摘要
考虑非线性等式约束优化问题,提出一种既约Hessian阵校正算法,此算法分别对Lagrange函数的单边既约Hessian阵的近似阵和双边既约Hessian阵的近似阵进行校正.我们证明了若每次迭代至少有一者被校正时,算法具有1—步Q—超线性收敛速度.
The problem considered is that of minimizing a nonlinear function subject to a set of equality constrains.We present an algorithm which updates an approximation to one-sided reduced Hessian and two-sided reduced Hessian respectively.It is shown that if at least one of the updates is performed at each iteration,the method is locally onestep Q-superlinearly convergent.
出处
《首都师范大学学报（自然科学版）》
2010年第3期1-10,15,共11页
Journal of Capital Normal University：Natural Science Edition
基金
北京市教委科研基金(KM200710028001)资助
