Use-case
The ECHO project (Expedited Constrained Hierarchical Optimization) is an automated planning process developed at MSKCC based on a hierarchical constrained optimization technique to prioritize clinical objectives. The automation relies on complex large-scale optimization models solved with Artelys Knitro. One key feature that speeds up the resolution of such complex models is the problem structure exploitation to automatically apply convex specializations.
The automation of radiation therapy cancer treatment results in faster treatment delivery, more accurate tumor irradiation, and healthy tissue sparing. The method has been implemented by the hospital since April 2017 and was already successfully used in the treatment process of more than 800 patients!
Start with a tutorial!
You’re not familiar with nonlinear optimization? This tutorial will present some examples of nonlinear problems for various applications. You will discover nonlinear programming methods using the Artelys Knitro solver in a Python notebook, through different examples.
Free trial
Get your trial license to test Artelys Knitro’s performances on your own mathematical optimization problem. The trial package includes free support and maintenance. You can have access to Artelys Knitro for free with a 1-month unlimited version or a 6-month limited version.
Artelys Knitro has unmatched performance
Best Nonlinear
Solver
Artelys Knitro has been ranked every year by public benchmarks consistently showing Artelys Knitro finds both feasible and proven optimal solutions faster than competing solvers.
Technical
support
The Artelys technical support team comprises Artelys’consultants (PhD-level) who are used to solving the most difficult problems and deploying enterprise-wide optimization solutions. They can advise on algorithmic or software features that may result in enhanced performance in your usage of Artelys Knitro.
Updates and
new features
The development team works continuously to provide two releases of Artelys Knitro every year. Based on feedback, we always improve our solver to meet users’ requirements and need to solve larger models faster.
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