论文标题
条件梯度方法
Conditional Gradient Methods
论文作者
论文摘要
这项调查的目的是作为温和的介绍，也是对最先进的弗兰克 - 沃尔夫算法（也称为条件梯度算法）的连贯概述，以最小化功能。当线性优化比投影便宜时，这些算法在凸优化方面特别有用。 材料的选择是由强调关键思想以及提出新方法的原则来指导的，我们认为将来可能会变得很重要，甚至充分引用了旧作品在开发较新的方法中的必要性。但是，我们的选择有时会偏见，并且不必反映研究界的共识，而且我们当然错过了最近的重要贡献。毕竟，弗兰克（Frank）的所有研究领域 - 沃尔夫（Wolfe）非常活跃，使其成为一个动人的目标。对于任何这样的扭曲，我们真诚地道歉，我们完全承认：我们站在巨人的肩膀上。
The purpose of this survey is to serve both as a gentle introduction and a coherent overview of state-of-the-art Frank--Wolfe algorithms, also called conditional gradient algorithms, for function minimization. These algorithms are especially useful in convex optimization when linear optimization is cheaper than projections. The selection of the material has been guided by the principle of highlighting crucial ideas as well as presenting new approaches that we believe might become important in the future, with ample citations even of old works imperative in the development of newer methods. Yet, our selection is sometimes biased, and need not reflect consensus of the research community, and we have certainly missed recent important contributions. After all the research area of Frank--Wolfe is very active, making it a moving target. We apologize sincerely in advance for any such distortions and we fully acknowledge: We stand on the shoulder of giants.
