A trust region method for nonlinear programming based on primal interior-point techniques
Journal Article
·
· SIAM Journal on Scientific Computing
- Sandia National Labs., Livermore, CA (United States)
This paper describes a new trust region method for solving large-scale optimization problems with nonlinear equality and inequality constraints. The new algorithm employs interior-point techniques from linear programming, adapting them for more general nonlinear problems. A software implementation based entirely on sparse matrix methods is described. The software handles infeasible start points, identifies the active set of constraints at a solution, and can use second derivative information to solve problems. Numerical results are reported for large and small problems, and a comparison is made with other large-scale optimization codes.
- Research Organization:
- Sandia National Laboratory
- Sponsoring Organization:
- USDOE, Washington, DC (United States); National Science Foundation, Washington, DC (United States)
- DOE Contract Number:
- AC04-94AL85000; FG02-87ER25047
- OSTI ID:
- 328393
- Journal Information:
- SIAM Journal on Scientific Computing, Journal Name: SIAM Journal on Scientific Computing Journal Issue: 1 Vol. 20; ISSN 1064-8275; ISSN SJOCE3
- Country of Publication:
- United States
- Language:
- English
Similar Records
A trust region method for nonlinear programming based on primal interior point techniques
Extending interior point method to nonlinear programming using trust regions
Research on trust-region algorithms for nonlinear programming
Technical Report
·
Fri Mar 31 23:00:00 EST 1995
·
OSTI ID:58060
Extending interior point method to nonlinear programming using trust regions
Conference
·
Fri Dec 30 23:00:00 EST 1994
·
OSTI ID:36393
Research on trust-region algorithms for nonlinear programming
Technical Report
·
Thu Oct 31 23:00:00 EST 1991
·
OSTI ID:5970506