[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 > 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. > 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 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? -- Robert Kern "I have come to believe that the whole world is an enigma, a harmless enigma that is made terrible by our own mad attempt to interpret it as though it had an underlying truth." -- Umberto Eco -- http://mail.python.org/mailman/listinfo/python-list
