Name
Title | ||
|---|---|---|
A Conjugate Gradient Method with Global Convergence for Large-Scale Unconstrained Optimization Problems. |
Abstract | ||
|---|---|---|
The conjugate gradient (CG) method has played a special role in solving large-scale nonlinear optimization problems due to the simplicity of their very low memory requirements. This paper proposes a conjugate gradient method which is similar to Dai-Liao conjugate gradient method (Dai and Liao, 2001) but has stronger convergence properties. The given method possesses the sufficient descent condition, and is globally convergent under strong Wolfe-Powell (SWP) line search for general function. Our numerical results show that the proposed method is very efficient for the test problems. |
Year | DOI | Venue |
|---|---|---|
2013 | 10.1155/2013/730454 | JOURNAL OF APPLIED MATHEMATICS |
Field | DocType | Volume |
Gradient method,Conjugate gradient method,Gradient descent,Mathematical optimization,Mathematical analysis,Proximal Gradient Methods,Nonlinear conjugate gradient method,Mathematics,Conjugate residual method,Derivation of the conjugate gradient method,Biconjugate gradient method | Journal | 2013 |
Issue | ISSN | Citations |
null | 1110-757X | 3 |
PageRank | References | Authors |
0.42 | 9 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Shengwei Yao | 1 | 65 | 5.53 |
| Xiwen Lu | 2 | 182 | 21.03 |
| Zengxin Wei | 3 | 373 | 28.04 |