An Improvement of Computing Newton’s Direction for Finding Unconstrained Minimizer for Large-Scale Problems with an Arrowhead Hessian Matrix

Author:

Ghazali Khadizah,Sulaiman Jumat,Dasril Yosza,Gabda Darmesah

Publisher

Springer Singapore

Reference21 articles.

1. Lin, S.Y., Lin, C.H.; An efficient method for unconstrained optimization problems of nonlinear large mesh-interconnected systems, IEEE Transaction on automatic control 40(3), 490-95 (1995).

2. Ng, C.K., Liao, LZ., Li, D.: A Globally Convergent and Efficient Method for Unconstrained Discrete-Time Optimal Control, Journal of Global Optimization 23, 401-421 (2003).

3. Zang, W., Zhang, W., Zhang, W., Liu, X.: A Genetic Algorithm Using Triplet Nucleotide Encoding and DNA Reproduction Operations for Unconstrained Optimization Problems. Algorithms 10, 76 (2017). http://dx.doi.org/10.3390/a10030076

4. Maratos, N.G., Moraitis, M.A.; Some results on the Sign recurrent neural network for unconstrained minimization, Neurocomputing 287, 1-21 (2018).

5. Sun, W., Yuan, Y.: Optimization Theory and Methods-Nonlinear Prog. Springer, United States (2006).

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