A Frank-Wolfe method that adapts both the smoothness constant and the triangle-scaling exponent achieves sublinear convergence and a tolerance-dependent linear rate, with a centralized distributed optimization application.
The annals of statistics 19(4), 2032–2066 (1991)
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A Fully Adaptive Frank-Wolfe Algorithm for Relatively Smooth Problems and Its Application to Centralized Distributed Optimization
A Frank-Wolfe method that adapts both the smoothness constant and the triangle-scaling exponent achieves sublinear convergence and a tolerance-dependent linear rate, with a centralized distributed optimization application.