The new feature introduced in version 9.2 is parametrization of Fusion models, that is the ability to build a Fusion model containing parameters and reoptimize it multiple times for varying input data without rebuilding the model from scratch. This is particularly useful for optimizing many instances of a problem with identical structure, where some of the input data change.
For more information, with links to examples in C++, Python, Java and C#, see our website:
In near future we will publish more examples of parametrized models as well as related benchmarks.
