10 papers
Bregman proximal gradient method for linear optimization under entropic constraints
Luis M. Briceño-Arias, Maël Le Treust
In this paper, we present an efficient algorithm for solving a linear optimization problem with entropic constraints, a class of problems that arises in game theory and information…
A Proximal Primal-Dual Approach to Generalized JKO Schemes for Doubly Nonlinear Parabolic Equations
Luis M. Briceño-Arias, José A. Carrillo, Dante Kalise +2
Variational methods based on optimization strategies are proposed to numerically solve a large family of nonlinear partial differential equations. They are all particular instances…
Optimal Leveraging of Smoothness and Strong Convexity for Peaceman--Rachford Splitting
Luis Briceño-Arias, Fernando Roldán
In this paper, we introduce a simple methodology to leverage strong convexity and smoothness in order to obtain an optimal linear convergence rate for the Peaceman--Rachford splitt…
A flexible block-coordinate forward-backward algorithm for non-smooth and non-convex optimization
Luis Briceño-Arias, Paulo Gonçalves, Guillaume Lauga +2
Block coordinate descent (BCD) methods are prevalent in large scale optimization problems due to the low memory and computational costs per iteration, the predisposition to paralle…
Restarted contractive operators to learn at equilibrium
Leo Davy, Luis M. Briceno-Arias, N. Pustelnik
Bilevel optimization offers a methodology to learn hyperparameters in imaging inverse problems, yet its integration with automatic differentiation techniques remains challenging. O…
Lyapunov analysis for FISTA under strong convexity
Luis M. Briceño-Arias, Luis M. Briceño-Arias
In this paper, we conduct a theoretical and numerical study of the Fast Iterative Shrinkage-Thresholding Algorithm (FISTA) under strong convexity assumptions. We propose an autonom…