Why? Speed and integration.
I’m not sure I understand what you mean by integration.
Worse in what sense? Speed? Almost certainly not, since you would be implementing the solution directly in C++, so it will be as fast as you need(since you can use inline ASM). In Julia the really good diffeq libraries are not built in and this is not a diffeq solving library, so you would have to implement them yourself if you were just using this(so you can use almost exactly the same algorithms, and with more control over memory management in C++ you can maybe have it be a bit faster).
These things may not matter to your application, but performance for me is more than just linear algebra. With C++ it’s straightforward to manage the working set so I can keep data with computational locality together, manage prefetching at the like. I can run the algos on multiple cores (no GIL, for example). Etc etc.
There is plenty of performance comparison between C++ and Python on the web, but alone the statistics mean nothing. Is your speed of development more important than runtime? For many people that would make Python faster. In my case I don’t use C++ as if it is C with other stuff bolted on so my development is just as fast in either language, so I choose the one which gives me more expressive power.
It’s not like I’m dissing Julia or Python, though my go to for little explorations is Common Lisp. They are just designed for different points in the solution space.
> I’m not sure I understand what you mean by integration.
Sorry, I didn’t mean mathematical integration, I meant integration with OpenCV, various robot hardware etc, as well as deeper integration with the hardware.
> There is plenty of performance comparison between C++ and Python on the web Sorry if I wasn't clear myself. I didn't mean for general python vs c++ comparison. I meant Castor vs the things it is trying to position itself as an alternative to (e.g. numpy or julia).
The big trade-off is you're specifying the task more precisely and as a result of that extra labor you get better performance for the life of the tool. SQLITE has a good story of putting together dozens of small optimizations, each <1% performance change, and at the end the library's throughput doubled in pretty much all tests. It's not worth it for research, but it is everywhere else.