86 citations · 685 across the 45 of their papers we have counts for
6 papers · 1 filter
A generic coordinate descent solver for nonsmooth convex optimization
Olivier Fercoq
We present a generic coordinate descent solver for the minimization of a nonsmooth convex objective with structure. The method can deal in particular with problems with linear cons…
Safe Grid Search with Optimal Complexity
Eugene Ndiaye, Tam Le, Olivier Fercoq +2
Popular machine learning estimators involve regularization parameters that can be challenging to tune, and standard strategies rely on grid search for this task. In this paper, we…
An Adaptive Primal-Dual Framework for Nonsmooth Convex Minimization
Quoc Tran-Dinh, Ahmet Alacaoglu, Olivier Fercoq +1
We propose a new self-adaptive, double-loop smoothing algorithm to solve composite, nonsmooth, and constrained convex optimization problems. Our algorithm is based on Nesterov's sm…
Neural Generative Models for Global Optimization with Gradients
Louis Faury, Flavian Vasile, Clément Calauzènes +1
The aim of global optimization is to find the global optimum of arbitrary classes of functions, possibly highly multimodal ones. In this paper we focus on the subproblem of global…
A Conditional Gradient Framework for Composite Convex Minimization with Applications to Semidefinite Programming
Alp Yurtsever, Olivier Fercoq, Francesco Locatello +1
We propose a conditional gradient framework for a composite convex minimization template with broad applications. Our approach combines smoothing and homotopy techniques under the…
Restarting the accelerated coordinate descent method with a rough strong convexity estimate
Olivier Fercoq, Zheng Qu
We propose new restarting strategies for the accelerated coordinate descent method. Our main contribution is to show that for a well chosen sequence of restarting times, the restar…