论文标题
可分离结构的稀疏凸编程的分布式原始外部近似算法
Distributed Primal Outer Approximation Algorithm for Sparse Convex Programming with Separable Structures
论文作者
论文摘要
本文介绍了分布式原始外近似（DIPOA）算法，用于以有效的方式和分散的方式解决稀疏的凸编程（SCP）问题。 DIPOA算法的开发包括嵌入最近提出的松弛混合式交流方向方法（RH-ADMM）算法中的外部近似（OA）算法。我们还提出了两个主要的改进，以控制近似非线性功能的切割平面的质量和数量。特别是，RH-ADMM算法充当双Dipoa算法内的分布式数值引擎。 DIPOA利用现代处理器的多核体系结构来加快优化算法。所提出的分布式算法使实用的SCP解决了从应用方面的学习和控制问题。本文以分布式稀疏逻辑回归和四次约束优化问题的DIPOA的性能分析结束。最后，本文以数值比较与最先进的优化求解器进行了结论。
This paper presents the Distributed Primal Outer Approximation (DiPOA) algorithm for solving Sparse Convex Programming (SCP) problems with separable structures, efficiently, and in a decentralized manner. The DiPOA algorithm development consists of embedding the recently proposed Relaxed Hybrid Alternating Direction Method of Multipliers (RH-ADMM) algorithm into the Outer Approximation (OA) algorithm. We also propose two main improvements to control the quality and the number of cutting planes that approximate nonlinear functions. In particular, the RH-ADMM algorithm acts as a distributed numerical engine inside the DiPOA algorithm. DiPOA takes advantage of the multi-core architecture of modern processors to speed up optimization algorithms. The proposed distributed algorithm makes practical the solution of SCP in learning and control problems from the application side. This paper concludes with a performance analysis of DiPOA for the distributed sparse logistic regression and quadratically constrained optimization problems. Finally, the paper concludes with a numerical comparison with state-of-the-art optimization solvers.
