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1مؤتمر
المؤلفون: Fu, Yuhui, Liu, Huiping, Dou, Xiaohua, Fan, Yuan
المساهمون: National Natural Science Foundation of China
المصدر: 2023 35th Chinese Control and Decision Conference (CCDC)
الإتاحة: https://doi.org/10.1109/ccdc58219.2023.10326496Test
http://xplorestaging.ieee.org/ielx7/10326447/10326467/10326496.pdf?arnumber=10326496Test -
2دورية أكاديمية
المؤلفون: Liu, Chenyang, Dou, Xiaohua, Fan, Yuan, Cheng, Songsong
مصطلحات موضوعية: keyword:quantized communication, keyword:distributed optimization, keyword:alternating direction method of multipliers (ADMM), keyword:constrained optimization, msc:90C33
وصف الملف: application/pdf
العلاقة: reference:[1] Alghunaim, S. A., Ryu, E. K., Yuan, K., Sayed, A. H.: Decentralized proximal gradient algorithms with linear convergence rates.IEEE Trans. Automat. Control 66 (2020), 6, 2787-2794. MR 4265114; reference:[2] Boyd, S., Persi, D., Xiao, L.: Fastest mixing Markov chain on a graph.SIAM Rev. 46 (2004), 4, 667-689. MR 2124681; reference:[3] Chen, Z., Liang, S.: Distributed aggregative optimization with quantized communication.Kybernetika 58 (2022), 1, 123-144. MR 4405950; reference:[4] Chen, Z., Ma, J., Liang, S., Li, L.: Distributed Nash equilibrium seeking under quantization communication.Automatica 141 (2022), 110318. MR 4409952; reference:[5] Cheng, S., Liang, S., Fan, Y., Hong, Y.: Distributed gradient tracking for unbalanced optimization with different constraint sets.IEEE Trans. Automat. Control (2022). MR 4596660; reference:[6] Dorina, T., Effrosyni, K., Pu, Y., Pascal, F.: Distributed average consensus with quantization refinement.IEEE Trans. Signal Process. 61 (2013), 1, 194-205. MR 3008630; reference:[7] Jian, L., Hu, J., Wang, J., Shi, K.: Distributed inexact dual consensus ADMM for network resource allocation.Optimal Control Appl. Methods 40 (2019), 6, 1071-1087. MR 4028355; reference:[8] Lei, J., Chen, H., Fang, H.: Primal-dual algorithm for distributed constrained optimization.Systems Control Lett. 96 (2016), 110-117. MR 3547663; reference:[9] Lei, J., Yi, P., Shi, G., Brian, D. O. A.: Distributed algorithms with finite data rates that solve linear equations.SIAM J. Optim. 30 (2020), 2, 1191-1222. MR 4091883; reference:[10] Li, X., Feng, G., Xie, L.: Distributed proximal algorithms for multi-agent optimization with coupled inequality constraints.IEEE Trans. Automat. Control 66 (2021), 3, 1223-1230. MR 4226768; reference:[11] Li, X., Gang, F., Lihua, X.: Distributed proximal point algorithm for constrained optimization over unbalanced graphs.2019 IEEE 15th International Conference on Control and Automation (ICCA), IEEE, (2019), 824-829. 10.1109/ICCA.2019.8899938; reference:[12] Li, P., Hu, J., Qiu, L., Zhao, Y., Bijoy, K. G.: A distributed economic dispatch strategy for power-water networks.IEEE Trans. Control Network Systems 9 (2022), 1, 356-366. MR 4450544; reference:[13] Li, W., Zeng, X., Liang, S., Hong, Y.: Exponentially convergent algorithm design for constrained distributed optimization via nonsmooth approach.IEEE Trans. Automat. Control 67 (2022), 2, 934-940. MR 4376129, 10.1109/TAC.2021.3075666; reference:[14] Liang, S., Wang, L., George, Y.: Exponential convergence of distributed primal-dual convex optimization algorithm without strong convexity.Automatica 105 (2019), 298-306. MR 3942714; reference:[15] Liu, Y., Wu, G., Tian, Z., Ling, Q.: DQC-ADMM: decentralized dynamic ADMM with quantized and censored communications.IEEE Trans. Neural Networks Learn. Systems 33 (2022), 8, 3290-3304. MR 4468237; reference:[16] Ma, S.: Alternating proximal gradient method for convex minimization.J. Scientific Computing 68 (2016), 2, 546-572. MR 3519192; reference:[17] Ma, X., Yi, P., Chen, J.: Distributed gradient tracking methods with finite data rates.J. Systems Science Complexity 34 (2021), 5, 1927-1952. MR 4331654; reference:[18] Pillai, S. U., Torsten, S., Seunghun, Ch.: The Perron-Frobenius theorem: some of its applications.IEEE Signal Process. Magazine 22 (2005), 2, 62-75.; reference:[19] Qiu, Z., Xie, L., Hong, Y.: Quantized leaderless and leader-following consensus of high-order multi-agent systems with limited data rate.IEEE Trans. Automat. Control 61 (2016), 9, 2432-2447. MR 3545063; reference:[20] Shi, W., Ling, Q., Yuan, K., Wu, G., Yin, W.: On the linear convergence of the ADMM in decentralized consensus optimization.IEEE Trans. Signal Process. 62 (2014), 7, 1750-1761. MR 3189404; reference:[21] Wang, C., Xu, S., Yuan, D., Zhang, B., Zhang, Z.: Distributed online convex optimization with a bandit primal-dual mirror descent push-sum algorithm.Neurocomputing 497 (2022), 204-215.; reference:[22] Wang, J., Fu, L., Gu, Y., Li, T.: Convergence of distributed gradient-tracking-based optimization algorithms with random graphs.J. Systems Science Complexity 34 (2021), 4, 1438-1453. MR 4298058; reference:[23] Wei, Y., Fang, H., Zeng, X., Chen, J., Panos, P.: A smooth double proximal primal-dual algorithm for a class of distributed nonsmooth optimization problems.IEEE Trans. Automat. Control 65 (2020), 4, 1800-1806. MR 4085556; reference:[24] Xie, X., Ling, Q., Lu, P., Xu, W., Zhu, Z.: Evacuate before too late: distributed backup in inter-DC networks with progressive disasters.IEEE Trans. Parallel Distributed Systems 29 (2018), 5, 1058-1074.; reference:[25] Xu, T., Wu, W.: Accelerated ADMM-based fully distributed inverter-based Volt/Var control strategy for active distribution networks.IEEE Trans. Industr. Inform. 16 (2020), 12, 7532-7543.; reference:[26] Yi, P., Hong, Y.: Quantized subgradient algorithm and data-rate analysis for distributed optimization.IEEE Trans. Contro Network Systems 1 (2014), 4, 380-392. MR 3303147; reference:[27] Yu, W., Liu, H., Zheng, W. Z., Zhu, Y.: Distributed discrete-time convex optimization with nonidentical local constraints over time-varying unbalanced directed graphs.Automatica 134 (2021), 11, 109899. MR 4309380; reference:[28] Yuan, D., Hong, Y., Daniel, W. C. H., Xu, S.: Distributed mirror descent for online composite optimization.IEEE Trans. Automat. 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3مؤتمر
المؤلفون: Liu, Chenyang, Dou, Xiaohua, Cheng, Songsong, Fan, Yuan
المساهمون: National Natural Science Foundation of China
المصدر: 2022 17th International Conference on Control, Automation, Robotics and Vision (ICARCV)
الإتاحة: https://doi.org/10.1109/icarcv57592.2022.10004351Test
http://xplorestaging.ieee.org/ielx7/10004205/10004219/10004351.pdf?arnumber=10004351Test -
4كتاب
المؤلفون: Dou, Xiaohua, Liu, Renzhong, Chen, Xinzheng
المصدر: Communications in Computer and Information Science ; Advances in Education and Management ; page 363-369 ; ISSN 1865-0929 1865-0937 ; ISBN 9783642230615 9783642230622
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5
المؤلفون: Liu, Chenyang, Dou, Xiaohua, Fan, Yuan, Cheng, Songsong
مصطلحات موضوعية: quantized communication, distributed optimization, alternating direction method of multipliers (ADMM), constrained optimization
جغرافية الموضوع: 392-417
وصف الملف: média; svazek
الإتاحة: https://doi.org/10.14736/kyb-2023-3-0392Test
https://kramerius.lib.cas.cz/view/uuid:6d46563b-dec7-400e-803f-812548ad430fTest
النص الكامل