Publications & Preprints

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Author Title Type [ Year(Asc)]
Submitted
Tunçel, L. , Vavasis, S. A. , & Xu, J. . (Submitted). Computational complexity of decomposing a symmetric matrix as a sum of positive semidefinite and diagonal matrices. Foundations of Computational Mathematics. Retrieved from https://arxiv.org/abs/2209.05678
Karimi, S. , & Vavasis, S. A. . (Submitted). Nonlinear conjugate gradient for smooth convex functions. Mathematical Programming - Computation. Retrieved from https://arxiv.org/pdf/2111.11613.pdf
2022
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
2021
Jiang, T. , & Vavasis, S. A. . (2021). Certifying clusters from sum-of-norms clustering. Retrieved from https://arxiv.org/abs/2006.11355
2020
Majmudar, J. , & Vavasis, S. . (2020). Provable overlapping community detection in weighted graphs. In Neural Information Processing Systems (NeurIPS) (Vol. 2020). Retrieved from https://proceedings.neurips.cc/paper/2020
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
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
2019
Doan, X. V. , & Vavasis, S. . (2019). Low-rank matrix recovery with Ky Fan 2-k-norm. In Optimization of Complex Systems: Theory, Models and Applications (pp. 310-319).
Paquette, C. , & Vavasis, S. . (2019). Potential-based analyses of first-order methods for constrained and composite optimization.
2018
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.
Vavasis, S. , Papoulia, K. , & Hirmand, M. . (2018). Second-order cone interior-point method for quasistatic and moderate dynamic cohesive fracture.
2017
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.
Karimi, S. , & Vavasis, S. . (2017). A single potential governing convergence of conjugate gradient, accelerated gradient and geometric descent.
2016
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.
Karimi, S. , & Vavasis, S. . (2016). A unified convergence bound for conjugate gradient and accelerated gradient.
2015
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
Gillis, N. , & Vavasis, S. A. . (2015). Semidefinite Programming Based Preconditioning for More Robust Near-Separable Nonnegative Matrix Factorization. SIAM J. Optim., 25, 677-698.
2014
Ames, B. P. W. , & Vavasis, S. A. . (2014). Convex optimization for the planted k-disjoint-clique problem. Math. Progr., 143, 299-337.
2013
Elkin, L. , Pong, T. Kei, & Vavasis, S. . (2013). Convex relaxation for finding planted influential nodes in a social network.

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