Dear all, I want now to hear your oppinion about one thought.
I am now working on superconductivity, where I need to calculate sets of dependencies, in practice G(E, \phi, \Phi) (superconducting phase and magnetic field). Usually it leads to a huge number of recalculations of Greenโs function on a grid of parameters. As for me, it is quite inefficient. If we know result of one calculation, we can use it as starting point of next calculation, if dependency is not singular, which is often the case. Moreover, seems my usecase is definitely not the only, where we need a dependency of GF/SM on some smooth enough parameter grid. Therefore I think, that it would be quite useful to implement iterative solvers for such cases. This means, that we call our current solver for smatrix/greens_function for initialization, and then use iterative ones to get dependency on a parameter set. I have following questions: 1) Is it useful, or I miss some nice pythonic pattern that already can implement it? ๐ 2) Does anyone have a feeling/knowledge, how this can speed up (or slow down?) calculations? 3) Does someone know nice numeric libraries for iterative linear algebra to test, that can be easily used in Python? I will try to find something myself, but if someone already knows something, I would be glad to know his opinion. With best regards, Viacheslav Ostroukh Instituut-Lorentz โ Niels Bohrweg 2 โ Room 259 โ 2333 CA Leiden ostro...@ilorentz.org sl...@ostroukh.me +31 6 444 968 12 +38 099 721 76 06
