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Article Navigation >  IEEE/CAA Journal of Automatica Sinica  > 2024  > In Press, Accepted Manuscript
W. Li and P. Yi, “Semi-decentralized convex optimization on ${\cal{SO}}(3) $,” IEEE/CAA J. Autom. Sinica.. doi: 10.1109/JAS.2024.124356
Citation: W. Li and P. Yi, “Semi-decentralized convex optimization on ${\cal{SO}}(3) $,” IEEE/CAA J. Autom. Sinica.. doi: 10.1109/JAS.2024.124356
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Semi-Decentralized Convex Optimization on $ {\cal{SO}}(3)$

doi: 10.1109/JAS.2024.124356
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    • References(22)
  • [1]
    A. Nedic and A. Ozdaglar, “Distributed subgradient methods for multi-agent optimization,” IEEE Trans. Automatic Control, vol. 54, no. 1, pp. 48–61, 2009. doi: 10.1109/TAC.2008.2009515
    [2]
    F. Chen and W. Ren, “On the control of multi-agent systems: A survey,” Foundations and Trends® in Systems and Control, vol. 6, no. 4, pp. 339–499, 2019.
    [3]
    J. Hou, X. Zeng, G. Wang, J. Sun, and J. Chen, “Distributed momentum-based frank-wolfe algorithm for stochastic optimization,” IEEE/CAA J. Autom. Sinica, vol. 10, no. 3, pp. 685–699, 2022.
    [4]
    R. M. Murray, Z. Li, and S. S. Sastry, A Mathematical Introduction to Robotic Manipulation. CRC press, 2017.
    [5]
    H. Nguyen and Q.-C. Pham, “On the covariance of X in AX=XB,” IEEE Trans. Robotics, vol. 34, no. 6, pp. 1651–1658, 2018. doi: 10.1109/TRO.2018.2861905
    [6]
    Z. Zuo, C. Liu, Q.-L. Han, and J. Song, “Unmanned aerial vehicles: Control methods and future challenges,” IEEE/CAA J. Autom. Sinica, vol. 9, no. 4, pp. 601–614, 2022. doi: 10.1109/JAS.2022.105410
    [7]
    J. Thunberg, W. Song, E. Montijano, Y. Hong, and X. Hu, “Distributed attitude synchronization control of multi-agent systems with switching topologies,” Automatica, vol. 50, pp. 832–840, 2014. doi: 10.1016/j.automatica.2014.02.002
    [8]
    W. Song, J. Markdahl, S. Zhang, X. Hu, and Y. Hong, “Intrinsic reduced attitude formation with ring inter-agent graph,” Automatica, vol. 85, pp. 193–201, 2017. doi: 10.1016/j.automatica.2017.07.015
    [9]
    R. Hartley, J. Trumpf, Y. Dai, and H. Li, “Rotation averaging,” Int. J. Computer Vision, vol. 103, no. 3, pp. 267–305, 2013. doi: 10.1007/s11263-012-0601-0
    [10]
    S. M. Shah, “Distributed optimization on Riemannian manifolds for multi-agent networks,” arXiv preprint: 1711.11196, 2017.
    [11]
    B. Mishra, H. Kasai, P. Jawanpuria, and A. Saroop, “A Riemannian gossip approach to subspace learning on Grassmann manifold,” Machine Learning, vol. 108, no. 10, pp. 1783–1803, 2019. doi: 10.1007/s10994-018-05775-x
    [12]
    S. Chen, A. Garcia, M. Hong, and S. Shahrampour, “Decentralized Riemannian gradient descent on the Stiefel manifold,” in Proc. Int. Conf. Machine Learning, 2021, pp. 1594–1605.
    [13]
    L. Wang and X. Liu, “Decentralized optimization over the Stiefel manifold by an approximate augmented Lagrangian function,” IEEE Trans. Signal Processing, vol. 70, pp. 3029–3041, 2022. doi: 10.1109/TSP.2022.3182883
    [14]
    A. Nedic, A. Ozdaglar, and P. A. Parrilo, “Constrained consensus and optimization in multi-agent networks,” IEEE Trans. Automatic Control, vol. 55, no. 4, pp. 922–938, 2010. doi: 10.1109/TAC.2010.2041686
    [15]
    X. Zeng, P. Yi, and Y. Hong, “Distributed continuous-time algorithm for constrained convex optimizations via nonsmooth analysis approach,” IEEE Trans. Autom. Control, vol. 62, no. 10, pp. 5227–5233, 2016.
    [16]
    W. Song, Y. Tang, Y. Hong, and X. Hu, “Relative attitude formation control of multi-agent systems,” Int. J. Robust and Nonlinear Control, vol. 27, no. 18, pp. 4457–4477, 2017. doi: 10.1002/rnc.3803
    [17]
    P.-A. Absil, R. Mahony, and R. Sepulchre, Optimization Algorithms on Matrix Manifolds. Princeton University Press, 2009.
    [18]
    Z. Qiu, S. Liu, and L. Xie, “Distributed constrained optimal consensus of multi-agent systems,” Automatica, vol. 68, pp. 209–215, 2016. doi: 10.1016/j.automatica.2016.01.055
    [19]
    F. Alimisis, A. Orvieto, G. Becigneul, and A. Lucchi, “Momentum improves optimization on Riemannian manifolds,” in Proc. Int. Conf. Artificial Intelligence and Statistics, 2021, pp. 1351–1359.
    [20]
    D. Chowdhury and H. K. Khalil, “Fast consensus in multi-agent systems with star topology using high gain observers,” IEEE Control Systems Letters, vol. 1, no. 1, pp. 188–193, 2017. doi: 10.1109/LCSYS.2017.2712565
    [21]
    X. Sun and C. G. Cassandras, “Optimal dynamic formation control of multi-agent systems in constrained environments,” Automatica, vol. 73, pp. 169–179, 2016. doi: 10.1016/j.automatica.2016.07.028
    [22]
    G. Shi and K. H. Johansson, “Robust consensus for continuous-time multiagent dynamics,” SIAM J. Control and Optimization, vol. 51, no. 5, pp. 3673–3691, 2013. doi: 10.1137/110841308
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