摘要
本文提出了一个新的求解无约束最优化问题的自适应信赖域算法.信赖域半径采用一个新的自适应调整策略.基于一个简单的子问题模型,算法需要很少的存储量和计算量.在一般假设条件下,证明了算法的全局收敛性.数值试验结果表明算法是有效的,适合求解大规模问题.
In this paper,we propose a new self adaptive trust region method for solving unconstrained optimization problems.The trust region radius is adjusted with a new self adaptive strategy.The method is based on a simple trust region subproblem model,which needs less memory capacitance and computational complexity.Convergence results of the method are proved under general conditions.Numerical experiments show that the new method is effective and attractive for large-scale optimization problems.
出处
《数学进展》
CSCD
北大核心
2010年第5期545-554,共10页
Advances in Mathematics（China）
基金
supported by the NSFC(No.10971118)
关键词
信赖域
无约束最优化
自适应
简单模型
全局收敛
数值试验
trust region
unconstrained optimization
self adaptive
simple model
global convergence
numerical experiment
