A GLOBALLY CONVERGENT TRUST-REGION METHOD FOR LARGE-SCALE SYMMETRIC NONLINEAR SYSTEMS

作者：Ahookhosh Masoud; Amini Keyvan^*; Kimiaei Morteza

来源：Numerical Functional Analysis and Optimization, 2015, 36(7): 830-855.

DOI：10.1080/01630563.2015.1046080

• Adaptive radius
• Derivative-free quasi-Newton update
• Global convergence
• Limited memory technique
• Nonlinear equations
• Trust-region method
• 34A34
• 93E24
• 90C30
• 65K05

This study presents a novel adaptive trust-region method for solving symmetric nonlinear systems of equations. The new method uses a derivative-free quasi-Newton formula in place of the exact Jacobian. The global convergence and local quadratic convergence of the new method are established without the nondegeneracy assumption of the exact Jacobian. Using the compact limited memory BFGS, we adapt a version of the new method for solving large-scale problems and develop the dogleg scheme for solving the associated trust-region subproblems. The sufficient decrease condition for the adapted dogleg scheme is established. While the efficiency of the present trust-region approach can be improved by using adaptive radius techniques, utilizing the compact limited memory BFGS adjusts this approach to handle large-scale symmetric nonlinear systems of equations. Preliminary numerical results for both medium- and large-scale problems are reported.

• 出版日期2015-7-3