论文标题
加速双平均原始偶偶联方法，用于最小化复合凸构
Accelerated Dual-Averaging Primal-Dual Method for Composite Convex Minimization
论文作者
论文摘要
双重平均型方法有效地促进解决方案结构（例如稀疏性），因此在工业机器学习应用中广泛使用。在本文中，我们提出了一种新型的加速双重平均二元算法，以最大程度地减少复合凸函数。我们还得出了提出的方法的随机版本，该方法解决了经验风险的最小化，其处理稀疏数据的优势在理论上和经验上都得到了证明。
Dual averaging-type methods are widely used in industrial machine learning applications due to their ability to promoting solution structure (e.g., sparsity) efficiently. In this paper, we propose a novel accelerated dual-averaging primal-dual algorithm for minimizing a composite convex function. We also derive a stochastic version of the proposed method which solves empirical risk minimization, and its advantages on handling sparse data are demonstrated both theoretically and empirically.
