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A New Search Direction for Broyden’s Family Method in Solving Unconstrained Optimization Problems

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Recent Advances on Soft Computing and Data Mining (SCDM 2016)

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Abstract

The conjugate gradient method plays an important role in solving large scale problems and the quasi-Newton method is known as the most efficient method in solving unconstrained optimization problems. Hence, in this paper, we proposed a new hybrid method between conjugate gradient method and quasi-Newton method known as the CG-Broyden method. Then, the new hybrid method is compared with the quasi-Newton methods in terms of the number of iterations and CPU-time using Matlabin Windows 10 which has 4 GB RAM and running using an Intel ® Core ™ i5. Furthermore, the performance profile graphic is used to show the effectiveness of the new hybrid method.. Our numerical analysis provides strong evidence that our CG-Broyden method is more efficient than the ordinary Broyden method Besides, we also prove that the new algorithm is globally convergent.

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Correspondence to Mohd Asrul Hery Ibrahim .

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Ibrahim, M.A.H., Abdullah, Z., Razik, M.A., Herawan, T. (2017). A New Search Direction for Broyden’s Family Method in Solving Unconstrained Optimization Problems. In: Herawan, T., Ghazali, R., Nawi, N.M., Deris, M.M. (eds) Recent Advances on Soft Computing and Data Mining. SCDM 2016. Advances in Intelligent Systems and Computing, vol 549. Springer, Cham. https://doi.org/10.1007/978-3-319-51281-5_7

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  • DOI: https://doi.org/10.1007/978-3-319-51281-5_7

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