A globally convergent primal-dual interior point method for constrained optimization

1998 ◽  
Vol 10 (2) ◽  
pp. 443-469 ◽  
Author(s):  
Hiroshi Yamashita
2015 ◽  
Vol 8 (3) ◽  
pp. 313-335 ◽  
Author(s):  
Jianling Li ◽  
Jian Lv ◽  
Jinbao Jian

AbstractIn this paper, a primal-dual interior point method is proposed for general constrained optimization, which incorporated a penalty function and a kind of new identification technique of the active set. At each iteration, the proposed algorithm only needs to solve two or three reduced systems of linear equations with the same coefficient matrix. The size of systems of linear equations can be decreased due to the introduction of the working set, which is an estimate of the active set. The penalty parameter is automatically updated and the uniformly positive definiteness condition on the Hessian approximation of the Lagrangian is relaxed. The proposed algorithm possesses global and superlinear convergence under some mild conditions. Finally, some preliminary numerical results are reported.


2000 ◽  
Vol 120 (8-9) ◽  
pp. 1175-1181
Author(s):  
Min-Hwa Jeong ◽  
Junji Kubokawa ◽  
Naoto Yorino ◽  
Hiroshi Sasaki ◽  
Byongjun Lee ◽  
...  

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 41053-41061
Author(s):  
Wenjing Shang ◽  
Wei Xue ◽  
Yingsong Li ◽  
Yidong Xu

1999 ◽  
Vol 32 (2) ◽  
pp. 4654-4658 ◽  
Author(s):  
Anders Hansson ◽  
Khalid El-Awady ◽  
Bo Wahlberg

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