A limited-memory trust-region method for nonlinear optimization with many equality constraints

被引：0
作者：
Jae Hwa Lee
Yoon Mo Jung
Sangwoon Yun
机构：
[1] Sungkyunkwan University,Convergence Research Center for Energy and Environmental Sciences
[2] Sungkyunkwan University,Department of Mathematics
[3] Sungkyunkwan University,Department of Mathematics Education
来源：
Computational and Applied Mathematics | 2023年 / 42卷
关键词：
Equality-constrained optimization; Large-scale; Limited-memory; Trust-region; Eigenvalue decomposition; 90C30; 90C06; 65K05;
D O I：
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学科分类号：
摘要：
In this paper, we propose a limited-memory trust-region method for solving large-scale nonlinear optimization problems with many equality constraints. Within the framework of the Byrd–Omojokun algorithm, we adopt the technique proposed by Burdakov et al. (Math Program Comput 9:101–134, 2017) to solve the accompanying trust-region subproblems. To successfully deal with the difficulties arising in the case of many constraints, we reduce the number of constraints in the normal subproblem, so that the computational cost at each iteration is suitable for large-scale problems. Furthermore, we establish the global convergence of the proposed method in that case. Numerical experiments on some test problems are given to verify the soundness and effectiveness of the proposed method.
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