Publications & Preprints

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[ Author(Desc)] Title Type Year
G
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.
H
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.
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.
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.
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.
I
Iverson, V. , & Vavasis, S. . (10040). Mean and Variance Estimation Complexity in Arbitrary Distributions via Wasserstein Minimization. Retrieved from https://arxiv.org/abs/2501.10172
J
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
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.
K
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
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.
Karimi, S. , & Vavasis, S. . (2016). A unified convergence bound for conjugate gradient and accelerated gradient.

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