Abstract
Sequential Quadratic Programming (SQP) is a powerful class of algorithms for solving nonlinear optimization problems. Local convergence of SQP algorithms is guaranteed when the Hessian approximation used in each Quadratic Programming subproblem is close to the true Hessian. However, a good Hessian approximation can be expensive to compute. Low cost Hessian approximations only guarantee local convergence under some assumptions, which are not always satisfied in practice. To address this problem, this paper proposes a simple method to guarantee local convergence for SQP with poor Hessian approximation. The effectiveness of the proposed algorithm is demonstrated in a numerical example.
| Original language | English |
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| Title of host publication | 2017 IEEE 56th Annual Conference on Decision and Control, CDC 2017 |
| Place of Publication | Piscataway |
| Publisher | Institute of Electrical and Electronics Engineers |
| Pages | 3781-3786 |
| Number of pages | 6 |
| ISBN (Electronic) | 978-1-5090-2873-3 |
| ISBN (Print) | 978-1-5090-2874-0 |
| DOIs | |
| Publication status | Published - 18 Jan 2018 |
| Event | 56th IEEE Conference on Decision and Control (CDC 2017) - Melbourne, VIC, Australia, Melbourne, Australia Duration: 12 Dec 2017 → 15 Dec 2017 Conference number: 56 http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=8253407 |
Conference
| Conference | 56th IEEE Conference on Decision and Control (CDC 2017) |
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| Abbreviated title | CDC 2017 |
| Country/Territory | Australia |
| City | Melbourne |
| Period | 12/12/17 → 15/12/17 |
| Internet address |