preygel

4 ms·

1.
by preygel 10y ago
Typically a mix of reasons: - Needing to interact with an existing codebase, and an existing developer base. If everyone knows and uses Python and only a few use Julia, it is too early to put Julia in production. If there are proprietary
2.
by preygel 10y ago
Exciting to hear -- I have used Julia for prototyping, and have found it to be excellent for that: In my experience there are still some rough edges as compared to the Python ecosystem (of course!), which together with the 0.x status make i
3.
by preygel 10y ago
Absolutely! Not to mention the often impressive performance of "general" MIP solvers. It is only a shame that the best ones there are commercial (Gurobi followed by Cplex). That said, Cbc is lovely in a wide range of cases, and
4.
by preygel 10y ago
A bit tangentially, this is also a great display of just how good readily available approximate solvers have gotten for a wide range of combinatorial optimization problems (like TSP). The LKH solver used here has rather impressive perform
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by preygel 10y ago
The distinction is being made on end outputs -- is it more like a Powerpoint deck, or more like a production data pipeline -- not on techniques. "Machine learning" includes plenty of activities that can be used to provide evidence
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by preygel 10y ago
Math PhD here. I have also had the chance to work with many bright "analytically-minded" people from other backgrounds while a management consultant. This article is spot on, and some of the behaviors really do seem more indicati