In this paper, we propose in a Hilbertian setting a second-order time-continuous dynamic
system with fast convergence guarantees to solve structured convex minimization problems …

Heterogeneous domain adaptation (HDA) aims to execute knowledge transfer from a source
domain to a heterogeneous target domain. Previous works typically inject knowledge from …

This paper proposes a two-point inertial proximal point algorithm to find zero of maximal
monotone operators in Hilbert spaces. We obtain weak convergence results and non …

Anderson acceleration is a well-established and simple technique for speeding up fixed-
point computations with countless applications. This work introduces novel methods for …

We propose a new fast algorithm to estimate any sparse generalized linear model with
convex or non-convex separable penalties. Our algorithm is able to solve problems with …

We present a strongly convergent Halpern-type proximal point algorithm with double inertial
effects to find a zero of a maximal monotone operator in Hilbert spaces. The strong …

In this work, we propose a novel class of forward‐backward‐forward algorithms for solving
monotone inclusion problems. Our approach incorporates a self‐adaptive technique to …

The alternating direction multiplier method (ADMM) is widely used in computer graphics for
solving optimization problems that can be nonsmooth and nonconvex. It converges quickly …

Empirical results show that Anderson acceleration (AA) can be a powerful mechanism to
improve the asymptotic linear convergence speed of the Alternating Direction Method of …

First-order operator splitting methods are ubiquitous among many fields through science
and engineering, such as inverse problems, signal/image processing, statistics, data …