A FILLED PENALTY FUNCTION METHOD FOR SOLVING CONSTRAINED OPTIMIZATION PROBLEMS

Jiahui Tang; Yifan Xu; Wei Wang

doi:10.11948/20220125

2023 Volume 13 Issue 2

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Jiahui Tang, Yifan Xu, Wei Wang. A FILLED PENALTY FUNCTION METHOD FOR SOLVING CONSTRAINED OPTIMIZATION PROBLEMS[J]. Journal of Applied Analysis & Computation, 2023, 13(2): 809-825. doi: 10.11948/20220125

Citation:

Jiahui Tang, Yifan Xu, Wei Wang. A FILLED PENALTY FUNCTION METHOD FOR SOLVING CONSTRAINED OPTIMIZATION PROBLEMS[J]. Journal of Applied Analysis & Computation, 2023, 13(2): 809-825. doi: 10.11948/20220125

A FILLED PENALTY FUNCTION METHOD FOR SOLVING CONSTRAINED OPTIMIZATION PROBLEMS

1.
School of Management, Fudan University, Shanghai, 200433, China
2.
School of Mathematics, East China University of Science and Technology, Shanghai, 200237, China

Corresponding author: Email address: tangjiahui0612@163.com(J. Tang)
Fund Project: The authors were supported by National Natural Science Foundation of China(72072036)

Abstract

Abstract

An important method to solve constrained optimization problem is to approach the optimal solution of constrained optimization problem gradually by sequential unconstrained optimization method, namely penalty function method. And the filling function method is one of the effective methods to solve the global optimal problem. In this paper, a class of augmented Lagrangian objective filled penalty functions are defined to solve non-convex constraint optimization problems, the authors call it filled penalty function method. The theoretical properties of these functions, such as exactness, smoothness, global convergence, are discussed. On this basis, a local optimization algorithm and an approximate global optimization algorithm with corresponding examples are given for solving constrained optimization problems.
- Filled penalty function method /
- non-convex constrained optimization problems /
- globally optimal point /
- convergence
MSC: 90C30

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References

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