Abstract
The global convergence properties of a class of penalty methods for nonlinear programming are analyzed. These methods include successive linear programming approaches and, more specifically, the successive linear-quadratic programming approach presented by Byrd et al. [Math. Program., 100 (2004), pp. 27-48]. Every iteration requires the solution of two trust-region subproblems involving piecewise linear and quadratic models, respectively. It is shown that, for a fixed penalty parameter, the sequence of iterates approaches stationarity of the penalty function. A procedure for dynamically adjusting the penalty parameter is described, and global convergence results for it are established.
Original language | English (US) |
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Pages (from-to) | 471-489 |
Number of pages | 19 |
Journal | SIAM Journal on Optimization |
Volume | 16 |
Issue number | 2 |
DOIs | |
State | Published - 2006 |
Keywords
- Global convergence theory
- Nonlinear optimization
- Penalty parameter updates
- Sequential linear programming
ASJC Scopus subject areas
- Software
- Theoretical Computer Science
- Applied Mathematics