SciPy's optimization package is scipy.optimize. The most basic non-linear optimization functions are:
- optimize.fmin(func, x0), which finds the minimum of f(x) starting x with x0 (x can be a vector)
- optimize.fsolve(func, x0), which finds a solution to func(x) = 0 starting with x = x0 (x can be a vector)
- optimize.fminbound(func, x1, x2), which finds the minimum of a scalar function func(x) for the range [x1,x2] (x1,x2 must be a scalar and func(x) must return a scalar)
See the scipy.optimze documentation for details.
This is a quick demonstration of generating data from several Bessel functions and finding some local maxima using fminbound. This uses ipython with the -pylab switch.
1 from scipy import optimize, special 2 from numpy import * 3 from pylab import * 4 5 x = arange(0,10,0.01) 6 7 for k in arange(0.5,5.5): 8 y = special.jv(k,x) 9 plot(x,y) 10 f = lambda x: -special.jv(k,x) 11 x_max = optimize.fminbound(f,0,6) 12 plot([x_max], [special.jv(k,x_max)],'ro') 13 14 title('Different Bessel functions and their local maxima') 15 show()
#class left inline:NumPyOptimizationSmall.png Optimization Example