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On the Global Linear Convergence of Frank-Wolfe Optimization Variants
Annual Conference on Neural Information Processing Systems, (2015): 496-504
EI
摘要
The Frank-Wolfe (FW) optimization algorithm has lately re-gained popularity thanks in particular to its ability to nicely handle the structured constraints appearing in machine learning applications. However, its convergence rate is known to be slow (sublinear) when the solution lies at the boundary. A simple less-known fix is to add the ...更多
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