Name
Title | ||
|---|---|---|
Addressing the greediness phenomenon in Nonlinear Programming by means of Proximal Augmented Lagrangians |
Abstract | ||
|---|---|---|
When one solves Nonlinear Programming problems by means of algorithms that use merit criteria combining the objective function
and penalty feasibility terms, a phenomenon called greediness may occur. Unconstrained minimizers attract the iterates at early stages of the calculations and, so, the penalty parameter
needs to grow excessively, in such a way that ill-conditioning harms the overall convergence. In this paper a regularization
approach is suggested to overcome this difficulty. An Augmented Lagrangian method is defined with the addition of a regularization
term that inhibits the possibility that the iterates go far from a reference point. Convergence proofs and numerical examples
are given. |
Year | DOI | Venue |
|---|---|---|
2010 | 10.1007/s10589-009-9271-4 | Comp. Opt. and Appl. |
Keywords | Field | DocType |
Nonlinear programming,Greediness,Augmented Lagrangian method,Regularization | Convergence (routing),Convergence proofs,Mathematical optimization,Mathematical analysis,Nonlinear programming,Augmented Lagrangian method,Regularization (mathematics),Phenomenon,Iterated function,Mathematics | Journal |
Volume | Issue | ISSN |
46 | 2 | 0926-6003 |
Citations | PageRank | References |
6 | 0.44 | 3 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Emerson V. Castelani | 1 | 13 | 1.29 |
| André L. M. Martinez | 2 | 13 | 1.63 |
| J. M. Martínez | 3 | 531 | 35.28 |
| B. F. Svaiter | 4 | 608 | 72.74 |