Publications & Preprints

Search
Author Title Type [ Year(Desc)]
2017
Karimi, S. , & Vavasis, S. . (2017). A single potential governing convergence of conjugate gradient, accelerated gradient and geometric descent.
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.
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.
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
2021
Jiang, T. , & Vavasis, S. A. . (2021). Certifying clusters from sum-of-norms clustering. Retrieved from https://arxiv.org/abs/2006.11355
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
2023
Moursi, W. , Pavlovic, V. , & Vavasis, S. . (2023). Accelerated gradient descent: A guaranteed bound for a heuristic restart strategy. Retrieved from https://arxiv.org/abs/2310.07674
Jiang, T. , Moursi, W. , & Vavasis, S. . (2023). Range of the displacement operator of PDHG with applications to quadratic and conic programming. Retrieved from https://arxiv.org/abs/2309.15009
Jiang, T. , Tang, S. , & Vavasis, S. . (2023). Re-embedding data to strengthen recovery guarantees of clustering. Retrieved from https://arxiv.org/abs/2301.10901
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
2024
Hough, M. , & Vavasis, S. . (2024). A Primal-Dual Frank-Wolfe Algorithm for Linear Programming. Retrieved from https://arxiv.org/abs/2402.18514
Accepted
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

Pages