论文标题
加速的最小算法群
Accelerated Minimax Algorithms Flock Together
论文作者
论文摘要
最近发现了最小值优化和定点迭代中的几种新加速方法，有趣的是，它们依赖于与Nesterov基于动量的加速度不同的机制。在这项工作中，我们表明这些加速算法表现出我们所谓的合并路径（MP）属性；这些算法的轨迹迅速合并。使用这种新颖的MP属性，我们建立了现有加速度最小算法的点收敛，并为强率浓度 - 侧面的concove设置和Prox-Grad设置提供了新的最新算法。
Several new accelerated methods in minimax optimization and fixed-point iterations have recently been discovered, and, interestingly, they rely on a mechanism distinct from Nesterov's momentum-based acceleration. In this work, we show that these accelerated algorithms exhibit what we call the merging path (MP) property; the trajectories of these algorithms merge quickly. Using this novel MP property, we establish point convergence of existing accelerated minimax algorithms and derive new state-of-the-art algorithms for the strongly-convex-strongly-concave setup and for the prox-grad setup.
