2015_New Family of Conjugate Gradient Methods with Sufficient Descent Condition and Global Convergence For Unconstrained Optimizations
| Format: | General Document |
|---|
| _version_ | 1860798155494260736 |
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| building | INTELEK Repository |
| collection | Online Access |
| collectionurl | https://intelek.unisza.edu.my/intelek/pages/search.php?search=!collection3 |
| copyright | Copyright©PWB2025 |
| country | Malaysia |
| date | 2015-09-08 |
| format | General Document |
| id | 16210 |
| institution | UniSZA |
| originalfilename | NEW FAMILY OF CONJUGATE GRADIENT METHODS WITH SUFFICIENT DESCENT CONDITION AND GLOBAL CONVERGENCE FOR UNCONSTRAINED OPTIMIZATIONS (PHD_2015).pdf |
| person | Ibrahim Bin Jusoh |
| recordtype | oai_dc |
| resourceurl | https://intelek.unisza.edu.my/intelek/pages/view.php?ref=16210 |
| sourcemedia | Server storage Scanned document |
| spelling | 16210 https://intelek.unisza.edu.my/intelek/pages/view.php?ref=16210 https://intelek.unisza.edu.my/intelek/pages/search.php?search=!collection3 General Document Malaysia Library Staff (Top Management) Library Staff (Management) Library Staff (Support) Terengganu Faculty of Informatics & Computing English application/pdf 1.5 Server storage Scanned document Universiti Sultan Zainal Abidin UniSZA Private Access UNIVERSITI SULTAN ZAINAL ABIDIN SAMBox 2.3.4; modified using iTextSharp™ 5.5.10 ©2000-2016 iText Group NV (AGPL-version) Copyright©PWB2025 Conjugate gradient methods 2015-09-08 244 NEW FAMILY OF CONJUGATE GRADIENT METHODS WITH SUFFICIENT DESCENT CONDITION AND GLOBAL CONVERGENCE FOR UNCONSTRAINED OPTIMIZATIONS (PHD_2015).pdf Ibrahim Bin Jusoh Sufficient Descent Condition 2015_New Family of Conjugate Gradient Methods with Sufficient Descent Condition and Global Convergence For Unconstrained Optimizations Conjugate gradient methods are a family of significance methods for solving of large-scale unconstrained optimization problems. This is due to both the simplicity of its algorithm and low memory requirement. A lot of efforts have been done to improve those methods since 1964 when the work of Fletcher and Reeve had opened the way to nonlinear conjugate gradient methods. In this research two new simple modifications of conjugate gradient coefficient have been proposed. Both algorithms satisfy sufficient descent conditions and global convergence for exact line search and strong Wolfe line search. The convergence rate is super linear and its search directions fulfill the angle conditions. Based on the fact that a proof of global convergence for an algorithm does not ensure that it is an efficient method, then the new is tested with twenty eight standard optimization test problems using MATLAB version 7.10.0 (R 2010a) subroutine programming and compared with five well- known conjugate gradient methods, which are Fletcher and Reeves (FR), Polak-Ribiere-Polyak (PRP), Hestenes and Steifel (HS), Wei-Yao-Liu (WYL) and Dai and Yuan (DY). Numerical results based on number of iterations and CPU time are analyzed and presented using performance profile of Dolan and Moore. For every test function four initial points are selected, some are close to the solution and some are further away. It is found out that both new formulas perform better than the other formulas for exact line search. However IMR1 performs better than the other formulas for strong Wolfe line search. Dissertations, Academic Unconstrained Optimization Conjugate Gradient Methods Thesis |
| spellingShingle | 2015_New Family of Conjugate Gradient Methods with Sufficient Descent Condition and Global Convergence For Unconstrained Optimizations |
| state | Terengganu |
| subject | Conjugate gradient methods Dissertations, Academic |
| summary | Conjugate gradient methods are a family of significance methods for solving of large-scale unconstrained optimization problems. This is due to both the simplicity of its algorithm and low memory requirement. A lot of efforts have been done to improve those methods since 1964 when the work of Fletcher and Reeve had opened the way to nonlinear conjugate gradient methods. In this research two new simple modifications of conjugate gradient coefficient have been proposed. Both algorithms satisfy sufficient descent conditions and global convergence for exact line search and strong Wolfe line search. The convergence rate is super linear and its search directions fulfill the angle conditions. Based on the fact that a proof of global convergence for an algorithm does not ensure that it is an efficient method, then the new is tested with twenty eight standard optimization test problems using MATLAB version 7.10.0 (R 2010a) subroutine programming and compared with five well- known conjugate gradient methods, which are Fletcher and Reeves (FR), Polak-Ribiere-Polyak (PRP), Hestenes and Steifel (HS), Wei-Yao-Liu (WYL) and Dai and Yuan (DY). Numerical results based on number of iterations and CPU time are analyzed and presented using performance profile of Dolan and Moore. For every test function four initial points are selected, some are close to the solution and some are further away. It is found out that both new formulas perform better than the other formulas for exact line search. However IMR1 performs better than the other formulas for strong Wolfe line search. |
| title | 2015_New Family of Conjugate Gradient Methods with Sufficient Descent Condition and Global Convergence For Unconstrained Optimizations |
| title_full | 2015_New Family of Conjugate Gradient Methods with Sufficient Descent Condition and Global Convergence For Unconstrained Optimizations |
| title_fullStr | 2015_New Family of Conjugate Gradient Methods with Sufficient Descent Condition and Global Convergence For Unconstrained Optimizations |
| title_full_unstemmed | 2015_New Family of Conjugate Gradient Methods with Sufficient Descent Condition and Global Convergence For Unconstrained Optimizations |
| title_short | 2015_New Family of Conjugate Gradient Methods with Sufficient Descent Condition and Global Convergence For Unconstrained Optimizations |
| title_sort | 2015_new family of conjugate gradient methods with sufficient descent condition and global convergence for unconstrained optimizations |