摘要
构造了一个求解一般约束非线性优化问题的增广Lagrangian算法 ,通过引进函数 φ(x) =max{g(x) ,- λr}可直接处理不等式的约束情形 .并且每次只需近似地求出对应增广Lagrangian罚函数的局部最小点 .在一般假设下 ,算法产生的点列的任意聚点都是问题的K
The paper presents an augmented lagrangian algorithm for optimization with general constraints. By applying to the function φ(x)=max｛g(x),-λr｝ , the algorithm can be applied directly in the case of inequality constraints. At any iteration, it only needs to get an approximate local minimum of the augmented lagrangian penalty fiction. Under general assumptions, the algorithm converges to the K-T point of the problem.
出处
《湘南学院学报》
2004年第5期31-34,43,共5页
Journal of Xiangnan University
