20 citations · 49 across the 16 of their papers we have counts for
21 papers
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…
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…
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…
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,…
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.…
Adaptive proximal gradient methods are universal without approximation
Konstantinos A. Oikonomidis, Emanuel Laude, Puya Latafat +2
We show that adaptive proximal gradient methods for convex problems are not restricted to traditional Lipschitzian assumptions. Our analysis reveals that a class of linesearch-free…
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…
SPIRAL: A superlinearly convergent incremental proximal algorithm for nonconvex finite sum minimization
Pourya Behmandpoor, Puya Latafat, Andreas Themelis +2
We introduce SPIRAL, a SuPerlinearly convergent Incremental pRoximal ALgorithm, for solving nonconvex regularized finite sum problems under a relative smoothness assumption. Each i…
Neural Network Training as an Optimal Control Problem: An Augmented Lagrangian Approach
Brecht Evens, Puya Latafat, Andreas Themelis +2
Training of neural networks amounts to nonconvex optimization problems that are typically solved by using backpropagation and (variants of) stochastic gradient descent. In this wor…
Bregman Finito/MISO for nonconvex regularized finite sum minimization without Lipschitz gradient continuity
Puya Latafat, Andreas Themelis, Masoud Ahookhosh +1
We introduce two algorithms for nonconvex regularized finite sum minimization, where typical Lipschitz differentiability assumptions are relaxed to the notion of relative smoothnes…
Block-coordinate and incremental aggregated proximal gradient methods for nonsmooth nonconvex problems
Puya Latafat, Andreas Themelis, Panagiotis Patrinos
This paper analyzes block-coordinate proximal gradient methods for minimizing the sum of a separable smooth function and a (nonseparable) nonsmooth function, both of which are allo…
Primal-dual algorithms for multi-agent structured optimization over message-passing architectures with bounded communication delays
Puya Latafat, Panagiotis Patrinos
We consider algorithms for solving structured convex optimization problems over a network of agents with communication delays. It is assumed that each agent performs its local upda…
A New Randomized Block-Coordinate Primal-Dual Proximal Algorithm for Distributed Optimization
Puya Latafat, Nikolaos M. Freris, Panagiotis Patrinos
This paper proposes TriPD, a new primal-dual algorithm for minimizing the sum of a Lipschitz-differentiable convex function and two possibly nonsmooth convex functions, one of whic…
New Primal-Dual Proximal Algorithm for Distributed Optimization
Puya Latafat, Lorenzo Stella, Panagiotis Patrinos
We consider a network of agents, each with its own private cost consisting of the sum of two possibly nonsmooth convex functions, one of which is composed with a linear operator. A…
Asymmetric Forward-Backward-Adjoint Splitting for Solving Monotone Inclusions Involving Three Operators
Puya Latafat, Panagiotis Patrinos
In this work we propose a new splitting technique, namely Asymmetric Forward-Backward-Adjoint splitting, for solving monotone inclusions involving three terms, a maximally monotone…