A Hybrid of Quasi-Newton Method with CG Method for Unconstrained Optimization
The quasi-Newton is a well-known method for solving small to medium-scale unconstrained optimization problems due to its simplicity and convergence. This leads to many modifications to improve its performance, and one of them is by hybridizing it with another optimization method. In this study, the...
| Published in: | Journal of Physics: Conference Series |
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| Main Author: | |
| Format: | Conference paper |
| Language: | English |
| Published: |
Institute of Physics Publishing
2019
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| Online Access: | https://www.scopus.com/inward/record.uri?eid=2-s2.0-85076084992&doi=10.1088%2f1742-6596%2f1366%2f1%2f012079&partnerID=40&md5=46ff12abe74da0f1ed986b0e5704ea6a |
| Summary: | The quasi-Newton is a well-known method for solving small to medium-scale unconstrained optimization problems due to its simplicity and convergence. This leads to many modifications to improve its performance, and one of them is by hybridizing it with another optimization method. In this study, the quasi-Newton method is combined with the ARM method, which is a type of conjugate gradient method. The resulting hybrid algorithm is globally convergent under exact line search. © Published under licence by IOP Publishing Ltd. |
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| ISSN: | 17426588 |
| DOI: | 10.1088/1742-6596/1366/1/012079 |