58 citations · 146 across the 28 of their papers we have counts for
15 papers · 1 filter
A closed-measure approach to stochastic approximation
Pascal Bianchi, Rodolfo Rios-Zertuche
This paper introduces a new method to tackle the issue of the almost sure convergence of stochastic approximation algorithms defined from a differential inclusion. Under the assump…
Stochastic Subgradient Descent Escapes Active Strict Saddles on Weakly Convex Functions
Pascal Bianchi, Walid Hachem, Sholom Schechtman
In non-smooth stochastic optimization, we establish the non-convergence of the stochastic subgradient descent (SGD) to the critical points recently called active strict saddles by…
Convergence Analysis of a Momentum Algorithm with Adaptive Step Size for Non Convex Optimization
Anas Barakat, Pascal Bianchi
Although ADAM is a very popular algorithm for optimizing the weights of neural networks, it has been recently shown that it can diverge even in simple convex optimization examples.…
A Fully Stochastic Primal-Dual Algorithm
Pascal Bianchi, Walid Hachem, Adil Salim
A new stochastic primal--dual algorithm for solving a composite optimization problem is proposed. It is assumed that all the functions/operators that enter the optimization problem…
A Constant Step Stochastic Douglas-Rachford Algorithm with Application to Non Separable Regularizations
Adil Salim, Pascal Bianchi, Walid Hachem
The Douglas Rachford algorithm is an algorithm that converges to a minimizer of a sum of two convex functions. The algorithm consists in fixed point iterations involving computatio…
Snake: a Stochastic Proximal Gradient Algorithm for Regularized Problems over Large Graphs
Adil Salim, Pascal Bianchi, Walid Hachem
A regularized optimization problem over a large unstructured graph is studied, where the regularization term is tied to the graph geometry. Typical regularization examples include…