An augmented Lagrangian trust region method for equality constrained optimization
An augmented Lagrangian trust region method for equality constrained optimization
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DOI：
10.1080/10556788.2014.940947
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发表时间：
2015-05
影响因子：
2.2
通讯作者：
Xiao Wang;Ya-xiang Yuan
中科院分区：
文献类型：
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作者：
Xiao Wang;Ya-xiang Yuan
本文提出了求解等式约束优化问题的增广拉格朗日信赖域方法。与标准增广拉格朗日方法在每次迭代中对固定的拉格朗日乘子和惩罚参数最小化增广拉格朗日函数不同，该方法试图最小化其二阶近似函数。提出了一种新的惩罚参数调整策略。利用拉格朗日乘子的自适应更新，证明了该方法的全局收敛性。本文还报道了CUTEr集合中测试问题的数值结果。
In this paper we propose an augmented Lagrangian trust region method for equality constrained optimization. Different from standard augmented Lagrangian methods which minimize the augmented Lagrangian function for fixed Lagrange multiplier and penalty parameter at each iteration, the proposed method tries to minimize its second-order approximation function. We propose a new strategy for adjusting the penalty parameter. With adaptive update of Lagrange multipliers, we prove the global convergence of the proposed method. Numerical results on test problems from the CUTEr collection are also reported.