Application of Linearized Alternating Direction Multiplier Method in Dictionary Learning

• 作者: Xiaoli Yu
• 来源期刊: 应用数学与应用物理(英文)
• 学科: 数学
• 关键词: ALTERNATING Direction MULTIPLIER Method DICTIONARY LEARNING Linearized ALTERNATING Direction MULTIPLIER Non-Convex Optimization CONVERGENCE
• 年，卷（期）: 2019,（1）
• 页码范围: 138-147
• 页数: 10页
• 分类号: O1

摘要

The Alternating Direction Multiplier Method (ADMM) is widely used in various fields, and different variables are customized in the literature for different application scenarios [1] [2] [3] [4]. Among them, the linearized alternating direction multiplier method (LADMM) has received extensive attention because of its effectiveness and ease of implementation. This paper mainly discusses the application of ADMM in dictionary learning (non-convex problem). Many numerical experiments show that to achieve higher convergence accuracy, the convergence speed of ADMM is slower, especially near the optimal solution. Therefore, we introduce the linearized alternating direction multiplier method (LADMM) to accelerate the convergence speed of ADMM. Specifically, the problem is solved by linearizing the quadratic term of the subproblem, and the convergence of the algorithm is proved. Finally, there is a brief summary of the full text.