18 citations · 21 across the 6 of their papers we have counts for
4 papers · 1 filter
A superlinearly convergent subgradient method for sharp semismooth problems
Vasileios Charisopoulos, Damek Davis
Subgradient methods comprise a fundamental class of nonsmooth optimization algorithms. Classical results show that certain subgradient methods converge sublinearly for general Lips…
Stochastic algorithms with geometric step decay converge linearly on sharp functions
Damek Davis, Dmitriy Drusvyatskiy, Vasileios Charisopoulos
Stochastic (sub)gradient methods require step size schedule tuning to perform well in practice. Classical tuning strategies decay the step size polynomially and lead to optimal sub…
Low-rank matrix recovery with composite optimization: good conditioning and rapid convergence
Vasileios Charisopoulos, Yudong Chen, Damek Davis +3
The task of recovering a low-rank matrix from its noisy linear measurements plays a central role in computational science. Smooth formulations of the problem often exhibit an undes…
Composite optimization for robust blind deconvolution
Vasileios Charisopoulos, Damek Davis, Mateo Díaz +1
The blind deconvolution problem seeks to recover a pair of vectors from a set of rank one bilinear measurements. We consider a natural nonsmooth formulation of the problem and show…