M. N. Vrahatis, G. S. Androulakis, J. N. Lambrinos and G. D. Magolas, “A Class of Gradient Unconstrained Minimization Algorithms with Adaptive Stepsize,” Journal of Computational and Applied Mathematics, Vol. 114, No. 2, 2000, pp. 367-386. doi10.1016/S0377-0427(99) 00276-9 - References

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M. N. Vrahatis, G. S. Androulakis, J. N. Lambrinos and G. D. Magolas, “A Class of Gradient Unconstrained Minimization Algorithms with Adaptive Stepsize,” Journal of Computational and Applied Mathematics, Vol. 114, No. 2, 2000, pp. 367-386. doi:10.1016/S0377-0427(99) 00276-9

has been cited by the following article:

TITLE: A Gauss-Newton-Based Broyden’s Class Algorithm for Parameters of Regression Analysis

AUTHORS: Xiangrong Li, Xupei Zhao

KEYWORDS: Global Convergence, Broyden’s Class, Regression Analysis, Nonlinear Equations, Gauss-Newton

JOURNAL NAME: Applied Mathematics, Vol.2 No.1, January 30, 2011

ABSTRACT: In this paper, a Gauss-Newton-based Broyden’s class method for parameters of regression problems is presented. The global convergence of this given method will be established under suitable conditions. Numerical results show that the proposed method is interesting.