Necessary and sufficient condition for non-concave network utility maximisation

References

• Andrievsky, B. R., Fradkov, A. L., & Stotsky, A. A. (1996). Shunt compensation for indirect sliding-mode adaptive control. IFAC Proceedings Volumes, 29(1), 5132–5137. doi: 10.1016/S1474-6670(17)58495-5
   Google Scholar
• Ashour, M., Wang, J., Lagoa, C., Aybat, N., & Che, H. (2017, May 1–4). Non-concave network utility maximization: A distributed optimization approach. Proceedings of the IEEE conference on Computer Communications, Atlanta, GA (pp. 1908–1916). IEEE.
   Google Scholar
• Beck, A., Nedic, A., Ozdaglar, A., & Teboulle, M. (2014). Optimal distributed gradient methods for network resource allocation problems. IEEE Transactions on Control of Network Systems, 1(1), 64–74. doi: 10.1109/TCNS.2014.2309751
   Google Scholar
• Boyd, S., & Vandenberghe, L. (2004). Convex optimization. Cambridge: Cambridge University Press.
   Google Scholar
• Chen, J., Chiang, M., Erman, J., Li, G., Ramakrishnan, K., & Sinha, R. (2015, April 26–May 1). Fair and optimal resource allocation for LTE multicast (eMBMS): Group partitioning and dynamics. Proceedings of the IEEE conference on Computer Communications (INFOCOM), Kowloon, Hong Kong (pp. 1266–1274). IEEE.
   Google Scholar
• Fazel, M., & Chiang, M. (2005, December 15). Network utility maximization with nonconcave utilities using sum-of-squares method. Proceedings of the 44th IEEE conference on Decision and Control, Seville, Spain (pp. 1867–1874). IEEE.
   Google Scholar
• Guo, J., Luo, Y., & Li, K. (2018). Coordinated path-following and direct yaw-moment control of autonomous electric vehicles with sideslip angle estimation. Mechanical Systems and Signal Processing, 105, 183–199. doi: 10.1016/j.ymssp.2017.12.018
   Web of Science ®Google Scholar
• Guo, J., Luo, Y., & Li, K. (2019). Robust gain-scheduling automatic steering control of unmanned ground vehicles under velocity-varying motion. Vehicle System Dynamics, 57(4), 595–616. doi: 10.1080/00423114.2018.1475677
   Web of Science ®Google Scholar
• Hande, P., Zhang, S. Y., & Chiang, M. (2007). Distributed rate allocation for inelastic flows. IEEE/ACM Transactions on Networking, 15(6), 1240–1253. doi: 10.1109/TNET.2007.896507
   Web of Science ®Google Scholar
• Juluri, P., Tamarapalli, V., & Medhi, D. (2016). Measurement of quality of experience of video-on-demand services: A survey. IEEE Communications Surveys & Tutorials, 18(1), 401–418. doi: 10.1109/COMST.2015.2401424
   Web of Science ®Google Scholar
• Kelly, F., Raina, G., & Voice, T. (2008). Stability and fairness of explicit congestion control with small buffers. ACM SIGCOMM Computer Communication Review, 38(3), 51–62. doi: 10.1145/1384609.1384615
   Web of Science ®Google Scholar
• Kelly, F. P., Maulloo, A. K., & Tan, D. K. H. (1998). Rate control for communication networks: Shadow prices, proportional fairness and stability. Journal of the Operational Research Society, 49(3), 237–252. doi: 10.1057/palgrave.jors.2600523
   Web of Science ®Google Scholar
• Lagoa, C. M., Che, H., & Movsichoff, B. A. (2004). Adaptive control algorithm for dencentralized optimal traffic engineering in the internet. IEEE/ACM Transactions on Networking, 12(3), 415–428. doi: 10.1109/TNET.2004.828949
   Google Scholar
• Lee, J. W., Mazumdar, R. R., & Shroff, N. B. (2005). Non-convex optimization and rate control for multi-class services in the Internet. IEEE/ACM Transactions on Networking, 13(4), 827–840. doi: 10.1109/TNET.2005.852876
   Web of Science ®Google Scholar
• Liu, J., Eryilmaz, A., Shroff, N., & Bentley, E. (2016, April 10–14). Heavy-ball: A new approach to tame delay and convergence in wireless network optimization. Proceedings of the IEEE conference on Computer Communications (INFOCOM), San Francisco, CA (pp. 1–9). IEEE.
   Google Scholar
• Liu, Y., Zhao, Y., Ren, W., & Chen, G. (2018). Appointed-time consensus: Accurate and practical designs. Automatica, 89, 425–429. doi: 10.1016/j.automatica.2017.12.030
   Web of Science ®Google Scholar
• Movsichoff, B. A., Lagoa, C. M., & Che, H. (2005). Decentralized optimal traffic engineering in connectionless networks. IEEE Journal on Selected Areas in Communications, 23(2), 293–303. doi: 10.1109/JSAC.2004.839384
   Web of Science ®Google Scholar
• Nekouei, E., Nair, G., & Alpcan, T. (2015, December 15–18). Convergence analysis of quantized primal–dual algorithm in quadratic network utility maximization problems. Proceedings of the IEEE Conference on Decision and Control (CDC), Osaka (pp. 2655–2660). IEEE.
   Google Scholar
• Sanchez, Y., Schierl, T., Hellge, C., Wiegand, T., Hong, D., De Vleeschauwer, D., … Le Louédec, Y. (2012). Efficient HTTP-based streaming using scalable video coding. Signal Processing: Image Communication, 27(4), 329–342.
   Web of Science ®Google Scholar
• Stotsky, A. A. (1994). Combined adaptive and variable structure control. In Variable structure and lyapunov control (pp. 313–333). London: Springer-Verlag.
   Google Scholar
• Tychogiorgos, G., Gkelias, A., & Leung, K. K. (2013). A non-convex distributed optimization framework and its application to wireless ad-hoc networks. IEEE Transactions on Wireless Communications, 12(9), 4286–4296. doi: 10.1109/TW.2013.072313.120739
   Web of Science ®Google Scholar
• Utkin, V. I. (2013). Sliding modes in control and optimization. Berlin: Springer-Verlag.
   Google Scholar
• VN Index. (2017). Cisco visual networking index: Forecast and methodology, 2016–2021. Retrieved from http://www.cisco.com/c/en/us/solutions/collateral/service-provider/visual-networking-index-vni/complete-white-paper-c11-481360.pdf
   Google Scholar
• Wang, J., Ashour, M., Lagoa, C., Aybat, N., & Che, H. (2017). Non-concave network utility maximization in connectionless networks: A fully distributed traffic allocation algorithm. Proceedings of the Amercian Control Conference, Seattle, WA (pp. 3980–3985).
   Google Scholar
• Yin, X., Jindal, A., Sekar, V., & Sinopoli, B. (2015). A control-theoretic approach for dynamic adaptive video streaming over HTTP. Proceedings of the ACM conference on Special Interest Group on Data Communication, Melbourne, Australia (pp. 325–338).
   Google Scholar
• Zangwill, W. I. (1967). Non-linear programming via penalty functions. Management Science, 13(5), 344–358. doi: 10.1287/mnsc.13.5.344
   Google Scholar
• Zhao, Y., Liu, Y., Wen, G., Ren, W., & Chen, G. (2018). Edge-based finite-time protocol analysis with final consensus value and settling time estimations. IEEE Transactions on Cybernetics. doi:10.1109/TCYB.2018.2872806
   Web of Science ®Google Scholar
• Zhao, Y., Liu, Y., Wen, G., Ren, W., & Chen, G. (2019). Designing distributed specified-time consensus protocols for linear multi-agent systems over directed graphs. IEEE Transactions on Automatic Control. doi:10.1109/TAC.2018.2872534
   Web of Science ®Google Scholar
• Zhou, H., Ji, Y., Wang, X., & Zhao, B. (2015, April 26–May 1). ADMM based algorithm for eICIC configuration in heterogeneous cellular networks. Proceedings of the IEEE conference on Computer Communications (INFOCOM), Kowloon, Hong Kong (pp. 343–351). IEEE.
   Google Scholar