Linearly Convergent Frank-Wolfe without Line-Search

Fabian Pedregosa (Google)*, Geoffrey Negiar (UC Berkeley), Armin Askari (UC Berkeley), Martin Jaggi (EPFL)


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26 Aug 2020 - 22:00:00-23:00:00
28 Aug 2020 - 17:00:00-18:00:00

Abstract: 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 convergence properties, the Away-steps and Pairwise FW variants have emerged as improved variants with faster convergence. However, these improved variants suffer from two practical limitations: they require at each iteration to solve a 1-dimensional minimization problem to set the step-size and also require the Frank-Wolfe linear subproblems to be solved exactly. In this paper we propose variants of Away-steps and Pairwise FW that lift both restrictions simultaneously. The proposed methods set the step-size based on a sufficient decrease condition, and do not require prior knowledge of the objective. Furthermore, they inherit all the favorable convergence properties of the exact line-search version, including linear convergence for strongly convex functions over polytopes. Benchmarks on different machine learning problems illustrate large performance gains of the proposed variants.

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