2007/10/26, Steffen <[EMAIL PROTECTED]>: > 1) Polynomial with max number of monomial. We dont need to worry about > that case, since here all the monomial are chosen, that means actually > there is nothing to choose. So this will be efficient anyway. > 2) A user wants an exact < totalmax number of monomials. In this case > its difficult to reach real randomness in an efficient way. Martins > and my proposal in this case was to except collisions during the > implementation, that is choose a random monomial and throw it away if > already chosen. This is not most efficient, the expectation > calculation period has 2 * the optimal time as upper bound. > 3) A user wants a real random polynom with a certain number of > monomials, but there is no need to fix a maximum number of monomials. > An efficient implementation here would be: > #m = maxNumberOfMonomials = "calculate it" > #d = desiredNumberOfMonomials = "choose it" > Iterate over the list of all monomials, with probability #d/#m choose > this monomial. > This would be faster than 2) and would return a polynomial with an > expectation value of monomials of #d
Could you give an example for each of these cases? I'm having trouble distinguishing the difference between 2) and 3) Mike, your code had a subtle bug, where random_monomials(n,degree,terms) failed each time for degree =1 (but was fine for degree=0). didier --~--~---------~--~----~------------~-------~--~----~ To post to this group, send email to sage-devel@googlegroups.com To unsubscribe from this group, send email to [EMAIL PROTECTED] For more options, visit this group at http://groups.google.com/group/sage-devel URLs: http://sage.scipy.org/sage/ and http://modular.math.washington.edu/sage/ -~----------~----~----~----~------~----~------~--~---
