Algorithmic enhancements and experience with a large scale SQP code for general nonlinear programming problems
Conference
·
OSTI ID:35846
We have developed a large scale sequential quadratic programming (SQP) code based on an interior-point method for solving general (convex or nonconvex) quadratic programs (QP). We often halt this QP solver prematurely by employing a trust-region strategy. This procedure typically reduces the overall cost of the code. In this talk we briefly review the algorithm and some of its theoretical justification and then discuss recent enhancements including automatic procedures for both increasing and decreasing the parameter in the merit function, a regularization procedure for dealing with linearly dependent active constraint gradients, and a method for modifying the linearized equality constraints. Some numerical results on a significant set of {open_quotes}real-world{close_quotes} problems will be presented.
- OSTI ID:
- 35846
- Report Number(s):
- CONF-9408161--
- Country of Publication:
- United States
- Language:
- English
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