Hybrid quasi-Newton with new conjugate gradient using exact line search
Until now, Quasi-newton (QN) method is the most well-known method for solving unconstrained optimization problem. This method consumes lesser time as compared to Newton method since it is unnecessary to compute Hessian matrices. For QN method, BFGS is the best solver in finding the optimum solutions...
| Published in: | International Journal of Recent Technology and Engineering |
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| Main Author: | |
| Format: | Article |
| Language: | English |
| Published: |
Blue Eyes Intelligence Engineering and Sciences Publication
2019
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| Online Access: | https://www.scopus.com/inward/record.uri?eid=2-s2.0-85070249746&partnerID=40&md5=3f1b4f68ecdfcefd1a314cb32c80837f |
| Summary: | Until now, Quasi-newton (QN) method is the most well-known method for solving unconstrained optimization problem. This method consumes lesser time as compared to Newton method since it is unnecessary to compute Hessian matrices. For QN method, BFGS is the best solver in finding the optimum solutions. Therefore, a new hybrid coefficient which possesses the convergence analysis computed by exact line search is introduced. This new hybrid coefficient is numerically proven by producing the best outcomes with least iteration number and CPU time. © BEIESP. |
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| ISSN: | 22773878 |