摘要
利用广义投影技术 ,将求解无约束规划的超记忆梯度算法推广 ,建立了求解带非线性等式和不等式约束优化问题的一种超记忆梯度广义投影算法 ,并证明了算法的收敛性。该算法具有稳定、计算量小、所需收敛条件弱、收敛性强等特点 ,并改进了广义梯度投影算法的收敛速度。数值算例表明该算法是有效的。
A general super memory gradient projection method was generalized to solve the nonlinear programming problems about the nonlinear equality constraints and in equality constraints by using general projection matrix. The global convergence properties of the new method were discussed. The new method is of stability in calculation and demands less number of iterations, shorter computing time, strong convergent properties and weaker conditions for convergence than the original algorithm. The numerical results illustrate that the new method is more effective.
出处
《石油大学学报（自然科学版）》
CSCD
北大核心
2003年第2期119-123,0,共5页
Journal of the University of Petroleum,China(Edition of Natural Science)
关键词
非线性等式
不等式
约束优化
超记忆梯度
广义投影算法
收敛性
非线性规划
nonlinear programming
nonlinear equality constraints and in equality constraints
general projection algorithm
super memory gradient algorithm
convergence
