The roots of a polynomial, as you might have learned in highschool, are the intersections of the polynomial curve through the x-axis. The problem with the graphical method of finding roots is that not all of them are real. Actually, creating random polynomials will create mostly complex roots.

That is exactly what I did today. Create random polynomials, and try to find a pattern in their roots. Turns out that by plotting their roots in a complex plane a pattern emerges.

For this task I’ll use python’s scipy library, which handles polynomials quite well. More specifically, the poly1d class.

%matplotlib inline
import scipy as sp
import numpy as np
import matplotlib.pyplot as plt


A polynomial is represented by it’s coefficients, the number that multiplies every order of the variable. The class poly1d takes as argument a list with these coefficients, and, to find the roots of a polynomial we’ll use scipy’s roots function.

p = sp.poly1d([1,-2,1])
sp.roots(p)

array([ 1.,  1.])


What I’ve done is plot a hundred polynomials, with coefficients 1, 0 or -1, for every order in the range 3 to 20, this means that there will be a hundred polynomials of second order, a hundred of third order, and so on… The next plot shows their position in the complex plane:

plt.figure(figsize=(16,10))
l = []
for i in range(3,21): # Degree of polynomial
for n in range(100): # How many polynomials?
p = sp.poly1d(np.random.randint(-1,2,i))
r = sp.roots(p)
for root in r:
l.append(root)
plt.show() Inspecting the plot we see that all the complex roots are inscribed inside a circle of radius 1.6, but never closer than 0.5 to the center of the circle (the complex point [0,0]). The radius of these circles is only dependent of the values of the polynomial’s coefficients, and not on the order of this.

lc = []
for root in l:
if root.imag == 0:
continue
lc.append(root)

Maximum radius: 1.56747624526