Name
Abstract | ||
|---|---|---|
In nonlinearly constrained optimization, penalty methods provide an effective strategy for handling equality constraints, while barrier methods provide an effective approach for the treatment of inequality constraints. A new algorithm for nonlinear optimization is proposed based on minimizing a shifted primal-dual penalty-barrier function. Certain global convergence properties are established. In particular, it is shown that a limit point of the sequence of iterates may always be found that is either an infeasible stationary point or a complementary approximate Karush-Kuhn-Tucker point; i.e., it satisfies reasonable stopping criteria and is a Karush-Kuhn-Tucker point under a regularity condition that is the weakest constraint qualification associated with sequential optimality conditions. It is also shown that under suitable additional assumptions, the method is equivalent to a shifted variant of the primal-dual path-following method in the neighborhood of a solution. Numerical examples are provided that illustrate the performance of the method compared to a widely used conventional interior-point method. |
Year | DOI | Venue |
|---|---|---|
2020 | 10.1137/19M1247425 | SIAM JOURNAL ON OPTIMIZATION |
Keywords | DocType | Volume |
nonlinear optimization,augmented Lagrangian methods,barrier methods,interior methods,path-following methods,regularized methods,primal-dual methods | Journal | 30 |
Issue | ISSN | Citations |
2 | 1052-6234 | 1 |
PageRank | References | Authors |
0.35 | 0 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Philip E. Gill | 1 | 43 | 4.70 |
| Vyacheslav Kungurtsev | 2 | 1 | 0.69 |
| Daniel P. Robinson | 3 | 261 | 21.51 |