论文标题
加速的元叠加器进行凸优化
Accelerated meta-algorithm for convex optimization
论文作者
论文摘要
我们提出了一个加速的元算法，它允许在不同设置中获得凸的无约束最小化的加速方法。作为一般方案的应用，我们提出了几乎最佳的方法，可以用任意秩序的Lipschitz衍生物以及平滑的最小值优化问题来最大程度地减少平滑函数。所提出的元算法比文献中的元容量更笼统，并允许在多种环境中获得更好的收敛速率和实际性能。
We propose an accelerated meta-algorithm, which allows to obtain accelerated methods for convex unconstrained minimization in different settings. As an application of the general scheme we propose nearly optimal methods for minimizing smooth functions with Lipschitz derivatives of an arbitrary order, as well as for smooth minimax optimization problems. The proposed meta-algorithm is more general than the ones in the literature and allows to obtain better convergence rates and practical performance in several settings.
