Name
Abstract | ||
|---|---|---|
This paper studies theoretical and practical properties of interior-penalty methods for mathematical programs with complementarity constraints. A framework for implementing these methods is presented, and the need for adaptive penalty update strategies is motivated with examples. The algorithm is shown to be globally convergent to strongly stationary points, under standard assumptions. These results are then extended to an interior-relaxation approach. Superlinear convergence to strongly stationary points is also established. Two strategies for updating the penalty parameter are proposed, and their efficiency and robustness are studied on an extensive collection of test problems. |
Year | DOI | Venue |
|---|---|---|
2006 | 10.1137/040621065 | SIAM Journal on Optimization |
Keywords | Field | DocType |
extensive collection,mathematical programs,interior-point methods,nonlinear programming,mpec,paper study,mpcc,interior-relaxation approach,penalty parameter,complementarity constraints. ams-msc2000: 90c30,complementarity constraints,49m37,complementarity constraint,adaptive penalty update strategy,exact penalty,interior methods,superlinear convergence,65k10.,equilibrium constraints,interior-penalty method,90c51,90c33,stationary point,mathematical program,equilibrium,interior point method,interior point methods | Superlinear convergence,Complementarity (molecular biology),Mathematical optimization,Nonlinear programming,Robustness (computer science),Stationary point,Interior point method,Mathematics | Journal |
Volume | Issue | ISSN |
17 | 1 | 1052-6234 |
Citations | PageRank | References |
53 | 2.56 | 17 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Sven Leyffer | 1 | 1437 | 121.55 |
| Gabriel López-Calva | 2 | 53 | 2.56 |
| Jorge Nocedal | 3 | 3276 | 301.50 |