摘要
该文利用矩阵分解与广义投影等技巧，给出了求解线性约束的非线性规划的一个广义投影型的超线性收敛算法，不需要δ－主动约束与每一步反复计算投影矩阵，避免了计算的数值不稳定性，利用矩阵求逆的递推公式，计算简便，由于采用了非精确搜索，算法实用可行，文中证明了算法具有收敛性及超线性的收敛速度．
In this paper,we present a superlinear convergence algorithm for nonlinear optimization with linear constraints using matrix decomposition and generalized projection techniques. The algorithm nces not to search the set of δ-active constraints and only needs one computation to the proective matrix at each itertion. Numerical instability of computation is avoided. The algorithm is practical because inexact linear search is adopted and the iterative formula of inverse matrix are given too. The convergence theorem and superlnear convergence rate theorem of the algorithm are proved.
出处
《数学物理学报（A辑）》
CSCD
北大核心
1997年第1期55-63,共9页
Acta Mathematica Scientia
基金
国家自然科学基金
关键词
广义投影
超线性收敛性
非线性规划
matrix decomposition,generalized gradient projection, globol conrergence Superlinear convergence
