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Solve Equations in Python

The following tutorials are an introduction to solving linear and nonlinear equations with Python. The solution to linear equations is through matrix operations while sets of nonlinear equations require a solver to numerically find a solution.

Solve Linear Equations with Python

Source Code for Linear Solutions

import numpy as np

A = np.array([ [3,-9], [2,4] ])
b = np.array([-42,2])
z = np.linalg.solve(A,b)
print(z)

M = np.array([ [1,-2,-1], [2,2,-1], [-1,-1,2] ])
c = np.array([6,1,1])
y = np.linalg.solve(M,c)
print(y)
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Solve Nonlinear Equations with Python

Source Code for Nonlinear Solution

from numpy import *
from scipy.optimize import *

def myFunction(z):
   x = z[0]
   y = z[1]
   w = z[2]

   F = empty((3))
   F[0] = pow(x,2)+pow(y,2)-20
   F[1] = y - pow(x,2)
   F[2] = w + 5 - x*y
   return F

zGuess = array([1,1,1])
z = fsolve(myFunction,zGuess)
print(z)
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Symbolic Solutions

Sympy is a package for symbolic solutions in Python that can be used to solve systems of equations.

$$2x^2+y+z=1$$ $$x+2y+z=c_1$$ $$-2*x+y=-z$$

import sympy as sym
sym.init_printing()
x,y,z = sym.symbols('x,y,z')
c1 = sym.Symbol('c1')
f = sym.Eq(2*x**2+y+z,1)
g = sym.Eq(x+2*y+z,c1)
h = sym.Eq(-2*x+y,-z)sourceend:)

sym.solve([f,g,h],(x,y,z))
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When solved in an IPython environment such as a Jupyter notebook, the result is displayed as:

$$x=-\frac{1}{2}+\frac{\sqrt{3}}{2}$$ $$y=c_1 - \frac{3 \sqrt{3}}{2} +\frac{3}{2}$$ $$z=-c_1 - \frac{5}{2} +\frac{5 \sqrt{3}}{2}$$

and a second solution:

$$x=-\frac{1}{2}-\frac{\sqrt{3}}{2}$$ $$y=c_1 + \frac{3 \sqrt{3}}{2} +\frac{3}{2}$$ $$z=-c_1 - \frac{5}{2} -\frac{5 \sqrt{3}}{2}$$

The same approach applies to linear or nonlinear equations.

Additional Tutorials

Linear and nonlinear equations can also be solved with Excel and MATLAB. Click on the appropriate link for additional information and source code.

The APMonitor Modeling Language with a Python interface is optimization software for mixed-integer and differential algebraic equations. It is coupled with large-scale solvers for linear, quadratic, nonlinear, and mixed integer programming (LP, QP, NLP, MILP, MINLP). Modes of operation include data reconciliation, real-time optimization, dynamic simulation, and nonlinear predictive control. It is freely available through MATLAB, Python, Julia, or from a web browser interface.

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