Artelys Knitro 12.2: solve your nonlinear problems faster than ever!
This release does not only provide performance and robustness improvements but major refinements in Artelys Knitro’s core algorithms:
- Up to 60% performance improvement on customer benchmark for very large-scale general nonlinear instances (more than 100 000 variables) solved with Knitro interior point algorithms.
- Average performance improvement of 50% on medium sized general nonlinear instances (between 1000 and 10 000 variables). This enhancement derives from the refinement of Knitro Sequential Quadratic Programming (SQP) algorithm which proves particularly useful for customers solving problems with expensive function evaluations (e.g. complex nonlinear expressions, external simulator, black-box model, etc.).
- Up to 75% performance improvement on very large-scale unconstrained models (more than 250 000 variables) such as machine learning applications. This significant enhancement derives from the refinement of Knitro quasi-Newton methods (namely the dense and limited-memory Quasi-Newton BFGS).
In addition, Artelys Knitro 12.2 brings:
- Default parallelism when using the multi-start, multi-algorithm, or tuner features.
- Average performance improvement of 14% on general MINLP instances.
- Support for ARM processors! Artelys Knitro can now be used on embedded systems. Contact us for more information.
- Significant speedup can be observed on some Optimal Power Flow (OPF) instances using a new presolve option.
- An updated C++ interface offering efficiency improvements, particularly when solving models with quadratic structures (e.g. QPs and QCQPs).
- Increased performance on problems with complementarity constraints (i.e. MPECs) when using quasi-Newton methods.
- Faster resolution of non-smooth unconstrained models (such as machine learning applications) relying on the new weak Wolfe linesearch.
Innovative grid technologies can improve renewable energy integration in the Latvian grid by up to 40%
—The power grid faces challenges in managing the increasing amounts of new wind and solar power generation. Grid Enhancing Technologies (GETs) are essential for optimizing the use of the existing infrastructure. Artelys carried out a study for Latvian Transmission System Operator (TSO) AST to assess the renewable generation hosting capacity of the transmission grid and to evaluate the benefits that GETs can provide to renewable integration. The study performed simulations using the open-source optimal flow tool, PowSyBI Metrix. Results show that Grid Enhancing Technologies can increase Renewable Energy Sources (RES) hosting capacity by up to 40% and were announced in the following press release.
Artelys Knitro 14.1: delivers very quick solutions on non-convex models
— We are pleased to announce that Artelys Knitro 14.0 is now available! This new version enables compagnies to solve complex non-linear optimization problems with unprecedented efficency and precision.
You missed the METIS 3 Dissemination event? The slides are now available!
— Artelys was thrilled to organize the METIS 3 Dissemination webinar on April 17, which was a great opportunity to present key studies and upgrades of METIS models and datasets conducted in the last four years, including exciting discussions with external panelists!
Artelys participates in the demonstration of large-scale underground hydrogen storage in Europe
— Artelys is involved in the five-year project FrHyGe funded by the European Commission via the Clean Hydrogen Partnership.
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