Abstract
In this paper, we propose two algorithms for solving nonlinear monotone operator equations. The two algorithms are based on the conjugate gradient method. The corresponding search directions were obtained via a modified memoryless symmetric rank-one (SR1) update. Independent of the line search, the two directions were shown to be sufficiently descent and bounded. Moreover, the convergence of the algorithms were established under suitable assumptions on the operator under consideration. In addition, numerical experiments were conducted on some benchmark test problems to depict the efficiency and competitiveness of the algorithms compared with existing algorithms. From the results of the experiments, we can conclude that the proposed algorithms are more efficient and robust.
| Original language | English |
|---|---|
| Pages (from-to) | 343-373 |
| Number of pages | 31 |
| Journal | Journal of Applied Mathematics and Computing |
| Volume | 67 |
| Issue number | 1-2 |
| DOIs | |
| State | Published - Oct 2021 |
| Externally published | Yes |
Bibliographical note
Publisher Copyright:© 2021, Korean Society for Informatics and Computational Applied Mathematics.
Keywords
- Derivative-free method
- Nonlinear monotone operator equations
- Self-scaling memoryless SR1 update
ASJC Scopus subject areas
- Computational Mathematics
- Applied Mathematics