SQP Methods for Mathematical Programs With Switching Constraints

LUO Meiling; LI Gaoxi; HUANG Yingquan; LIU Liying

doi:10.21656/1000-0887.420294

Volume 43 Issue 7

Jul. 2022

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LUO Meiling, LI Gaoxi, HUANG Yingquan, LIU Liying. SQP Methods for Mathematical Programs With Switching Constraints[J]. Applied Mathematics and Mechanics, 2022, 43(7): 792-801. doi: 10.21656/1000-0887.420294

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SQP Methods for Mathematical Programs With Switching Constraints

doi: 10.21656/1000-0887.420294

School of Mathematics and Statistics, Chongqing Technology and Business University, Chongqing 400067, P.R.China

Received Date: 2021-09-26
Rev Recd Date: 2021-11-23
Publish Date: 2022-07-15

Abstract

Abstract

The mathematical program with switching constraint (MPSC) problem makes a new-type optimization issue in recent years. Due to the existence of switching constraints, the common constraint specification is not satisfied, so that the convergence results of existing algorithms can not be directly applied to this problem. The sequential quadratic programming (SQP) method was applied to solve the problem, and to prove that the clustering point of the solution sequence of the subproblem is the Karush-Kuhn-Tucker point of the original problem under the linear independent constraint specification with the switching constraint. At the same time, in order to improve the relationship between stationary points, the equivalence between the strong stationary point and the KKT point was proved. Finally, the numerical results show that, the sequential quadratic programming method is feasible to deal with this type of problems.
- nonlinear programming,
- mathematical program with switching contraint,
- sequential quadratic programming method,
- global convergence

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References(14)

References

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