Abstract
A sequential quadratic programming (SQP) method is presented that aims to overcome some of the drawbacks of contemporary SQP methods. It avoids the difficulties associated with indefinite quadratic programming subproblems by defining this subproblem to be always convex. The novel feature of the approach is the addition of an equality constrained quadratic programming (EQP) phase that promotes fast convergence and improves performance in the presence of ill conditioning. This EQP phase uses exact second-order information and can be implemented using either a direct solve or an iterative method. The paper studies the global and local convergence properties of the new algorithm and presents a set of numerical experiments to illustrate its practical performance.
Original language | English (US) |
---|---|
Pages (from-to) | 553-579 |
Number of pages | 27 |
Journal | IMA Journal of Numerical Analysis |
Volume | 32 |
Issue number | 2 |
DOIs | |
State | Published - Apr 2012 |
Keywords
- Constrained optimization
- Nonlinear programming
- Sequential quadratic programming
ASJC Scopus subject areas
- General Mathematics
- Computational Mathematics
- Applied Mathematics