Generalized conditional gradient: analysis of convergence and applications

Abstract

The objectives of this technical report is to provide additional results on the generalized conditional gradient methods introduced by Bredies et al. [BLM05]. Indeed , when the objective function is smooth, we provide a novel certificate of optimality and we show that the algorithm has a linear convergence rate. Applications of this algorithm are also discussed.

References in corpus (1)

• Fast Computation of Wasserstein Barycenters

Cited by in corpus (6)

• A sparse control approach to optimal sensor placement in PDE-constrained parameter estimation problems
• Optimal Transport losses and Sinkhorn algorithm with general convex regularization
• Block-coordinate Frank-Wolfe algorithm and convergence analysis for semi-relaxed optimal transport problem
• Fast block-coordinate Frank-Wolfe algorithm for semi-relaxed optimal transport
• Generalized conditional gradient and learning in potential mean field games
• Optimal transport with $f$-divergence regularization and generalized Sinkhorn algorithm