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Apr, 2023
降低Frank-Wolfe方法的离散化误差
Reducing Discretization Error in the Frank-Wolfe Method
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Zhaoyue Chen, Yifan Sun
TL;DR
通过改进多步骤的Frank-Wolfe方法和LMO-平均方案使用一阶和高阶离散方案，从而减少离散化误差，其局部收敛速率通过一般凸集可以加速从O(1/k)到O(1/k^{3/2})，改善Frank-Wolfe算法的收敛速度。
Abstract
The
frank-wolfe algorithm
is a popular method in structurally constrained
machine learning
applications, due to its fast per-iteration complexity. However, one major limitation of the method is a slow rate of
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