摘要
本文对一类大规模二次规划问题,提出了矩阵剖分的概念和方法,并将问题转化为求解一系列容易求解的小规模二次规划子问题.另外,通过施加某些约束机制,使子问题所产生的迭代点均为可行下降点.在通常的假定下,证明算法具有全局收敛性,大量数值实验表明,本文所提出的新算法是有效的.
Abstract:In this paper, a new algorithm for solving large scale quadratic programming is proposed. We decompose a large scale quadratic programming into a serial of small scale ones whose solutions approximate that of the large scale quadratic programming. Furthermore, the algorithm is a feasible descent one. It is proved that the algorithm proposed is of the global convergence under the certain conditions. Numerical tests show that the algorithm has performed more effectively.
出处
《应用数学》
CSCD
北大核心
2007年第2期370-376,共7页
Mathematica Applicata
基金
国家自然科学基金资助项目(10671057)
关键词
大规模
二次规划
矩阵剖分
全局收敛
Large scale Quadratic programming
Matrix partition
Global convergence