Publication (by topic)

Optimization landscape, dynamical system, synchronization

  • 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)

Mathematics of data science

  • 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)

  • 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)

Nonconvex optimization and mathematics of signal processing

  • 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)

  • 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)

  • 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)

  • 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)

Convex optimization and mathematics of signal processing

  • 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)

  • 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)

  • 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)

  • 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)

Numerical linear algebra

  • 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)


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