Solving simultaneous equations in python -

i have following test program. query 2 folded: (1) how solution giving 0 , (2) appropriate use x2= np.where(x > y, 1, x) kind of conditions on variables ? there constrained optimization routines in scipy ?

a = 13.235 b = 70.678  def system(x, a,b):     x=x[0]     y=x[1]      x2= np.where(x > y, 1, x)          f=np.zeros(3)     f[0] = 2*x2 - y -     f[1] = 3*x2 + 2*y- b       return (x)   func=  lambda x: system(x, a, b)  guess=[5,5] sol =  optimize.root(func,guess) print(sol) 

edit: (2a) here x2= np.where(x > y, 1, x) condition, 2 equations becomes 1 equation. (2b) in variation requirement is: x2= np.where(x > y, x^2, x^3). let me comments on these 2 well. !

first up, system function identity, since return x instead of return f. return should same shape x had better have

f = np.array([2*x2 - y - a, 3*x2 + 2*y- b]) 

next function, written has discontinuity x=y, , causing there be problem initial guess of (5,5). setting initial guess (5,6) allows the solution [13.87828571, 14.52157143] found rapidly.

with second example, again using initial guess of [5,5] causes problems of discontinuity, using [5,6] gives solution of [ 2.40313743, 14.52157143].

here code:

import numpy np scipy import optimize  def system(x, a=13.235, b=70.678):     x = np.where(x[0] > x[1], x[0]**2, x[0]**3)     y=x[1]         return np.array( [2*x - y - a, 3*x + 2*y - b])  guess = [5,6] sol = optimize.root(system, guess) print(sol)