The Superlinear Convergence of a Modified BFGS-Type Method for Unconstrained Optimization
Computational Optimization and Applications
Spectral gradient projection method for solving nonlinear monotone equations
Journal of Computational and Applied Mathematics
A new backtracking inexact BFGS method for symmetric nonlinear equations
Computers & Mathematics with Applications
Journal of Computational and Applied Mathematics
BFGS trust-region method for symmetric nonlinear equations
Journal of Computational and Applied Mathematics
A PRP type method for systems of monotone equations
Mathematical and Computer Modelling: An International Journal
Limited memory BFGS method with backtracking for symmetric nonlinear equations
Mathematical and Computer Modelling: An International Journal
Journal of Computational and Applied Mathematics
Nonmonotone spectral method for large-scale symmetric nonlinear equations
Numerical Algorithms
Hi-index | 0.01 |
In this paper, we present a Gauss--Newton-based BFGS method for solving symmetric nonlinear equations which contain, as a special case, an unconstrained optimization problem, a saddle point problem, and an equality constrained optimization problem. A suitable line search is proposed with which the presented BFGS method exhibits an approximate norm descent property. Under appropriate conditions, global convergence and superlinear convergence of the method are established. The numerical results show that the proposed method is successful.