论文标题
复合优化的随机原始双固定点方法
Stochastic primal dual fixed point method for composite optimization
论文作者
论文摘要
在本文中，我们提出了一种随机原始双固定点法（SPDFP），用于求解两个适当的下半连续凸功能的总和，其中一种是复合材料。该方法基于[7]中提出的原始双固定点法（PDFP），该方法不需要子问题解决。在某种温和的条件下，基于两组假设建立了收敛：有界和无限的梯度以及迭代预期误差的收敛速率是O（k^α），其中k是迭代的数量，而α\ in（0，1）。最后，在图形lasso和logistic Recression上，有效地提出了该提议的有效性。
In this paper we propose a stochastic primal dual fixed point method (SPDFP) for solving the sum of two proper lower semi-continuous convex function and one of which is composite. The method is based on the primal dual fixed point method (PDFP) proposed in [7] that does not require subproblem solving. Under some mild condition, the convergence is established based on two sets of assumptions: bounded and unbounded gradients and the convergence rate of the expected error of iterate is of the order O(k^α) where k is iteration number and α\in (0, 1]. Finally, numerical examples on graphic Lasso and logistic regressions are given to demonstrate the effectiveness of the proposed algorithm.
