pythonnumpyscipydifferential-equationsodeint

How to solve differential equation using Python builtin function odeint?


I want to solve this differential equations with the given initial conditions:

(3x-1)y''-(3x+2)y'+(6x-8)y=0, y(0)=2, y'(0)=3

the ans should be

y=2*exp(2*x)-x*exp(-x)

here is my code:

def g(y,x):
    y0 = y[0]
    y1 = y[1]
    y2 = (6*x-8)*y0/(3*x-1)+(3*x+2)*y1/(3*x-1)
    return [y1,y2]

init = [2.0, 3.0]
x=np.linspace(-2,2,100)
sol=spi.odeint(g,init,x)
plt.plot(x,sol[:,0])
plt.show()

but what I get is different from the answer. what have I done wrong?


Solution

  • There are several things wrong here. Firstly, your equation is apparently

    (3x-1)y''-(3x+2)y'-(6x-8)y=0; y(0)=2, y'(0)=3

    (note the sign of the term in y). For this equation, your analytical solution and definition of y2 are correct.

    Secondly, as the @Warren Weckesser says, you must pass 2 parameters as y to g: y[0] (y), y[1] (y') and return their derivatives, y' and y''.

    Thirdly, your initial conditions are given for x=0, but your x-grid to integrate on starts at -2. From the docs for odeint, this parameter, t in their call signature description:

    odeint(func, y0, t, args=(),...):

    t : array A sequence of time points for which to solve for y. The initial value point should be the first element of this sequence.

    So you must integrate starting at 0 or provide initial conditions starting at -2.

    Finally, your range of integration covers a singularity at x=1/3. odeint may have a bad time here (but apparently doesn't).

    Here's one approach that seems to work:

    import numpy as np
    import scipy as sp
    from scipy.integrate import odeint
    import matplotlib.pyplot as plt
    
    def g(y, x):
        y0 = y[0]
        y1 = y[1]
        y2 = ((3*x+2)*y1 + (6*x-8)*y0)/(3*x-1)
        return y1, y2
    
    # Initial conditions on y, y' at x=0
    init = 2.0, 3.0
    # First integrate from 0 to 2
    x = np.linspace(0,2,100)
    sol=odeint(g, init, x)
    # Then integrate from 0 to -2
    plt.plot(x, sol[:,0], color='b')
    x = np.linspace(0,-2,100)
    sol=odeint(g, init, x)
    plt.plot(x, sol[:,0], color='b')
    
    # The analytical answer in red dots
    exact_x = np.linspace(-2,2,10)
    exact_y = 2*np.exp(2*exact_x)-exact_x*np.exp(-exact_x)
    plt.plot(exact_x,exact_y, 'o', color='r', label='exact')
    plt.legend()
    
    plt.show()
    

    enter image description here