查看“Scipy”的源代码

http://www.scipy-lectures.org

==积分==
 >>> from scipy.integrate import quad
 >>> def integrand(x, a, b):
 ...     return a*x**2 + b
 >>> a = 2
 >>> b = 1
 >>> I = quad(integrand, 0, 1, args=(a,b))




==求函数最小值==
*Methods based on conjugate gradient are named with ‘cg’ in scipy. The simple conjugate gradient method to minimize a function is scipy.optimize.fmin_cg():
*n scipy, scipy.optimize.fmin() implements the Nelder-Mead approach: (不太依赖于倒数)
*Brute force: a grid search
:scipy.optimize.brute() evaluates the function on a given grid of parameters and returns the parameters corresponding to the minimum value.The parameters are specified with ranges given to numpy.mgrid. By default, 20 steps are taken in each direction:
*Non-linear least squares: Levenberg–Marquardt algorithm implemented in scipy.optimize.leastsq().
*If the function is linear, this is a linear-algebra problem, and should be solved with scipy.linalg.lstsq().