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scipy.optimize now offers mixed integer programming solution methods through a HiGHS interface. This is currently in the main branch and will be part of the v1.9.0 release, which should be cut soon. We may want to see about incorporating this new functionality due to the COIN-OR funding situation.
The text was updated successfully, but these errors were encountered:
It seems a good tool. Maybe we should do an example for a simple locate model like LSCP? Then we can discuss about API changes and other enhancements to adopt that API. What do you think?
pulp now supports HiGHS. So now we can easily implement that solver in a demo/example. And if we decide to support our MIPs through scipy.optimize with HiGHS later, it will provide an easier transition for testing and ensuring equivalent solutions are obtained.
I have have successfully installed HiGHS and used from the pulp interface. The demo runs will be in the facloc_real-world_* notebooks and added in #300.
scipy.optimize
now offers mixed integer programming solution methods through a HiGHS interface. This is currently in themain
branch and will be part of thev1.9.0
release, which should be cut soon. We may want to see about incorporating this new functionality due to the COIN-OR funding situation.The text was updated successfully, but these errors were encountered: