As far as i can tell, simulated annealing is quite out of touch with more modern optimization strategies. See http://scicomp.stackexchange.com/questions/3372/simulated-annealing-proof-of... and the references mentioned, so more optimization algorithms are good. But i think the scipy staff should decide the scope of scipy: either add almost every proved and usable algorithm with an python implementation or only the provide the most famous. I prefer the first way. OT things i would like to see in scipy, if anybody has too much time: * non-negative matrix factorization * a data fitting function, aka curve_fit on steroids. Maybe lmfit, kaptyn or mpfit. * an bouded linear least squares solver, like bvls. * Bayesian frequency estimation * NFFT