Publications & Preprints

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Author Title [ Type(Asc)] Year
Miscellaneous
Hsu, H. - W. , Lean, M. H. , Liu, P. L. - F. , & Vavasis, S. A. . (1991). A boundary element method for three-dimensional free surface flow. Xerox.
Vavasis, S. A. . (1990). A note on wavelet bases for two-dimensional surfaces. Department of Computer Science, Cornell University.
Vavasis, S. A. , & Zippel, R. . (1990). Proving polynomial-time for sphere-constrained quadratic programming. Department of Computer Science, Cornell University.
Manuscript
Baghal, S. , Paquette, C. , & Vavasis, S. . (2020). A termination criterion for stochastic gradient descent for binary classification. Retrieved from https://arxiv.org/abs/2003.10312
Journal Article
Ang, A. , De Sterck, H. , & Vavasis, S. . (Accepted). MGProx: A nonsmooth multigrid proximal gradient method with adaptive restriction for strongly convex optimization. SIAM J. Optimization. Retrieved from https://arxiv.org/abs/2302.04077
Karimi, S. , & Vavasis, S. A. . (Accepted). Nonlinear conjugate gradient for smooth convex functions. Mathematical Programming - Computation. Retrieved from https://arxiv.org/pdf/2111.11613.pdf
Tunçel, L. , Vavasis, S. A. , & Xu, J. . (2023). Computational complexity of decomposing a symmetric matrix as a sum of positive semidefinite and diagonal matrices. Foundations of Computational Mathematics, 2023, 1-47. Retrieved from https://arxiv.org/abs/2209.05678
Doan, X. Vinh, & Vavasis, S. A. . (2022). Low-rank matrix recovery with Ky Fan 2-k-norm. Journal of Global Optimization, 82, 727-751. Retrieved from https://link.springer.com/article/10.1007/s10898-021-01031-0
Jiang, T. , & Vavasis, S. A. . (2021). Certifying clusters from sum-of-norms clustering. Retrieved from https://arxiv.org/abs/2006.11355
Vavasis, S. , Papoulia, K. , & M. Hirmand, R. . (2020). Second-order cone interior-point method for quasistatic and moderate dynamic cohesive fracture. Comput. Meth. Appl. Mech. Engr., 358, 112633. Retrieved from https://arxiv.org/abs/1909.10641
Jiang, T. , Vavasis, S. , & Zhai., C. W. . (2020). Recovery of a mixture of Gaussians by sum-of-norms clustering. Journal of Machine Learning Research, 21(225), 1-16. Retrieved from https://jmlr.org/papers/volume21/19-218/19-218.pdf
Gillis, N. , & Vavasis, S. A. . (2018). On the Complexity of Robust PCA and l1-Norm Low-Rank Matrix Approximation. Mathematics of Operations Research, 43, 1072-1084.
Karimi, S. , & Vavasis, S. . (2017). IMRO: A Proximal Quasi-Newton Method for Solving $\ell_1$-Regularized Least Squares Problems. SIAM J. Optimiz, 27, 583-615.
Doan, X. V. , & Vavasis, S. . (2016). Finding the largest low-rank clusters with Ky Fan 2-k-norm and l1-norm. SIAM J. Optim., 26, 274-312.
Gillis, N. , & Vavasis, S. A. . (2015). Semidefinite Programming Based Preconditioning for More Robust Near-Separable Nonnegative Matrix Factorization. SIAM J. Optim., 25, 677-698.
Drusvyatskiy, D. , Vavasis, S. A. , & Wolkowicz, H. . (2015). Extreme point inequalities and geometry of the rank sparsity ball. Mathematical Programming, 152, 521–544. Aug. doi:10.1007/s10107-014-0795-8
Ames, B. P. W. , & Vavasis, S. A. . (2014). Convex optimization for the planted k-disjoint-clique problem. Math. Progr., 143, 299-337.
Gillis, N. , & Vavasis, S. A. . (2013). Fast and Robust Recursive Algorithms for Separable Nonnegative Matrix Factorization. IEEE Trans. Pattern Analysis and Machine Intelligence, 36, 698-714.
Doan, X. V. , Toh, K. - C. , & Vavasis, S. . (2013). A proximal point algorithm for sequential feature extraction applications. SIAM J. Sci. Comput., A517-A540.
Doan, X. V. , & Vavasis, S. . (2013). Finding approximately rank-one submatrices with the nuclear norm and $\ell_1$ norm. SIAM J. Optimiz., 23, 2502-2540.

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