Separating variables to accelerate non-convex regularized optimization

Wenchen Liu, Yincai Tang and Xianyi Wu

Computational Statistics & Data Analysis, 2020, vol. 147, issue C

Abstract: In this paper, a novel variable separation algorithm stemmed from the idea of orthogonalization EM is proposed to find the minimization of general function with non-convex regularizer. The main idea of our algorithm is to construct a new function by adding an item that allows minimization to be solved separately on each component. Several attractive theoretical properties concerning the new algorithm are established. The new algorithm converges to one of the critical points with the condition that the objective function is coercive or the generated sequence is in a compact set. The convergence rate of the algorithm is also obtained. The Barzilai–Borwein (BB) rule and Nesterov’s method are also used to accelerate our algorithm. The new algorithm can also be used to solve the minimization of general function with group structure regularizer. The simulation and real data results show that these methods can accelerate our method obviously.

Keywords: Variable separation algorithm; Non-convex regularization; Optimization; Convergence; Acceleration (search for similar items in EconPapers)
Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0167947320300347
Full text for ScienceDirect subscribers only.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:eee:csdana:v:147:y:2020:i:c:s0167947320300347

DOI: 10.1016/j.csda.2020.106943

Access Statistics for this article

Computational Statistics & Data Analysis is currently edited by S.P. Azen

More articles in Computational Statistics & Data Analysis from Elsevier
Bibliographic data for series maintained by Catherine Liu ().