Norm descent conjugate gradient methods for solving symmetric nonlinear equations
The authors propose a family of conjugate gradient methods for solving large-scale symmetric nonlinear equations. The proposed methods do not require the Jacobian information of the quations and do not store any matrix at each iteration. The global convergence of the method is established under certain technical conditions. Some experimental results are presented to show the effectiveness of the proposed methods.
large-scale symmetric nonlinear equations
scientific article; zbMATH DE number 6484165
scientific article