Tuesday, 15:45 - 16:10 h, Room: H 1058


Klaus Schittkowski
MISQP: A TR-SQP algorithm for the efficient solution of non-convex, non-relaxable mixed-integer nonlinear programming problems

Coauthors: Oliver Exler, Thomas Lehmann


We present a new sequential quadratic programming (SQP) algorithm stabilized by trust-regions for solving nonlinear, non-convex and non-relaxable mixed-integer optimization problems. The mixed-integer quadratic programming subproblems are solved by a branch-and-cut algorithm. Second order information is updated by a modified quasi-Newton update formula (BFGS) applied to the Lagrange function for continuous, but also for integer variables. The design goal is to solve practical optimization problems based on expensive executions of an underlying simulation program. Thus, the number of simulations or function evaluations, respectively, is our main performance criterion to measure the efficiency of the code. Numerical results are presented for a set of 175 mixed-integer test problems and different parameter settings of MISQP. The average total number of function evaluations of the new mixed-integer SQP code is about 1,200 including those needed for approximating partial derivatives.


Talk 2 of the invited session Tue.3.H 1058
"NLP and MINLP software" [...]
Cluster 10
"Implementations & software" [...]


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