| Derivation and Global Convergence for Memoryless Non-quasi-Newton Method |
| Received:May 26, 2007 Revised:July 06, 2008 |
| Key Words:
memoryless non-quasi-Newton method Wolfe line search global convergence.
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| Fund Project:the National Natural Science Foundation of China (No.60472071); the Science Foundation of Beijing Municipal Commission of Education (No.KM200710028001). |
| Author Name | Affiliation | | JIAO Bao Cong | School of Mathematical Sciences, Capital Normal University, Beijing 100037, China | | YU Jing Jing | School of Mathematical Sciences, Capital Normal University, Beijing 100037, China
Department of Electrical Engineering, Qingdao Harbor Vocational Technology College, Shandong 266404, China | | CHEN Lan Ping | School of Mathematical Sciences, Capital Normal University, Beijing 100037, China |
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| Abstract: |
| In this paper, a new class of memoryless non-quasi-Newton method for solving unconstrained optimization problems is proposed, and the global convergence of this method with inexact line search is proved. Furthermore, we propose a hybrid method that mixes both the memoryless non-quasi-Newton method and the memoryless Perry-Shanno quasi-Newton method. The global convergence of this hybrid memoryless method is proved under mild assumptions. The initial results show that these new methods are efficient for the given test problems. Especially the memoryless non-quasi-Newton method requires little storage and computation, so it is able to efficiently solve large scale optimization problems. |
| Citation: |
| DOI:10.3770/j.issn:1000-341X.2009.03.006 |
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