Hi Robert, Thanks a lot for your kindness.
Robert Kern wrote: > [EMAIL PROTECTED] wrote: >> Hi all, >> >> I have two questions about scipy. > > You're likely to get a better response from the scipy mailing list. Here, you'll > primarily get me, and I have to rush out right now. > > http://www.scipy.org/Mailing_Lists In fact, I have tried to subscribe to the list, but got no response. I will try again. > >> 1) When I was trying to solve a single variable equations using scipy, I >> found two methods: scipy.optimize.fsolve, which is designated to find the >> roots of a polynomial, > > No, it finds the roots of a non-linear system of N functions in N variables. The > documentation makes no mention of polynomials. > >> and scipy.optimize.newton, which is used for Scalar >> function root finding according to the help(). > > There's also brentq, brenth, ridder, and bisect for this problem. > >> I have tried both, and it seemed that both worked well, and fsolve ran >> faster. >> >> My questions is, which is the right choose ? > > Whichever one works faster and more robustly for your problem. fsolve is > implemented in FORTRAN, which sometimes helps. I do recommend looking at the > brentq and brenth if you can provide bounds rather than just an initial guess. "brentq" or "brenth" are used to find a zero of the function f between the argument a and b. It demanded that f(a) and f(b) can not have the same signs. I think it is very convenient to provide valid a and b before I've got the solution of the function. > >> 2) I have to solve a linear equation, with the constraint that all >> variables should be positive. Currently I can solve this problem by >> manually adjusting the solution in each iteration after get the solution >> bu using scipy.linalg.solve(). >> >> Is there a smart way ? > > I don't think that's a well-defined problem. Either the (unique) solution is I found a web page about optimization solver in openoffice( http://wiki.services.openoffice.org/wiki/Optimization_Solver#Non-Linear_Programming). Openoffice has an option of "Allow only positive values", so I think that's a well-defined problem. Sorry for my ignorance if I was wrong. > within the constraint or it's not. Are you sure you don't want to find the > minimum-error solution that obeys the constrain, instead? Yes, I do want to find the minimum-error solution that obeys the constrain. Please tell me more ! Regards, Xiaojf -- http://mail.python.org/mailman/listinfo/python-list
