How to solve a pair of nonlinear equations using Python?

Question

What's the (best) way to solve a pair of non linear equations using Python. (Numpy, Scipy or Sympy)

eg:

• x+y^2 = 4
• e^x+ xy = 3

A code snippet which solves the above pair will be great

Answer 1

for numerical solution, you can use fsolve:

http://docs.scipy.org/doc/scipy/reference/generated/scipy.optimize.fsolve.html#scipy.optimize.fsolve

from scipy.optimize import fsolve
import math

def equations(p):
    x, y = p
    return (x+y**2-4, math.exp(x) + x*y - 3)

x, y =  fsolve(equations, (1, 1))

print equations((x, y))

Answer 2

If you prefer sympy you can use nsolve.

>>> nsolve([x+y**2-4, exp(x)+x*y-3], [x, y], [1, 1])
[0.620344523485226]
[1.83838393066159]

The first argument is a list of equations, the second is list of variables and the third is an initial guess.

Answer 3

Try this one, I assure you that it will work perfectly.

    import scipy.optimize as opt
    from numpy import exp
    import timeit

    st1 = timeit.default_timer()

    def f(variables) :
        (x,y) = variables

        first_eq = x + y**2 -4
        second_eq = exp(x) + x*y - 3
        return [first_eq, second_eq]

    solution = opt.fsolve(f, (0.1,1) )
    print(solution)


    st2 = timeit.default_timer()
    print("RUN TIME : {0}".format(st2-st1))

->

[ 0.62034452  1.83838393]
RUN TIME : 0.0009331008900937708

FYI. as mentioned above, you can also use 'Broyden's approximation' by replacing 'fsolve' with 'broyden1'. It works. I did it.

I don't know exactly how Broyden's approximation works, but it took 0.02 s.

And I recommend you do not use Sympy's functions <- convenient indeed, but in terms of speed, it's quite slow. You will see.

Answer 4

from scipy.optimize import fsolve

def double_solve(f1,f2,x0,y0):
    func = lambda x: [f1(x[0], x[1]), f2(x[0], x[1])]
    return fsolve(func,[x0,y0])

def n_solve(functions,variables):
    func = lambda x: [ f(*x) for f in functions]
    return fsolve(func, variables)

f1 = lambda x,y : x**2+y**2-1
f2 = lambda x,y : x-y

res = double_solve(f1,f2,1,0)
res = n_solve([f1,f2],[1.0,0.0])

Answer 5

You can use openopt package and its NLP method. It has many dynamic programming algorithms to solve nonlinear algebraic equations consisting:
goldenSection, scipy_fminbound, scipy_bfgs, scipy_cg, scipy_ncg, amsg2p, scipy_lbfgsb, scipy_tnc, bobyqa, ralg, ipopt, scipy_slsqp, scipy_cobyla, lincher, algencan, which you can choose from.
Some of the latter algorithms can solve constrained nonlinear programming problem. So, you can introduce your system of equations to openopt.NLP() with a function like this:

lambda x: x[0] + x[1]**2 - 4, np.exp(x[0]) + x[0]*x[1]

Answer 6

I got Broyden's method to work for coupled non-linear equations (generally involving polynomials and exponentials) in IDL, but I haven't tried it in Python:

http://docs.scipy.org/doc/scipy/reference/generated/scipy.optimize.broyden1.html#scipy.optimize.broyden1

scipy.optimize.broyden1

scipy.optimize.broyden1(F, xin, iter=None, alpha=None, reduction_method='restart', max_rank=None, verbose=False, maxiter=None, f_tol=None, f_rtol=None, x_tol=None, x_rtol=None, tol_norm=None, line_search='armijo', callback=None, **kw)[source]

Find a root of a function, using Broyden’s first Jacobian approximation.

This method is also known as “Broyden’s good method”.

Answer 7

An alternative to fsolve is root:

import numpy as np
from scipy.optimize import root    

def your_funcs(X):

    x, y = X
    # all RHS have to be 0
    f = [x + y**2 - 4,
         np.exp(x) + x * y - 3]

    return f

sol = root(your_funcs, [1.0, 1.0])
print(sol.x)

This will print

[0.62034452 1.83838393]

If you then check

print(your_funcs(sol.x))

you obtain

[4.4508396968012676e-11, -1.0512035686360832e-11]

confirming that the solution is correct.