求解极大极小问题的共轭梯度法  

Conjugate Gradient Method for Solving Minimax Problems

作　　者：郝月 

机构地区：[1]青岛大学数学与统计学院,山东 青岛

出　　处：《应用数学进展》2020年第11期1916-1924,共9页Advances in Applied Mathematics

摘　　要：本文研究了极大极小问题的求解方法,利用指数罚函数对该问题进行光滑化处理,将其转化成光滑的无约束优化问题,并利用共轭梯度法来求解含有罚参数的无约束优化问题。最后,我们给出了数值算例来验证该算法求解极大极小问题的有效性。This paper studies the method for solving the minimax problem, the exponential penalty function is used for smoothing the problem which can be transformed into a smooth unconstrained optimization problem. We also use the conjugate gradient method to solve the unconstrained optimization with penalty parameters problem. Finally, numerical results are given to illustrate the effectiveness of the algorithm for solving minimax problems.

关 键 词：极大极小 指数罚函数 光滑化 共轭梯度法 

分 类 号：G63[文化科学—教育学]