Littérature scientifique sur le sujet « Penalty Decomposition »
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Articles de revues sur le sujet "Penalty Decomposition"
Dong, Zhengshan, Geng Lin et Niandong Chen. « An Inexact Penalty Decomposition Method for Sparse Optimization ». Computational Intelligence and Neuroscience 2021 (14 juillet 2021) : 1–8. http://dx.doi.org/10.1155/2021/9943519.
Texte intégralWang, Yuanxin. « An Adaptive Variational Mode Decomposition Technique with Differential Evolution Algorithm and Its Application Analysis ». Shock and Vibration 2021 (11 novembre 2021) : 1–5. http://dx.doi.org/10.1155/2021/2030128.
Texte intégralLu, Zhaosong, et Yong Zhang. « Sparse Approximation via Penalty Decomposition Methods ». SIAM Journal on Optimization 23, no 4 (janvier 2013) : 2448–78. http://dx.doi.org/10.1137/100808071.
Texte intégralLu, Zhaosong, Yong Zhang et Xiaorui Li. « Penalty decomposition methods for rank minimization ». Optimization Methods and Software 30, no 3 (8 août 2014) : 531–58. http://dx.doi.org/10.1080/10556788.2014.936438.
Texte intégralXu, Huaqing, Tieding Lu, Jean-Philippe Montillet et Xiaoxing He. « An Improved Adaptive IVMD-WPT-Based Noise Reduction Algorithm on GPS Height Time Series ». Sensors 21, no 24 (11 décembre 2021) : 8295. http://dx.doi.org/10.3390/s21248295.
Texte intégralLee, Ju Hwan et 윤자영. « Wage Penalty and Decomposition of Care Employment ». Korean Journal of Social Welfare Studies 46, no 4 (décembre 2015) : 33–57. http://dx.doi.org/10.16999/kasws.2015.46.4.33.
Texte intégralWang, Caihua, Juan Liu, Wenwen Min et Aiping Qu. « A Novel Sparse Penalty for Singular Value Decomposition ». Chinese Journal of Electronics 26, no 2 (1 mars 2017) : 306–12. http://dx.doi.org/10.1049/cje.2017.01.025.
Texte intégralLazarov, Raytcho D., Stanimire Z. Tomov et 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.
Texte intégralOuellet, Yanick, et Claude-Guy Quimper. « The SoftCumulative Constraint with Quadratic Penalty ». Proceedings of the AAAI Conference on Artificial Intelligence 36, no 4 (28 juin 2022) : 3813–20. http://dx.doi.org/10.1609/aaai.v36i4.20296.
Texte intégralZhao, Ming-Min, Qingjiang Shi, Yunlong Cai, Min-Jian Zhao et Quan Yu. « Decoding Binary Linear Codes Using Penalty Dual Decomposition Method ». IEEE Communications Letters 23, no 6 (juin 2019) : 958–62. http://dx.doi.org/10.1109/lcomm.2019.2911277.
Texte intégralThèses sur le sujet "Penalty Decomposition"
Lapucci, Matteo, et Marco Sciandrone. « Theory and algorithms for sparsity constrained optimization problems ». Doctoral thesis, 2022. http://hdl.handle.net/2158/1258429.
Texte intégralZehtabian, Shohre. « Development of new scenario decomposition techniques for linear and nonlinear stochastic programming ». Thèse, 2016. http://hdl.handle.net/1866/16182.
Texte intégralIn 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.
Kundu, Madan Gopal. « Advanced Modeling of Longitudinal Spectroscopy Data ». Thesis, 2014. http://hdl.handle.net/1805/5454.
Texte intégralMagnetic 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
Chapitres de livres sur le sujet "Penalty Decomposition"
Spiridonov, Kirill, Sergei Sidorov et Michael Pleshakov. « Weak Penalty Decomposition Algorithm for Sparse Optimization in High Dimensional Space ». Dans 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.
Texte intégralLee, Chang-Ock, et Eun-Hee Park. « A Domain Decomposition Method Based on Augmented Lagrangian with a Penalty Term ». Dans 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.
Texte intégralProkopyshyn, Ihor I., Ivan I. Dyyak, Rostyslav M. Martynyak et Ivan A. Prokopyshyn. « Penalty Robin-Robin Domain Decomposition Schemes for Contact Problems of Nonlinear Elasticity ». Dans 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.
Texte intégralBrenner, Susanne C., Eun-Hee Park, Li-Yeng Sung et Kening Wang. « A Balancing Domain Decomposition by Constraints Preconditioner for a C0 Interior Penalty Method ». Dans 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.
Texte intégralLee, Chang-Ock, et Eun-Hee Park. « A Domain Decomposition Method Based on Augmented Lagrangian with an Optimized Penalty Parameter ». Dans 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.
Texte intégralParker, David, Martin Walker, Luís Silva Azevedo, Yiannis Papadopoulos et Rui Esteves Araújo. « Automatic Decomposition and Allocation of Safety Integrity Levels Using a Penalty-Based Genetic Algorithm ». Dans 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.
Texte intégralLi, Zhiyong, Ke Lin, Mourad Nouioua et Shilong Jiang. « A Decomposition Based Evolutionary Algorithm with Angle Penalty Selection Strategy for Many-Objective Optimization ». Dans Lecture Notes in Computer Science, 561–71. Cham : Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-93815-8_53.
Texte intégralPleshakov, Michael, Sergei Sidorov et Kirill Spiridonov. « Convergence Analysis of Penalty Decomposition Algorithm for Cardinality Constrained Convex Optimization in Hilbert Spaces ». Dans 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.
Texte intégralde Sampaio, Raimundo J. B., Rafael R. G. Wollmann, Jin Yun Yuan et Fábio Favaretto. « Using Penalty in Mathematical Decomposition for Production-Planning to Accommodate Clearing Function Constraints of Capacity ». Dans 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.
Texte intégralLee, Chang-Ock, et Eun-Hee Park. « A Domain Decomposition Method Based on Augmented Lagrangian with a Penalty Term in Three Dimensions ». Dans 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.
Texte intégralActes de conférences sur le sujet "Penalty Decomposition"
Ulfarsson, M. O., V. Solo et G. Marjanovic. « Sparse and low rank decomposition using l0 penalty ». Dans ICASSP 2015 - 2015 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2015. http://dx.doi.org/10.1109/icassp.2015.7178584.
Texte intégralShi, Qingjiang, et Mingyi Hong. « Penalty dual decomposition method with application in signal processing ». Dans 2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2017. http://dx.doi.org/10.1109/icassp.2017.7952919.
Texte intégralKanatsoulis, Charilaos I., Xiao Fu, Nicholas D. Sidiropoulos et Mingyi Hong. « Large-Scale Regularized Sumcor GCCA via Penalty-Dual Decomposition ». Dans ICASSP 2018 - 2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2018. http://dx.doi.org/10.1109/icassp.2018.8462354.
Texte intégralYu, Deli, Peipei Yang et Cheng-Lin Liu. « Learning-based Tensor Decomposition with Adaptive Rank Penalty for CNNs Compression ». Dans 2021 IEEE 4th International Conference on Multimedia Information Processing and Retrieval (MIPR). IEEE, 2021. http://dx.doi.org/10.1109/mipr51284.2021.00014.
Texte intégralPatrascu, Andrei, et Ion Necoara. « Penalty decomposition method for solving ℓ0 regularized problems : Application to trend filtering ». Dans 2014 18th International Conference on System Theory, Control and Computing (ICSTCC). IEEE, 2014. http://dx.doi.org/10.1109/icstcc.2014.6982506.
Texte intégralLin, Wei-Chih, Chan-Yun Yang, Gene Eu Jan et Jr-Syu Yang. « Penalty and margin decomposition - an inspection of loss function regularization in SVM ». Dans 2015 IEEE 12th International Conference on Networking, Sensing and Control (ICNSC). IEEE, 2015. http://dx.doi.org/10.1109/icnsc.2015.7116068.
Texte intégralLu, Shen, et Harrison M. Kim. « A Regularized Inexact Penalty Decomposition Algorithm for Multidisciplinary Design Optimization Problem With Complementarity Constraints ». Dans ASME 2009 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. ASMEDC, 2009. http://dx.doi.org/10.1115/detc2009-87278.
Texte intégralGuo, Jinglei, Miaomiao Shao, Shouyong Jiang et Shengxiang Yang. « An improved multiobjective optimization evolutionary algorithm based on decomposition with hybrid penalty scheme ». Dans GECCO '20 : Genetic and Evolutionary Computation Conference. New York, NY, USA : ACM, 2020. http://dx.doi.org/10.1145/3377929.3389958.
Texte intégralQuoc Tran Dinh, Ion Necoara et Moritz Diehl. « A dual decomposition algorithm for separable nonconvex optimization using the penalty function framework ». Dans 2013 IEEE 52nd Annual Conference on Decision and Control (CDC). IEEE, 2013. http://dx.doi.org/10.1109/cdc.2013.6760235.
Texte intégralXu, Meng, Georges Fadel et Margaret M. Wiecek. « Dual Residual in Augmented Lagrangian Coordination for Decomposition-Based Optimization ». Dans 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.
Texte intégralRapports d'organisations sur le sujet "Penalty Decomposition"
Multiple Engine Faults Detection Using Variational Mode Decomposition and GA-K-means. SAE International, mars 2022. http://dx.doi.org/10.4271/2022-01-0616.
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