论文标题
缩放信任区域牛顿算法的有效参数选择在求解边界约束的非线性系统中
Efficient Parameter Selection for Scaled Trust-Region Newton Algorithm in Solving Bound-constrained Nonlinear Systems
论文作者
论文摘要
我们研究了缩放信任区域牛顿（Strn）算法的参数选择问题，以求解边界约束的非线性方程。在大量的测试问题上进行了数值实验，以找到最佳的参数值范围，这些参数可提供最小的算法迭代和函数评估。我们的实验表明，通常没有选择最佳参数，并且每个特定值都在某些问题上显示出有效的性能，而在其他问题上则表现出较弱的性能。在这项研究中，我们报告了STRN的各种参数选择的性能，然后提出了最有效的参数。
We investigate the problem of parameter selection for the scaled trust-region Newton (STRN) algorithm in solving bound-constrained nonlinear equations. Numerical experiments were performed on a large number of test problems to find the best value range of parameters that give the least algorithm iterations and function evaluations. Our experiments demonstrate that, in general, there is no best parameter to be chosen and each specific value shows an efficient performance on some problems and weak performance on other ones. In this research, we report the performance of STRN for various choices of parameters and then suggest the most effective one.
