Back to Search
Start Over
A parameterized proximal point algorithm for separable convex optimization
- Publication Year :
- 2018
-
Abstract
- In this paper, we develop a parameterized proximal point algorithm (P-PPA) for solving a class of separable convex programming problems subject to linear and convex constraints. The proposed algorithm is provable to be globally convergent with a worst-case O(1/t) convergence rate, wheret denotes the iteration number. By properly choosing the algorithm parameters, numerical experiments on solving a sparse optimization problem arising from statistical learning show that our P-PPA could perform significantly better than other state-of-the-art methods, such as the alternating direction method of multipliers and the relaxed proximal point algorithm.
- Subjects :
- Class (set theory)
021103 operations research
Control and Optimization
Optimization problem
0211 other engineering and technologies
Regular polygon
Parameterized complexity
010103 numerical & computational mathematics
02 engineering and technology
01 natural sciences
Separable space
Proximal point
Rate of convergence
Optimization and Control (math.OC)
Convex optimization
FOS: Mathematics
0101 mathematics
Algorithm
Mathematics - Optimization and Control
Mathematics
Subjects
Details
- Language :
- English
- Database :
- OpenAIRE
- Accession number :
- edsair.doi.dedup.....76841603d7c856cd6fa6ba997a624a39