Penalty-Based Genetic Algorithm." In Recent Trends in Applied Artificial Intelligence, 449–59. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-38577-3_46.

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Li, Zhiyong, Ke Lin, Mourad Nouioua, and Shilong Jiang. "A Decomposition Based Evolutionary Algorithm with Angle Penalty Selection Strategy for Many-Objective Optimization." In Lecture Notes in Computer Science, 561–71. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-93815-8_53.

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Pleshakov, Michael, Sergei Sidorov, and Kirill Spiridonov. "Convergence Analysis of Penalty Decomposition Algorithm for Cardinality Constrained Convex Optimization in Hilbert Spaces." In Mathematical Optimization Theory and Operations Research, 141–53. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-49988-4_10.

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de Sampaio, Raimundo J. B., Rafael R. G. Wollmann, Jin Yun Yuan, and Fábio Favaretto. "Using Penalty in Mathematical Decomposition for Production-Planning to Accommodate Clearing Function Constraints of Capacity." In Springer Proceedings in Mathematics & Statistics, 137–49. Berlin, Heidelberg: Springer Berlin Heidelberg, 2014. http://dx.doi.org/10.1007/978-3-662-43404-8_7.

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Lee, Chang-Ock, and Eun-Hee Park. "A Domain Decomposition Method Based on Augmented Lagrangian with a Penalty Term in Three Dimensions." In Lecture Notes in Computational Science and Engineering, 399–406. Berlin, Heidelberg: Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-11304-8_46.

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Conference papers on the topic "Penalty Decomposition"

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Ulfarsson, M. O., V. Solo, and G. Marjanovic. "Sparse and low rank decomposition using l0 penalty." In ICASSP 2015 - 2015 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2015. http://dx.doi.org/10.1109/icassp.2015.7178584.

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Shi, Qingjiang, and Mingyi Hong. "Penalty dual decomposition method with application in signal processing." In 2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2017. http://dx.doi.org/10.1109/icassp.2017.7952919.

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Kanatsoulis, Charilaos I., Xiao Fu, Nicholas D. Sidiropoulos, and Mingyi Hong. "Large-Scale Regularized Sumcor GCCA via Penalty-Dual Decomposition." In ICASSP 2018 - 2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2018. http://dx.doi.org/10.1109/icassp.2018.8462354.

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Yu, Deli, Peipei Yang, and Cheng-Lin Liu. "Learning-based Tensor Decomposition with Adaptive Rank Penalty for CNNs Compression." In 2021 IEEE 4th International Conference on Multimedia Information Processing and Retrieval (MIPR). IEEE, 2021. http://dx.doi.org/10.1109/mipr51284.2021.00014.

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Patrascu, Andrei, and Ion Necoara. "Penalty decomposition method for solving ℓ0 regularized problems: Application to trend filtering." In 2014 18th International Conference on System Theory, Control and Computing (ICSTCC). IEEE, 2014. http://dx.doi.org/10.1109/icstcc.2014.6982506.

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Lin, Wei-Chih, Chan-Yun Yang, Gene Eu Jan, and Jr-Syu Yang. "Penalty and margin decomposition - an inspection of loss function regularization in SVM." In 2015 IEEE 12th International Conference on Networking, Sensing and Control (ICNSC). IEEE, 2015. http://dx.doi.org/10.1109/icnsc.2015.7116068.

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Lu, Shen, and Harrison M. Kim. "A Regularized Inexact Penalty Decomposition Algorithm for Multidisciplinary Design Optimization Problem With Complementarity Constraints." In ASME 2009 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. ASMEDC, 2009. http://dx.doi.org/10.1115/detc2009-87278.

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Economic and physical considerations often lead to equilibrium problems in multidisciplinary design optimization (MDO), which can be captured by MDO problems with complementarity constraints (MDO-CC) — a newly emerging class of problem. Due to the ill-posedness associated with the complementarity constraints, many existing MDO methods may have numerical difficulties solving the MDO-CC. In this paper, we propose a new decomposition algorithm for MDO-CC based on the regularization technique and inexact penalty decomposition. The algorithm is presented such that existing proofs can be extended, under certain assumptions, to show that it converges to stationary points of the original problem and that it converges locally at a superlinear rate. Numerical computation with an engineering design example and several analytical example problems shows promising results with convergence to the all-in-one (AIO) solution.
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Guo, Jinglei, Miaomiao Shao, Shouyong Jiang, and Shengxiang Yang. "An improved multiobjective optimization evolutionary algorithm based on decomposition with hybrid penalty scheme." In GECCO '20: Genetic and Evolutionary Computation Conference. New York, NY, USA: ACM, 2020. http://dx.doi.org/10.1145/3377929.3389958.

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Quoc Tran Dinh, Ion Necoara, and Moritz Diehl. "A dual decomposition algorithm for separable nonconvex optimization using the penalty function framework." In 2013 IEEE 52nd Annual Conference on Decision and Control (CDC). IEEE, 2013. http://dx.doi.org/10.1109/cdc.2013.6760235.

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Xu, Meng, Georges Fadel, and Margaret M. Wiecek. "Dual Residual in Augmented Lagrangian Coordination for Decomposition-Based Optimization." In ASME 2014 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. American Society of Mechanical Engineers, 2014. http://dx.doi.org/10.1115/detc2014-35103.

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As system design problems increase in complexity, researchers seek approaches to optimize such problems by coordinating the optimizations of decomposed sub-problems. Many methods for optimization by decomposition have been proposed in the literature among which, the Augmented Lagrangian Coordination (ALC) method has drawn much attention due to its efficiency and flexibility. The ALC method involves a quadratic penalty term, and the initial setting and update strategy of the penalty weight are critical to the performance of the ALC. The weight in the traditional weight update strategy always increases and previous research shows that an inappropriate initial value of the penalty weight may cause the method not to converge to optimal solutions. Inspired by the research on Augmented Lagrangian Relaxation in the convex optimization area, a new weight update strategy in which the weight can either increase or decrease is introduced into engineering optimization. The derivation of the primal and dual residuals for optimization by decomposition is conducted as a first step. It shows that the traditional weight update strategy only considers the primal residual, which may result in a duality gap and cause a relatively big solution error. A new weight update strategy considering both the primal and dual residuals is developed which drives the dual residual to zero in the optimization process, thus guaranteeing the solution accuracy of the decomposed problem. Finally, the developed strategy is applied to both mathematical and engineering test problems and the results show significant improvements in solution accuracy. Additionally, the proposed approach makes the ALC method more robust since it allows the coordination to converge with an initial weight selected from a much wider range of possible values while the selection of initial weight is a big concern in the traditional weight update strategy.
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Reports on the topic "Penalty Decomposition"

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Multiple Engine Faults Detection Using Variational Mode Decomposition and GA-K-means. SAE International, March 2022. http://dx.doi.org/10.4271/2022-01-0616.

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As a critical power source, the diesel engine is widely used in various situations. Diesel engine failure may lead to serious property losses and even accidents. Fault detection can improve the safety of diesel engines and reduce economic loss. Surface vibration signal is often used in non-disassembly fault diagnosis because of its convenient measurement and stability. This paper proposed a novel method for engine fault detection based on vibration signals using variational mode decomposition (VMD), K-means, and genetic algorithm. The mode number of VMD dramatically affects the accuracy of extracting signal components. Therefore, a method based on spectral energy distribution is proposed to determine the parameter, and the quadratic penalty term is optimized according to SNR. The results show that the optimized VMD can adaptively extract the vibration signal components of the diesel engine. In the actual fault diagnosis case, it is difficult to obtain the data with labels. The clustering algorithm can complete the classification without labeled data, but it is limited by the low accuracy. In this paper, the optimized VMD is used to decompose and standardize the vibration signal. Then the correlation-based feature selection method is implemented to obtain the feature results after dimensionality reduction. Finally, the results are input into the classifier combined by K-means and genetic algorithm (GA). By introducing and optimizing the genetic algorithm, the number of classes can be selected automatically, and the accuracy is significantly improved. This method can carry out adaptive multiple fault detection of a diesel engine without labeled data. Compared with many supervised learning algorithms, the proposed method also has high accuracy.
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