Publications & Preprints

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Author(Asc) Title Type Year
K
Karimi, S., & Vavasis, S.. (2017). A single potential governing convergence of conjugate gradient, accelerated gradient and geometric descent.
Karimi, S., & Vavasis, S.. (2016). A unified convergence bound for conjugate gradient and accelerated gradient.
Karimi, S., & Vavasis, S.. (2012). Detecting and correcting loss of independence in nonlinear conjugate gradient.
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Jónsson, G., & Vavasis, S.. (2005). Accurate solution of polynomial equations using Macaulay resultant matrices. Mathematics of Computation, 74, 221-262.
Jónsson, G., & Vavasis, S.. (2005). Solving polynomials with small leading coefficients. SIAM J. Matrix Analysis App., 26, 400-414.
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
Jiang, T., & Vavasis, S. A.. (2021). Certifying clusters from sum-of-norms clustering. Retrieved from https://arxiv.org/abs/2006.11355
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
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Iverson, V., & Vavasis, S.. (10040). Mean and Variance Estimation Complexity in Arbitrary Distributions via Wasserstein Minimization. Retrieved from https://arxiv.org/abs/2501.10172
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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.
Howle, V. E., & Vavasis, S. A.. (2005). An iterative method for solving complex-symmetric systems arising in electrical power modeling. SIAM J. Matrix Analysis App., 26, 1150-1178.
Howle, V. E., & Vavasis, S.. (1998). Preconditioning complex-symmetric layered systems arising in electrical power modeling. In Proceedings of the Copper Mountain Conference on Iterative Methods.
Hough, M., & Vavasis, S.. (2024). A Primal-Dual Frank-Wolfe Algorithm for Linear Programming. Retrieved from https://arxiv.org/abs/2402.18514
Hough, P., & Vavasis, S.. (1997). Complete orthogonal decomposition for weighted least squares. SIAM J. Matrix Anal. Appl., 18.
Hirsch, M. D., Papadimitriou, C. H., & Vavasis, S. A.. (1989). Exponential lower bounds for finding Brouwer fixed points. Journal of Complexity, 5, 379–416.
Hirsch, M. D., & Vavasis, S. A.. (1987). Exponential lower bounds for finding Brouwer fixed points. In Proceedings 28th Symposium on Foundations of Computer Science (p. 401–-410). IEEE Computer Society Press.
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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.
Gillis, N., & Vavasis, S. A.. (2015). Semidefinite Programming Based Preconditioning for More Robust Near-Separable Nonnegative Matrix Factorization. SIAM J. Optim., 25, 677-698.
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.

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