63.6k citations · 104.5k across the 42 of their papers we have counts for
6 papers · 1 filter
Proximal Splitting Meets Variance Reduction
Fabian Pedregosa, Kilian Fatras, Mattia Casotto
Despite the rise to fame of incremental variance-reduced methods in recent years, their use in nonsmooth optimization is still limited to few simple cases. This is due to the fact…
Linearly Convergent Frank-Wolfe with Backtracking Line-Search
Fabian Pedregosa, Geoffrey Negiar, Armin Askari +1
Structured constraints in Machine Learning have recently brought the Frank-Wolfe (FW) family of algorithms back in the spotlight. While the classical FW algorithm has poor local co…
Frank-Wolfe Splitting via Augmented Lagrangian Method
Gauthier Gidel, Fabian Pedregosa, Simon Lacoste-Julien
Minimizing a function over an intersection of convex sets is an important task in optimization that is often much more challenging than minimizing it over each individual constrain…
Adaptive Three Operator Splitting
Fabian Pedregosa, Gauthier Gidel
We propose and analyze an adaptive step-size variant of the Davis-Yin three operator splitting. This method can solve optimization problems composed by a sum of a smooth term for w…
Frank-Wolfe with Subsampling Oracle
Thomas Kerdreux, Fabian Pedregosa, Alexandre d'Aspremont
We analyze two novel randomized variants of the Frank-Wolfe (FW) or conditional gradient algorithm. While classical FW algorithms require solving a linear minimization problem over…
Improved asynchronous parallel optimization analysis for stochastic incremental methods
Rémi Leblond, Fabian Pedregosa, Simon Lacoste-Julien
As datasets continue to increase in size and multi-core computer architectures are developed, asynchronous parallel optimization algorithms become more and more essential to the fi…