Artelys Knitro 10.3: support for Python 3 and improved performance!

29 August 2017

— A new version of Artelys Knitro is now available! This update brings support for Python 3 and significant improvements of the R interface.

From a numerical perspective, large speedups are to be seen on some classes of ill-conditioned models. This is due to new preconditioning techniques applied to the subproblems solved by conjugate gradient method in the Knitro interior-point/barrier algorithms. Furthermore, Artelys Knitro 10.3 offers several improvements to the internal linear algebra and linear solvers used internally for better robustness and efficiency.

Exponential speedups on selected nonlinear least square instance

Additional features:

• Significant improvements in large-scale least-squares models, solving the instances 10 times faster!
• Improvements in the “feasibility restoration phase” used when Knitro is struggling to get feasible allowing faster detection of infeasible models.
• Overall efficiency and robustness improvements on general nonlinear models as well as significant improvements in efficiency on models with integer variables.

Supercharging Optimisation: How Artelys, FICO and NVIDIA cuOpt Join Efforts to Scale Up Energy System Optimisation

Supercharging Optimisation: How Artelys, FICO and NVIDIA cuOpt Join Efforts to Scale Up Energy System Optimisation

As energy system models continue to scale—reaching up to hundreds of millions of variables and constraints—traditional CPU-based optimisation solvers are hitting performance and memory bottlenecks. These increasingly complex models are essential for planning Europe’s energy transition, yet solving them within realistic timeframes has become a pressing challenge.

read more

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Artelys Knitro 10.3 : support de Python 3 et amélioration des performances !

29 August 2017

— Une nouvelle version d’Artelys Knitro est disponible ! Cette mise à jour apporte le support de Python 3 et des améliorations significatives de l’interface R.

D’un point de vue numérique, la résolution de certaines classes de problèmes mal conditionnés est considérablement accélérée. Cette amélioration est rendue possible par des techniques de pré-conditionnement des sous-problèmes résolus par gradient conjugué dans les algorithmes de point intérieur de Knitro. De plus, Artelys Knitro 10.3 comprend une nette amélioration de l’algèbre linéaire qui rend le solveur plus stable et plus efficace.

Amélioration des performances sur une sélection d’instances de moindres carrés

Autres fonctionnalités :

• Meilleures performances sur les problèmes de moindres carrés de grande taille, un temps de calcul divisé par 10 !
• Améliorations de la gestion de la faisabilité permettant une meilleure détection des problèmes infaisables.
• Augmentation des performances et de la robustesse sur les problèmes non linéaires généraux ainsi que sur les problèmes avec variables entières.

Supercharging Optimisation: How Artelys, FICO and NVIDIA cuOpt Join Efforts to Scale Up Energy System Optimisation

Supercharging Optimisation: How Artelys, FICO and NVIDIA cuOpt Join Efforts to Scale Up Energy System Optimisation

As energy system models continue to scale—reaching up to hundreds of millions of variables and constraints—traditional CPU-based optimisation solvers are hitting performance and memory bottlenecks. These increasingly complex models are essential for planning Europe’s energy transition, yet solving them within realistic timeframes has become a pressing challenge.

read more
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