| Title | A dual-primal balanced augmented Lagrangian method for linearly constrained convex programming |
| Author | |
| Corresponding Author | Xu, Shengjie |
| Publication Years | 2022-08-01
|
| DOI | |
| Source Title | |
| ISSN | 1598-5865
|
| EISSN | 1865-2085
|
| Abstract | Most recently, a balanced augmented Lagrangian method (ALM) has been proposed by He and Yuan for the canonical convex minimization problem with linear constraints, which advances the original ALM by balancing its subproblems, improving its implementation and enlarging its applicable range. In this paper, we propose a dual-primal version of the newly developed balanced ALM, which updates the new iterate via a conversely dual-primal iterative order formally. The new algorithm inherits all advantages of the prototype balanced ALM, and it can be extended to more general separable convex programming problems with both linear equality and inequality constraints. The convergence analysis of the proposed method can be well conducted in the context of variational inequalities. In particular, by some application problems, we numerically validate that these balanced ALM type methods can outperform existing algorithms of the same kind significantly. |
| Keywords | |
| URL | [Source Record] |
| Indexed By | |
| Language | English
|
| SUSTech Authorship | Corresponding
|
| WOS Research Area | Mathematics
|
| WOS Subject | Mathematics, Applied
; Mathematics
|
| WOS Accession No | WOS:000842873300002
|
| Publisher | |
| EI Accession Number | 20223512641178
|
| EI Keywords | Constrained optimization
; Iterative methods
; Lagrange multipliers
; Software prototyping
; Variational techniques
|
| ESI Classification Code | Computer Programming:723.1
; Calculus:921.2
; Numerical Methods:921.6
; Systems Science:961
|
| Data Source | Web of Science
|
| Citation statistics |
Cited Times [WOS]:0
|
| Document Type | Journal Article |
| Identifier | http://kc.sustech.edu.cn/handle/2SGJ60CL/394164 |
| Department | Department of Mathematics |
| Affiliation | 1.Harbin Inst Technol, Dept Math, Harbin 150001, Peoples R China 2.Southern Univ Sci & Technol, Dept Math, Shenzhen 518055, Peoples R China |
| First Author Affilication | Department of Mathematics |
| Corresponding Author Affilication | Department of Mathematics |
| Recommended Citation GB/T 7714 |
Xu, Shengjie. A dual-primal balanced augmented Lagrangian method for linearly constrained convex programming[J]. Journal of Applied Mathematics and Computing,2022.
|
| APA |
Xu, Shengjie.(2022).A dual-primal balanced augmented Lagrangian method for linearly constrained convex programming.Journal of Applied Mathematics and Computing.
|
| MLA |
Xu, Shengjie."A dual-primal balanced augmented Lagrangian method for linearly constrained convex programming".Journal of Applied Mathematics and Computing (2022).
|
| Files in This Item: | There are no files associated with this item. | |||||
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