Transformed Hessian methods for large-scale constrained optimization

Conference · Sat Dec 31 00:00:00 EST 1994

OSTI ID:36060

Gill, P; Murray, W; Saunders, M

We define the transformed Hessian and discuss its role in sequential quadratic programming (SQP) methods for constrained optimization. In large-scale optimization, nonlinear inequality constraints are usually converted to equalities by adding bounded slack variables. Provided the proper sparse-matrix techniques are used, there is no significant penalty with the increased problem size. We identify structure in the transformed Hessian that is present when there are slack variables and describe how this structure may be exploited in a quasi-Newton method. Numerical results from the large-scale SQP code SNOPT will be presented.

• OSTI ID: 36060
• Report Number(s): CONF-9408161-; TRN: 94:009753-0330
• 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

Related Subjects

99 MATHEMATICS
COMPUTERS
INFORMATION SCIENCE
MANAGEMENT
LAW
MISCELLANEOUS
ALGORITHMS
OPTIMIZATION
MATRICES
NUMERICAL SOLUTION
VARIATIONAL METHODS
ACCURACY
CONVERGENCE
NEWTON METHOD