5 papers
Linesearch-free adaptive Bregman proximal gradient for convex minimization under local relative smoothness
Hongjia Ou, Puya Latafat, Andreas Themelis
This paper introduces adaptive Bregman proximal gradient algorithms for solving convex composite minimization problems without relying on global relative smoothness or strong conve…
A Lyapunov analysis of Korpelevich's extragradient method with fast and flexible extensions
Manu Upadhyaya, Puya Latafat, Pontus Giselsson
We develop a Lyapunov-based analysis of Korpelevich's extragradient method and show that it achieves an last-iterate convergence rate of the constructed Lyapunov function.…
A proximal augmented Lagrangian method for nonconvex optimization with equality and inequality constraints
Adeyemi D. Adeoye, Puya Latafat, Alberto Bemporad
We propose an inexact proximal augmented Lagrangian method (P-ALM) for nonconvex structured optimization problems. The proposed method features an easily implementable rule not onl…
Spingarn's Method and Progressive Decoupling Beyond Elicitable Monotonicity
Brecht Evens, Puya Latafat, Panagiotis Patrinos
Spingarn's method of partial inverses and the progressive decoupling algorithm address inclusion problems involving the sum of an operator and the normal cone of a linear subspace,…
Convergence of the Chambolle-Pock Algorithm in the Absence of Monotonicity
Brecht Evens, Puya Latafat, Panagiotis Patrinos
The Chambolle-Pock algorithm (CPA), also known as the primal-dual hybrid gradient method, has gained popularity over the last decade due to its success in solving large-scale conve…