Tuesday, 16:15 - 16:40 h, Room: H 0110

 

Andreas Waechter
A hot-started NLP solver

Coauthor: Travis C. Johnson

 

Abstract:
We discuss an active-set SQP method for nonlinear continuous
optimization that avoids the re-factorization of derivative matrices
during the solution of the step computation QP in each iteration.
Instead, the approach uses hot-starts of the QP solver for a QP with
matrices corresponding to an earlier iteration, or available from the
solution of a similar NLP. The goal of this work is the acceleration
of the solution of closely related NLPs, as they appear, for instance,
during strong-branching or diving heuristics in MINLP.

 

Talk 3 of the invited session Tue.3.H 0110
"Recent advances in nonlinear optimization" [...]
Cluster 16
"Nonlinear programming" [...]

 

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