摘要
提出一种解大规模无约束优化问题的自适应过滤信赖域法。用目标函数的梯度及迭代点的信息来构造目标函数海赛矩阵的近似数量矩阵,引进了过滤技术和自适应技术,大大提高了计算效率。从理论上证明了新算法的全局收敛性,数值试验结果也表明了新算法的有效性。
An adaptive filter trust region method for large scale unconstrained optimization is proposed.This new algorithm uses the function and its gradients to determine a scale matrix as an approximation of its Hessian matrix in the subproblem. The adaptive tecluaique and filter technique are introduced to improve the behavior of the method.The new algorithm is shown to be globally convergent and numerical experiments indicate that it is very effective for large scale unconstrained minimization problems.
出处
《计算机工程与应用》
CSCD
北大核心
2011年第20期47-49,108,共4页
Computer Engineering and Applications
基金
江苏技术师范学院基金(No.KYY08041)
关键词
大规模无约束优化
过滤技术
梯度法
自适应信赖域法
全局收敛性
large scale unconstrained optimization
filter technique
gradient method
adaptive trust region method
global convergence
