Artelys Knitro 13.2: increased robustness for MINLP
Mixed-integer nonlinear programs offer a very natural way to model many engineering problems where nonlinear functions can capture the complex behavior of physical processes and integer variables can be used to model discrete decisions. Several examples can be found in our extensive library of Artelys Knitro use-case, including MINLP formulations covering applications such as dimensioning EV charging infrastructures, efficient fault detection and isolation (FDI) on complex systems and many more!
Artelys Knitro 13.2 is more robust and consistently converging in shorter time to optimality on MINLP thanks to several developments:
- Presolve refinements including bound tightening and detection of redundant constraints
- Improved cut generation and selection strategy
- New automatic strategies for cut selection
- Improved multi-start heuristics for non-convex mixed-integer problems
In particular, Artelys Knitro 13.2 solves 12 additional instances on the MINLPLib benchmark compared to Artelys Knitro 13.1. In addition, this new version converges to a global optimum on 9% additional non-convex instances from MINLPLib2.
If you want to know more about Artelys Knitro, you can contact us or visit the dedicated web page.
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