Artelys Knitro 12.4 further improves the interface versatility

12 July 2021

— 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.

This release does not only come with performance improvements, in particular for general nonlinear problems, but also with improvements of the interface:

  • Improvement on NLP:
    • New dynamic scaling of the limited-memory Quasi-Newton BFGS, more robust and efficient for all algorithms. This leads to an average performance improvement of 20% on general nonlinear instances when using this Hessian approximation.


  • New Knitro API functions:
    • Allow deleting or changing linear or constant structures of a problem and then re-solving.
    • Allow to retrieve information about the feasibility of the variables and constraints after both the presolve and solving process, giving a detailed analysis of the complex infeasibilities in nonlinear problems.
    • Allow users to provide their own customized linear algebra solver, which is useful when the problem has a very specific structure and can be solved with highly specialized routines. This feature was recently applied with success to an industrial problem and enabled a 2x overall speedup.

In addition, Artelys Knitro 12.4 brings:

  • Improvement on MINLP:
    • Improved heuristics strategies
    • Early integer-feasible solution found on 15% more instances


  • Newly supported platforms:
    • Support for the new Apple M1 chip
New method for energy mix pathway optimization problems

New method for energy mix pathway optimization problems

— Planning for the energy transition requires the ability to optimize energy system development pathways, considering complex interactions and constraints, particularly concerning interactions between sectors and vectors. In this context, Artelys has developed a new method for solving these large-scale mathematical problems.

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