A trust region method for nonlinear programming based on primal interior-point techniques

Journal Article · Sat Aug 01 00:00:00 EDT 1998 · SIAM Journal on Scientific Computing

DOI:https://doi.org/10.1137/S1064827595284403· OSTI ID:328393

Plantenga, T ^[1]

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1. 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 Laboratories (SNL), Albuquerque, NM, and Livermore, CA (United States)
• 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, Vol. 20, Issue 1; Other Information: PBD: Aug 1998
• Country of Publication: United States
• Language: English

Related Subjects

99 MATHEMATICS
COMPUTERS
INFORMATION SCIENCE
MANAGEMENT
LAW
MISCELLANEOUS
OPTIMIZATION
ALGORITHMS
NONLINEAR PROGRAMMING
NONLINEAR PROBLEMS