Publications & Preprints

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Author Title Type Year(Asc)
10040
Iverson, V., & Vavasis, S.. (10040). Mean and Variance Estimation Complexity in Arbitrary Distributions via Wasserstein Minimization. Retrieved from https://arxiv.org/abs/2501.10172
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
2024
Hough, M., & Vavasis, S.. (2024). A Primal-Dual Frank-Wolfe Algorithm for Linear Programming. Retrieved from https://arxiv.org/abs/2402.18514
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
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.

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