o(1) is "little O" notation, where for a function y = o(f), y(x) / f(x) --> 0 as x --> infinity. In other words, y goes to 0 faster than f. In this context, m^(1 + o(1)) means roughly that you converge to linear time…
Super cool! How does this compare with Casadi? I didn't see it mentioned in the related AD frameworks in the paper, but I think that targets a similar niche? Thanks for releasing the library!
I am also an alum. (Some of) Brown's tracking information isn't even particularly restricted. I have seen firsthand that live location information based on your device being connected to the network (I assume based on…
o(1) is "little O" notation, where for a function y = o(f), y(x) / f(x) --> 0 as x --> infinity. In other words, y goes to 0 faster than f. In this context, m^(1 + o(1)) means roughly that you converge to linear time…
Super cool! How does this compare with Casadi? I didn't see it mentioned in the related AD frameworks in the paper, but I think that targets a similar niche? Thanks for releasing the library!
I am also an alum. (Some of) Brown's tracking information isn't even particularly restricted. I have seen firsthand that live location information based on your device being connected to the network (I assume based on…