This paper considers the generalized continuation Newton method and the trust-region updating strategy for the underdetermined system of nonlinear equations.Moreover, in order to improve its computational efficiency, the new method uses a switching updating technique of the Jacobian matrix. That is to say, it does not compute the next Jacobian matrix and replaces it with the current jacobian matrix when the linear approximation model of the merit function approximates it well. The numerical results show that the new method is more robust and faster than the traditional optimization method such as the Levenberg-Marquardt method (a variant of trust-region methods, the built-in subroutine fsolve.m of the MATLAB environment). The computational speed of the new method is about eight to fifty times as fast as that of fsolve. Furthermore, it also proves the global convergence and the local superlinear convergence of the new method under some standard assumptions.
翻译:本文件考虑了普遍延续牛顿法和信任区非线性方程系统更新战略。此外,为了提高计算效率,新方法使用了雅各布矩阵的转换更新技术。也就是说,它没有计算下一个雅各布矩阵的转换更新技术,而是在功绩函数的线性近似模型非常接近时用目前的雅科比矩阵取而代之。数字结果显示,新方法比传统的优化方法,如Levenberg-Marquardt方法(信任区域方法的一种变式,MATLAB环境的内置子例程fsolve.m)更强大、更快。新方法的计算速度大约是FSolve的八至五十倍。此外,它还证明了在某些标准假设下新方法的全球趋同和本地超线性趋同。