المؤلفون: Ma, Ji, Yang, Zheng, Chen, Ziqin

مصطلحات موضوعية: keyword:game theory, keyword:time-varying Nash equilibrium tracking, keyword:alternating direction method of multipliers, msc:68W15, msc:90C33

وصف الملف: application/pdf

Relation: mr:MR4660381; zbl:Zbl 07790653; reference:[1] Ardagna, D., Panicucci, B., Passacantando, M.: Generalized Nash equilibria for the service provisioning problem in cloud systems.IEEE Trans. Serv. Comput. 6 (2012), 429-442.; reference:[2] Bhatti, B. A., Broadwater, R.: Distributed Nash equilibrium seeking for a dynamic micro-grid energy trading game with non-quadratic payoffs.Energy. 202 (2020), 117709.; reference:[3] Cadre, H. Le, Jacquot, P., Wan, C., Alasseur, C.: Peer-to-peer electricity market analysis: From variational to generalized Nash equilibrium.Eur. J. Oper. Res., 282 (2020), 753-771. MR 4042753; 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] Persis, C. De, Grammatico, S.: Distributed averaging integral Nash equilibrium seeking on networks.Automatica 110 (2019), 1085448. MR 4001040; reference:[6] Huang, B., Yang, C., Meng, Z., Chen, F., Ren, W.: Distributed nonlinear placement for multicluster systems: A time-varying Nash equilibrium-seeking approach.IEEE Trans. Cybernet. 52 (2022), 11614-11623.; reference:[7] Li, Z., Li, Z., Ding, Z.: Distributed generalized Nash equilibrium seeking and its application to Femtocell networks.IEEE Trans. Cybern., 52 (2022), 2505-2517. MR 4486900; reference:[8] Li, X., Li, X., Hong, Y., Chen, J., Wang, L.: A survey of decentralized online learning.arxiv preprint (2022). MR 4070203; reference:[9] Ling, Q., Ribeiro, A.: Decentralized dynamic optimization through the alternating direction method of multipliers.IEEE Trans. Signal Process. 62 (2014), 1185-1197. MR 3168144; reference:[10] Lu, K., Jing, G., Wang, L.: Distributed algorithms for searching generalized Nash equilibrium of noncooperative games.IEEE Trans. Cybernet. 49 (2019), 2362-2371.; reference:[11] Lu, K., Li, H., Wang, L.: Online distributed algorithms for seeking generalized Nash equilibria in dynamic environments.IEEE Trans. Autom. Control 66 (2020), 2289-2296. MR 4250871; reference:[12] Maskery, M., Krishnamurthy, V., Zhao, Q.: Decentralized dynamic spectrum access for cognitive radios: Cooperative design of a noncooperative game.IEEE Trans. Commun. 57 (2009), 459-469.; reference:[13] Meng, M., Li, X., Hong, Y., Chen, J., Wang, L.: Decentralized online learning for noncooperative games in dynamic environments.arxiv preprint (2021).; reference:[14] Ospina, A. M., Simonetto, A., Dall'Anese, E.: Time-varying optimization of networked systems with human preferences.IEEE Trans. Control Netw. Syst. 10 (2023), 503-515. MR 4597837; reference:[15] Salehisadaghiani, F., Pavel, L.: Distributed Nash equilibrium seeking: A gossip-based algorithm.Automatica 72 (2016), 209-216. MR 3542934; reference:[16] Simonetto, A., Mokhtari, A., Koppel, A., Leus, G., Ribeiro, A.: A class of prediction-correction methods for time-varying convex optimization.IEEE Trans. Signal Process. 64 (2016), 4576-4591. MR 3530422; reference:[17] Tao, Q., Liu, Y., Xian, C., Zhao, Y.: Prescribed-time distributed time-varying Nash equilibrium seeking for formation placement control.IEEE Trans. Circuits Syst., II, Exp. Briefs 69 (2022), 4423-4427.; reference:[18] Ye, M., Hu, G.: Distributed seeking of time-varying Nash equilibrium for non-cooperative games.IEEE Trans. Autom. Control 60 (2015), 3000-3005. MR 3419589; reference:[19] Ye, M., Hu, G.: Distributed Nash equilibrium seeking by a consensus based approach.IEEE Trans. Autom. Control 62 (2017), 4811-4818. MR 3691908; reference:[20] Zeng, X., Chen, J., Liang, S., Hong, Y.: Generalized Nash equilibrium seeking strategy for distributed nonsmooth multi-cluster game.Automatica 103 (2019), 20-26. MR 3908257

الاتاحة: http://hdl.handle.net/10338.dmlcz/151854

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

Relation: 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. Control 66 (2021), 2, 714-729. MR 4210454; reference:[29] Yuan, D., Xu, S., Zhang, B., Rong, L.: Distributed primal-dual stochastic subgradient algorithms for multi-agent optimization under inequality constraints.Int. J. Robust Nonlinear Control 23 (2013), 15, 1846-1868. MR 3126782; reference:[30] Zhang, J., Liu, H., Anthony, M.-Ch. S., Man-Cho, Ling, Q.: A penalty alternating direction method of multipliers for convex composite optimization over decentralized networks.IEEE Trans. Signal Process. 69 (2021), 4282-4295. MR 4302986, 10.1109/TSP.2021.3092347; reference:[31] Zhao, X., Yi, P., Li, L.: Distributed policy evaluation via inexact ADMM in multi-agent reinforcement learning.Control Theory Technol. 18 (2020), 4, 362-378. MR 4188357; reference:[32] Zhou, H., Zeng, X., Hong, Y.: Adaptive exact penalty design for constrained distributed optimization.IEEE Trans. Automat. Control 64 (2019), 11, 4661-4667. MR 4030790

الاتاحة: http://hdl.handle.net/10338.dmlcz/151722

المؤلفون: Soheili, Ali R., Arezoomandan, Mahdieh

مصطلحات موضوعية: keyword:stochastic partial differential equation, keyword:finite difference method, keyword:alternating direction method, keyword:Saul'yev method, keyword:Liu method, keyword:convergence, keyword:consistency, keyword:stability, msc:60H15, msc:65M06, msc:65M75

وصف الملف: application/pdf

Relation: mr:MR3083523; zbl:Zbl 06221240; reference:[1] Allen, E. J., Novosel, S. J., Zhang, Z.: Finite element and difference approximation of some linear stochastic partial differential equations.Stochastics Stochastics Rep. 64 (1998), 117-142. Zbl 0907.65147, MR 1637047, 10.1080/17442509808834159; reference:[2] Ames, W. F.: Numerical Methods for Partial Differential Equations. 3. ed. Computer Science and Scientific Computing.Academic Press Boston (1992). MR 1184394; reference:[3] Campbell, L. J., Yin, B.: On the stability of alternating-direction explicit methods for advection-diffusion equations.Numer. Methods Partial Differ. Equations 23 (2007), 1429-1444. Zbl 1129.65058, MR 2355168, 10.1002/num.20233; reference:[4] Davie, A. M., Gaines, J. G.: Convergence of numerical schemes for the solution of parabolic stochastic partial differential equations.Math. Comput. 70 (2001), 121-134. Zbl 0956.60064, MR 1803132, 10.1090/S0025-5718-00-01224-2; reference:[5] Higham, D. J.: An algorithmic introduction to numerical simulation of stochastic differential equations.SIAM Rev. 43 (2001), 525-546. Zbl 0979.65007, MR 1872387, 10.1137/S0036144500378302; reference:[6] Kloeden, P. E., Platen, E.: Numerical Solution of Stochastic Differential Equations. Applications of Mathematics 23.Springer Berlin (1992). MR 1214374; reference:[7] Komori, Y., Mitsui, T.: Stable ROW-type weak scheme for stochastic differential equations.Monte Carlo Methods Appl. 1 (1995), 279-300. Zbl 0938.65535, MR 1368807, 10.1515/mcma.1995.1.4.279; reference:[8] Liu, S. L.: Stable explicit difference approximations to parabolic partial differential equations.AIChE J. 15 (1969), 334-338. 10.1002/aic.690150308; reference:[9] McDonald, S.: Finite difference approximation for linear stochastic partial differential equation with method of lines.MPRA Paper No. 3983 (2006), \\http://mpra.ub.uni-muenchen.de/3983.; reference:[10] Milstein, G. N.: Numerical Integration of Stochastic Differential Equations. Transl. from the Russian. Mathematics and its Applications 313.Kluwer Academic Publishers Dordrecht (1994). MR 1335454; reference:[11] Rößler, A.: Stochastic Taylor expansions for the expectation of functionals of diffusion processes.Stochastic Anal. Appl. 22 (2004), 1553-1576. Zbl 1065.60068, MR 2095070, 10.1081/SAP-200029495; reference:[12] Rößler, A., Seaïd, M., Zahri, M.: Method of lines for stochastic boundary-value problems with additive noise.Appl. Math. Comput. 199 (2008), 301-314. Zbl 1142.65007, MR 2415825, 10.1016/j.amc.2007.09.062; reference:[13] Roth, C.: Difference methods for stochastic partial differential equations.Z. Angew. Math. Mech. 82 (2002), 821-830. Zbl 1010.60057, MR 1944425, 10.1002/1521-4001(200211)82:11/123.0.CO;2-L; reference:[14] Roth, C.: Approximations of Solution of a First Order Stochastic Partial Differential Equation, Report.Institut Optimierung und Stochastik, Universität Halle-Wittenberg Halle (1989).; reference:[15] Saul'yev, V. K.: Integration of Equations of Parabolic Type by the Method of Nets. Translated by G. J. Tee. International Series of Monographs in Pure and Applied Mathematics Vol. 54.K.L. Stewart Pergamon Press Oxford (1964). MR 0197994; reference:[16] Saul'yev, V. K.: On a method of numerical integration of a diffusion equation.Dokl. Akad. Nauk SSSR 115 (1957), 1077-1080. MR 0142205; reference:[17] Soheili, A. R., Niasar, M. B., Arezoomandan, M.: Approximation of stochastic parabolic differential equations with two different finite difference schemes.Bull. Iran. Math. Soc. 37 (2011), 61-83. Zbl 1260.60124, MR 2890579; reference:[18] Strikwerda, J. C.: Finite difference schemes and partial differential equations. 2nd ed.Society for Industrial and Applied Mathematics Philadelphia (2004). Zbl 1071.65118, MR 2124284; reference:[19] Thomas, J. W.: Numerical Partial Differential Equations: Finite Difference Methods. Texts in Applied Mathematics 22.Springer New York (1995). MR 1367964, 10.1007/978-1-4899-7278-1_7

الاتاحة: http://hdl.handle.net/10338.dmlcz/143340