Block-coordinate methods and restarting for solving extensive-form games
D Chakrabarti, J Diakonikolas… - Advances in Neural …, 2023 - proceedings.neurips.cc
Coordinate descent methods are popular in machine learning and optimization for their
simple sparse updates and excellent practical performance. In the context of large-scale …
simple sparse updates and excellent practical performance. In the context of large-scale …
Interpolation conditions for linear operators and applications to performance estimation problems
The Performance Estimation Problem methodology makes it possible to determine the exact
worst-case performance of an optimization method. In this work, we generalize this …
worst-case performance of an optimization method. In this work, we generalize this …
Random coordinate descent methods for nonseparable composite optimization
F Chorobura, I Necoara - SIAM Journal on Optimization, 2023 - SIAM
In this paper we consider large-scale composite optimization problems having the objective
function formed as a sum of two terms (possibly nonconvex); one has a (block) …
function formed as a sum of two terms (possibly nonconvex); one has a (block) …
Coordinate descent methods beyond smoothness and separability
F Chorobura, I Necoara - Computational Optimization and Applications, 2024 - Springer
This paper deals with convex nonsmooth optimization problems. We introduce a general
smooth approximation framework for the original function and apply random (accelerated) …
smooth approximation framework for the original function and apply random (accelerated) …
Coordinate projected gradient descent minimization and its application to orthogonal nonnegative matrix factorization
In this paper we consider large-scale composite nonconvex optimization problems having
the objective function formed as a sum of three terms, first has block coordinate-wise …
the objective function formed as a sum of three terms, first has block coordinate-wise …
Efficiency of stochastic coordinate proximal gradient methods on nonseparable composite optimization
I Necoara, F Chorobura - Mathematics of Operations …, 2024 - pubsonline.informs.org
This paper deals with composite optimization problems having the objective function formed
as the sum of two terms; one has a Lipschitz continuous gradient along random subspaces …
as the sum of two terms; one has a Lipschitz continuous gradient along random subspaces …
Can random proximal coordinate descent be accelerated on nonseparable convex composite minimization problems?
F Chorobura, F Glineur… - 2023 European Control …, 2023 - ieeexplore.ieee.org
In this paper we consider convex composite optimization problems, where first term is
smooth, while the second term is proximal easy but nonseparable (possibly non-smooth) …
smooth, while the second term is proximal easy but nonseparable (possibly non-smooth) …