Check out the new version of Artelys Knitro! A brand-new Julia interface is now available. It relies on both Knitro and MathOptInterface most recent APIs providing an efficient and flexible model definition. This Julia interface also supports the JuMP modeling language enabling optimization problem modeling in no time!
Artelys Knitro 12 also comes with clear improvements on mixed-integer models (MIP/MINLP). Several new cut families including Mixed Integer Rouding, zero-half and clique have been implemented and add up to the already existing knapsack cuts.
Those new cuts have been tested on a subset of instances derived from CMU-IBM and MINLPLib1&2 libraries. As illustrated above, both the computation time and the number of tree search nodes generated have been drastically improved when using Artelys Knitro 12!
• A new Python function, called ‘optimize’, solving all your optimization problems using Knitro in a single call fashion
• Enhancement of the presolver including multipass methods applying successive presolve operations to fully exploit your problem structure
• The Knitro-Tuner has been extended to handle mixed-integer models to automatically identify the best parameters required to solve the most complex instances!
• Support of the MPS file format (Mathematical Programming System) enabling the resolution of a wide range of linear and quadratic problems
• General performance improvement especially for large SOCP instances
— We are pleased to announce that Artelys Knitro 12.4, the new version of the leading nonlinear optimization solver, is now available! Check out how its improvements on performance and interface versatility can help you leverage mathematical optimization when tackling your problems.
— Within the INSULAE project, Artelys develops a tool for the strategic planning of the energy transition of European Union islands.
— Watch the video presenting the Artelys Crystal Energy Planner software, entirely dedicated to the operational scheduling of energy systems.
— Join Artelys and FICO on 18 May 2021 to learn more about ways to unleash the full potential of mathematical optimization.
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