Publications & Preprints

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Author Title [ Type(Desc)] Year
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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
Unpublished
Iverson, V. , & Vavasis, S. . (10040). Mean and Variance Estimation Complexity in Arbitrary Distributions via Wasserstein Minimization. Retrieved from https://arxiv.org/abs/2501.10172
Paquette, C. , & Vavasis, S. . (2019). Potential-based analyses of first-order methods for constrained and composite optimization.
Vavasis, S. , Papoulia, K. , & Hirmand, M. . (2018). Second-order cone interior-point method for quasistatic and moderate dynamic cohesive fracture.
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.
Elkin, L. , Pong, T. Kei, & Vavasis, S. . (2013). Convex relaxation for finding planted influential nodes in a social network.
Vavasis, S. . (2013). Some notes on applying computational divided differencing in optimization.
Karimi, S. , & Vavasis, S. . (2012). Detecting and correcting loss of independence in nonlinear conjugate gradient.
Vavasis, S. A. . (2008). A new secant method for unconstrained optimization.
Srijuntongsiri, G. , & Vavasis, S. A. . (2007). A Condition Number Analysis of a Surface-Surface Intersection Algorithm.
Srijuntongsiri, G. , & Vavasis, S. A. . (2007). Properties of polynomial bases used in line-surface intersection algorithm.
Vavasis, S. . (2006). A conjecture that the roots of a univariate polynomial lie in a union of annuli.
Srijuntongsiri, G. , & Vavasis, S. . (2004). A Fully Sparse Implementation of a Primal- Dual Interior-Point Potential Reduction Method for Semidefinite Programming.
Vavasis, S. . (2003). A Bernstein-Bézier Sufficient Condition for Invertibility of Polynomial Mappings.
Vavasis, S. . (1999). A note on efficient computation of the gradient in semidefinite programming.
Vavasis, S. A. . (1996). QMG: Software for finite-element mesh generation.

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