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An Adaptive Linesearch-free Method for Monotone Variational Inequalities under Local Lipschitz Continuity
Hongjia Ou, Andreas Themelis, Puya Latafat
The forward-reflected-backward (FRB) splitting solves inclusion problems involving the sum of a maximally monotone operator and a monotone Lipschitz continuous operator. Each itera…
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…
Safeguarding adaptive methods: global convergence of Barzilai-Borwein and other stepsize choices
Hongjia Ou, Andreas Themelis
Leveraging on recent advancements on adaptive methods for convex minimization problems, this paper provides a linesearch-free proximal gradient framework for globalizing the conver…
A mirror inertial forward-reflected-backward splitting: Global convergence and linesearch extension beyond convexity and Lipschitz smoothness
Ziyuan Wang, Andreas Themelis, Hongjia Ou +1
This work investigates a Bregman and inertial extension of the forward-reflected-backward algorithm [Y. Malitsky and M. Tam, SIAM J. Optim., 30 (2020), pp. 1451--1472] applied to s…