Publication (by year)


  1. On the landscape of synchronization networks: a perspective from nonconvex optimization.
    S. Ling, R. Xu, A. S. Bandeira, arXiv:1809.11083, Preprint, 2018. (Preprint)(arXiv)

  2. Certifying global optimality of graph cuts via semidefinite relaxation: a performance guarantee for spectral clustering.
    S. Ling, T. Strohmer, arXiv:1806.11429, Preprint, 2018. (Preprint)(arXiv)(Slides)

Journal Publications

  1. When do birds of a feather flock together? k-means, proximity, and conic programming.
    X. Li, Y. Li, S. Ling, T. Strohmer, K. Wei, Mathematical Programming, Series A, 2018+. (Preprint)(Final)(Slides)

  2. Regularized gradient descent: a nonconvex recipe for fast joint blind deconvolution and demixing.
    S. Ling, T. Strohmer, Information and Inference: A Journal of the IMA, 2018+. (Preprint)(Final)(Slides)

  3. Rapid, robust, and reliable blind deconvolution via nonconvex optimization.
    X. Li, S. Ling, T. Strohmer, K. Wei, Applied and Computational Harmonic Analysis, 2018+. (Preprint)(Final)(Slides)

  4. Self-calibration and bilinear inverse problems via linear least squares.
    S. Ling, T. Strohmer, SIAM Journal on Imaging Sciences, Vol.11, No.1, pp.252-292, 2018. (Preprint)(Final)

  5. Blind deconvolution meets blind demixing: algorithms and performance bounds.
    S. Ling, T. Strohmer, IEEE Transactions on Information Theory, Vol.63, No.7, pp.4497 - 4520, July 2017. (Preprint)(Final)(Slides)

  6. Self-calibration and biconvex compressive sensing.
    S. Ling, T. Strohmer, Inverse Problems, Vol. 31(11): 115002, 2015. (Preprint)(Final)(Slides)
    (SIAM Student Paper Award 2017)

  7. Backward error and perturbation bounds for high order Sylvester tensor equation.
    X. Shi, Y. Wei, S. Ling, Linear and Multilinear Algebra, 61 (10), 1436-1446, 2013. (Final)

Conference Proceedings

  1. Fast blind deconvolution and blind demixing via nonconvex optimization.
    S.Ling, T.Strohmer, International Conference on Sampling Theory and Applications (SampTA), pp.114-118, 2017. (Final)

  2. You can have it all – Fast algorithms for blind deconvolution, self-calibration, and demixing.
    S.Ling, T.Strohmer, Mathematics in Imaging, MW1C.1, 2017. (Final)

  3. Simultaneous blind deconvolution and blind demixing via convex programming.
    S.Ling, T.Strohmer, 50th Asilomar Conference on Signals, Systems and Computers, pp.1223-1227, 2016. (Final)


  • Bilinear Inverse Problems: Theory, Algorithms, and Applications.
    S.Ling, University of California Davis, 2017, (Manucript)(Slides)