20 citations · 49 across the 16 of their papers we have counts for
7 papers · 1 filter
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…
On the convergence of adaptive first order methods: proximal gradient and alternating minimization algorithms
Puya Latafat, Andreas Themelis, Panagiotis Patrinos
Building upon recent works on linesearch-free adaptive proximal gradient methods, this paper proposes adaPG, a framework that unifies and extends existing results by provid…
Convergence of the Preconditioned Proximal Point Method and Douglas-Rachford Splitting in the Absence of Monotonicity
Brecht Evens, Pieter Pas, Puya Latafat +1
The proximal point algorithm (PPA) is the most widely recognized method for solving inclusion problems and serves as the foundation for many numerical algorithms. Despite this popu…
On the convergence of proximal gradient methods for convex simple bilevel optimization
Puya Latafat, Andreas Themelis, Silvia Villa +1
This paper studies proximal gradient iterations for solving simple bilevel optimization problems where both the upper and the lower level cost functions are split as the sum of dif…
Escaping limit cycles: Global convergence for constrained nonconvex-nonconcave minimax problems
Thomas Pethick, Puya Latafat, Panagiotis Patrinos +2
This paper introduces a new extragradient-type algorithm for a class of nonconvex-nonconcave minimax problems. It is well-known that finding a local solution for general minimax pr…
Solving stochastic weak Minty variational inequalities without increasing batch size
Thomas Pethick, Olivier Fercoq, Puya Latafat +2
This paper introduces a family of stochastic extragradient-type algorithms for a class of nonconvex-nonconcave problems characterized by the weak Minty variational inequality (MVI)…
Adaptive proximal algorithms for convex optimization under local Lipschitz continuity of the gradient
Puya Latafat, Andreas Themelis, Lorenzo Stella +1
Backtracking linesearch is the de facto approach for minimizing continuously differentiable functions with locally Lipschitz gradient. In recent years, it has been shown that in th…