Adaptive proximal gradient methods are universal without approximation

no code implementations • 9 Feb 2024 • Konstantinos A. Oikonomidis, Emanuel Laude, Puya Latafat, Andreas Themelis, Panagiotis Patrinos

We show that adaptive proximal gradient methods for convex problems are not restricted to traditional Lipschitzian assumptions.

On the convergence of adaptive first order methods: proximal gradient and alternating minimization algorithms

1 code implementation • 30 Nov 2023 • Puya Latafat, Andreas Themelis, Panagiotis Patrinos

Building upon recent works on linesearch-free adaptive proximal gradient methods, this paper proposes AdaPG$^{\pi, r}$, a framework that unifies and extends existing results by providing larger stepsize policies and improved lower bounds.

Escaping limit cycles: Global convergence for constrained nonconvex-nonconcave minimax problems

1 code implementation • ICLR 2022 • Thomas Pethick, Puya Latafat, Panagiotis Patrinos, Olivier Fercoq, Volkan Cevher

This paper introduces a new extragradient-type algorithm for a class of nonconvex-nonconcave minimax problems.

Solving stochastic weak Minty variational inequalities without increasing batch size

1 code implementation • 17 Feb 2023 • Thomas Pethick, Olivier Fercoq, Puya Latafat, Panagiotis Patrinos, Volkan Cevher

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

2 code implementations • 11 Jan 2023 • Puya Latafat, Andreas Themelis, Lorenzo Stella, Panagiotis Patrinos

Backtracking linesearch is the de facto approach for minimizing continuously differentiable functions with locally Lipschitz gradient.

SPIRAL: A superlinearly convergent incremental proximal algorithm for nonconvex finite sum minimization

no code implementations • 17 Jul 2022 • Pourya Behmandpoor, Puya Latafat, Andreas Themelis, Marc Moonen, Panagiotis Patrinos

We introduce SPIRAL, a SuPerlinearly convergent Incremental pRoximal ALgorithm, for solving nonconvex regularized finite sum problems under a relative smoothness assumption.

Block-coordinate and incremental aggregated proximal gradient methods for nonsmooth nonconvex problems

3 code implementations • 24 Jun 2019 • 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 allowed to be nonconvex.

Optimization and Control 90C06, 90C25, 90C26, 49J52, 49J53