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Pairwise Conditional Gradients without Swap Steps and Sparser Kernel Herding
International Conference on Machine Learning
The Pairwise Conditional Gradients (PCG) algorithm is a powerful extension of the Frank-Wolfe algorithm leading to particularly sparse solutions, which makes PCG very appealing for problems such as sparse signal recovery, sparse regression, and kernel herding. Unfortunately, PCG exhibits so-called swap steps that might not provide sufficient primal progress. The number of these bad steps is bounded by a function in the dimension and as such known guarantees do not generalize to thedblp:conf/icml/TsujiTP22 fatcat:75oibibgdjenlla7gfylskq4tu