Projected sequential quadratic programming methods
In this talk we investigate projected SQP methods for the solution of min f(y, u). s.t. c(y, u) = 0 a {<=} u {<=} b. These methods combine the ideas of (reduced) SQP methods and projected Newton methods. The problem formulation and the design of these solution methods is motivated by optimal control problems. In this case y and u are the state and the control, respectively, and c(y, u) = 0 represents the state equation. Projected SQP methods use the simple projection onto the set {l_brace}a {<=} u {<=} b{r_brace} and maintain feasibility with respect to the inequality constraints. In each iteration only linearized constraint equations need to be solved. Global convergence of the method is enforced using a constrained merit function and an Armijo-like line search. We discuss global and local convergence properties of these methods, the identification of active indices, and implementation details for optimal control problems. Numerical examples of projected SQP methods applied to optimal control problems are presented.
- OSTI ID:
- 36121
- Report Number(s):
- CONF-9408161-; TRN: 94:009753-0396
- Resource Relation:
- Conference: 15. international symposium on mathematical programming, Ann Arbor, MI (United States), 15-19 Aug 1994; Other Information: PBD: 1994; Related Information: Is Part Of Mathematical programming: State of the art 1994; Birge, J.R.; Murty, K.G. [eds.]; PB: 312 p.
- Country of Publication:
- United States
- Language:
- English
Similar Records
Large-scale sequential quadratic programming algorithms
Large-scale sequential quadratic programming algorithms