28 citations · 71 across the 18 of their papers we have counts for
5 papers · 1 filter
Gradient correlation is a key ingredient to accelerate SGD with momentum
Julien Hermant, Marien Renaud, Jean-François Aujol +2
Empirically, it has been observed that adding momentum to Stochastic Gradient Descent (SGD) accelerates the convergence of the algorithm. However, the literature has been rather pe…
Towards optimal algorithms for the recovery of low-dimensional models with linear rates
Yann Traonmilin, Jean François Aujol, Antoine Guennec
We consider the problem of recovering elements of a low-dimensional model from linear measurements. From signal and image processing to inverse problems in data science, this quest…
Strong Convergence of FISTA Iterates under H{ö}lderian and Quadratic Growth Conditions
Jean-François Aujol, Charles Dossal, Hippolyte Labarrière +1
Introduced by Beck and Teboulle, FISTA (for Fast Iterative Shrinkage-Thresholding Algorithm) is a first-order method widely used in convex optimization. Adapted from Nesterov's acc…
Heavy Ball Momentum for Non-Strongly Convex Optimization
Jean-François Aujol, Charles Dossal, Hippolyte Labarrière +1
When considering the minimization of a quadratic or strongly convex function, it is well known that first-order methods involving an inertial term weighted by a constant-in-time pa…
Projected Block Coordinate Descent for sparse spike estimation
Pierre-Jean Bénard, Yann Traonmilin, Jean François Aujol
We consider the problem of recovering off-the-grid spikes from linear measurements. The state of the art Over-Parametrized Continuous Orthogonal Matching Pursuit (OP-COMP) with Pro…