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Filtered performance diagrams for analyzing MINLP solver performance

Hold Date
2014-06-17 10:30〜2014-06-17 12:00
#317, Faculty of Mathematics building, Ito Campus
Object person
Ambros Gleixner (ZIB)

General-purpose solvers that address large and heterogeneous problem classes like mixed-integer nonlinear programming (MINLP) necessarily combine a variety of algorithmic techniques in their solution process. In this talk, we present recent advances in the constraint integer programming-based MINLP solver SCIP with a special focus on analyzing the computational impact of individual solver components such as branching strategies, separation routines, bound tightening techniques, and primal heuristics.  We propose the use of so-called filtered performance diagrams in order to exhibit average performance impact not only on the overall collection of a set of benchmark instances, but also on subsets of instances of increasing hardness.