论文标题
非平滑凸优化的快速近端算法
Fast proximal algorithms for nonsmooth convex optimization
论文作者
论文摘要
在我们在\ cite {ouorou2019}中的方法中，我们利用Nesterov快速梯度概念\ cite {nesterov1983} {Nesterov1983} {nesterov1983}，我们为凸功能的Moreau-yosida正则化，我们设计了新的近端算法，以实现非态量凸出凸的优化。这些算法无需捆绑机制即可更新稳定中心，同时保留在\ cite {ouorou2019}中建立的复杂性估计值。我们报告了一些有关某些学术测试问题的初步计算结果，以对其与经典近端捆绑算法相关的性能进行首次估算。
In the lines of our approach in \cite{Ouorou2019}, where we exploit Nesterov fast gradient concept \cite{Nesterov1983} to the Moreau-Yosida regularization of a convex function, we devise new proximal algorithms for nonsmooth convex optimization. These algorithms need no bundling mechanism to update the stability center while preserving the complexity estimates established in \cite{Ouorou2019}. We report some preliminary computational results on some academic test problem to give a first estimate of their performance in relation with the classical proximal bundle algorithm.
