# How to solve the problem without using symbolic computation

I have the following simple nonlinear equations with two unknowns only:

$$\left\{ \begin{array}{c} \int_1^2{\dfrac{ e^{{a_1} x+{a_2} x^3}}{1+x^2}} \, dx=1 \\[13pt] \int_1^2{ x^2 e^{{a_1} x+{a_2} x^3}} \, dx=\dfrac{1}3 \\ \end{array} \right.$$

I understand it is almost impossible to obtain all the possible numerical solutions in the complex field $\mathbb{C}$. So any numerical complex solution per a specific given initial value with good enough accuracy would be OK.

By symbolic-numerical computation, it is easy to obtain one solution:

$$\left\{\quad \begin{array}{ccrcr} {a_1}&= & 2.083619981922075 &-& 2.163052112144277\; i \\ {a_2}&= & -0.144311264409679 &+& 1.206590594556891\; i \\ \end{array} \right.$$

However, I found it difficult to handle such a problem using the built-in integral functions in Matlab since such integral and quadgk functions as accept function handle-form only integrands.

My try is first composing the nonlinear equations into user defined functions, then solve it per iteration algorithm (e.g. Newton's method) with a specific initial value), but when I tried to pass symbolic variables or global variables $a_1$, $a_2$, problems (error messages) would happen.

Though symbolic computation is possible to obtain a solution, it is too slow especially for more general nonlinear equations. Since the solution I need is only numerical one, I wonder:

Is it possible to solve such a problem by Matlab without using the symbolic computation? If the integral function has to accept symbolics $a_1$, $a_2$, how to handle it in Matlab (or C++)?

• What errors did you get? Can you post the code? The literal answer is that yes it's possible to do what you want, but maybe not in the way that you tried to program it. Your best choice is probably to give up on using symbolic variables altogether, code the Jacobian/derivative of your function as a function of numeric variables, and implement Newton's method with regular variables. – Bill Barth Apr 26 '16 at 13:27
• You can of course avoid symbolic in Matlab. Just define the function for integration inside the function for the nonlinear equations. – Hui Zhang Apr 30 '16 at 2:34
• The bottle-neck is that I am trying to use the Matlab built-in integral functions, integral and/or quadgk without composing my own, but it seems impossible. integral does not accept integrand with arguments to be determined. – user6043040 May 1 '16 at 5:32
• @BillBarth I did not have a code that works because when I saw the online documents of the built-in function I realized it is impossible. Unless I rewrite the integral function myself. Numerical integration is more complicated than I expected especially when there are singularity points of integrand to handle, that makes further generalization to other integrand difficult. thanks – user6043040 May 1 '16 at 5:38

You can solve this numerically in Python without symbolic computation.

from __future__ import print_function, division
import numpy as np
from numpy import exp
from scipy.optimize import root

def f1(a1, a2, x):
return exp(a1 * x + a2 * x * x * x) / (1 + x * x)

def f2(a1, a2, x):
return exp(a1 * x + a2 * x * x * x) * x

def g(a1, a2, f):
def r(x):
return f(a1, a2, x).real
def i(x):
return f(a1, a2, x).imag
return y

def func(Z):
a1 = Z[0] + Z[1] * 1j
a2 = Z[2] + Z[3] * 1j
y1 = g(a1, a2, f1) - 1
y2 = g(a1, a2, f2) - 1/3
return np.array([y1.real, y1.imag, y2.real, y2.imag])

X0 = np.array([2.0, -2.0, -0.1, 1.0])
print(root(func, X0, tol=1e-12))


output:

fjac: array([[-0.30890916,  0.05007557,  0.09254457,  0.94525291],
[-0.12460792, -0.41710616, -0.89760745,  0.06925448],
[ 0.41309815, -0.83196711,  0.34050112,  0.14573825],
[ 0.84758358,  0.36241635, -0.26418811,  0.28365668]])
fun: array([ -2.88657986e-15,  -2.49800181e-15,  -1.89293026e-14,
-2.43138842e-14])
message: 'The solution converged.'
nfev: 24
qtf: array([ -9.32740208e-12,   6.42466839e-12,  -3.53520661e-12,
-1.78427395e-12])
r: array([ -3.43272029,   0.72043771, -14.00467579, -11.98268077,
-2.65005807,  13.29034808, -12.53526771,  -7.04373163,
2.58188876,  -7.03891799])
status: 1
success: True
x: array([ 2.08361998, -2.16305211, -0.14431126,  1.20659059])


Notice that I've changed the definition of the second function to use an x term instead of an x^2 term so that it matches your solution :) I guess this was a typo in your question.

• You're right. I made a mistake. Such a nonlinear function was given for demonstration purpose only, so it does not matter. -- I want to see whether it is possible to use Matlab's built-in integration functions because they can handle more general cases even when integrands have singularity points. – user6043040 May 1 '16 at 5:51