A New Penalty Dual-primal Augmented Lagrangian Method and its Extensions-Reference-Cited by-同舟云学术

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A New Penalty Dual-primal Augmented Lagrangian Method and its Extensions

Published:2024-01-01 Issue:-1 Volume:-1 Page:
ISSN:1027-5487
Container-title:Taiwanese Journal of Mathematics
language:
Short-container-title:Taiwanese J. Math.

Author:

Ou Xiaoqing¹,Yu Guolin²,Liu Jie³,Chen Jiawei³,Liu Zhaohan³

Affiliation:

1. School of Mathematics and Information Science, North Minzu University, China, and College of Management, Chongqing College of Humanities, China

2. School of Mathematics and Information Science, North Minzu University, China

3. School of Mathematics and Statistics, Southwest University, China

Publisher

The Mathematical Society of the Republic of China

Reference23 articles.

1. J. Bai, L. Jia and Z. Peng, A new insight on augmented Lagrangian method with applications in machine learning, J. Sci. Comput. 99 (2024), no. 2, Paper No. 53, 33 pp.

2. D. P. Bertsekas, Constrained Optimization and Lagrange Multiplier Methods, Computer Science and Applied Mathematics, Academic Press, New York, 1982.

3. E. G. Birgin and J. M. Martínez, Practical Augmented Lagrangian Methods for Constrained Optimization, Fundamentals of Algorithms 10, Society for Industrial and Applied Mathematics (SIAM), Philadelphia, PA, 2014.

4. S. Boyd, N. Parikh, E. Chu, B. Peleato and J. Eckstein, Distributed optimization and statistical learning via the alternating direction method of multipliers, Found. Trends Mach. Learn. 3 (2011), no. 1, 1–122.

5. R. Burachik, L. M. G. Drummond, A. N. Iusem and B. F. Svaiter, Full convergence of the steepest descent method with inexact line searches, Optimization 32 (1995), no. 2, 137–146.

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