A RECURSIVE EQUALITY CONSTRAINED QUADRATIC APPROXIMATION METHOD WITH AN AUGMENTED LAGRANGIAN TYPE PENALTY FUNCTION FOR LINE SEARCH
采用增广拉格朗日乘子形式的罚函数作线性搜索的递归等式约束二次逼近算法

The recursive cquality constrained quadratic programming method requires least executioncomputer time for solving the constrained optimization problem. Biggs used the quadraticpenalty function for the line search and proved that the method is globally convergent.Tofurther raise the efficiency and reduce the sensitivity to some parameters, this paper presentsan augmented Lagrangian type penalty function for the line search. An algorithm is describedand the global convergence of the method is proved. Some computing results of the algorithmare showed to contrast with other algorithms of the same type.