Tensor methods for nonlinear equality constrained optimization
We introduce tensor methods for nonlinear equality constrained optimization problems. These are general purpose methods especially intended for problems where the constraint gradient matrix is rank deficient or ill-conditioned at the solution. They are adapted from the standard successive quadratic programming method by augmenting the linear model of constraints with a simple second order term. The second order term is selected so that the model of the constraints interpolates constraint function values from several previous iterations, as well as the current constraint function value and gradients. Similarly to tensor methods for nonlinear equations, the tensor methods for constrained optimization require no more function and derivative evaluations, and hardly more storage or arithmetic per iteration, than the standard SQP methods. We establish strong local convergence properties of a tensor method on an interesting class of singular problems, and show that they are very efficient computationally on singular and well-conditioned nonlinear equality constrained optimization problems.
- OSTI ID:
- 35999
- Report Number(s):
- CONF-9408161-; TRN: 94:009753-0267
- 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
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