Euler's Method

Taha Azzaoui - 2018.02.16

“Euler’s Method? bUt ThAt’S aNcIeNt!”

I recently saw the movie Hidden Figures in which there was a scene where a group of mathematicians are trying to calculate the trajectory of a capsule. As the group is stuck brainstorming, Katherine Johnson heroically exclaims that their problem can be solved using Euler’s method. The scene can be seen here. Now, the scene is obviously yet another example of Hollywood dramatizing mathematics, as it’s hard to believe that an elite group of mathematicians employed by NASA would have overlooked a solution as trivial as Euler’s method. In any event, this post seeks to outline the method in all of its simplicity.


In any introductory course on the theory of ordinary differential equations, a significant amount of time is dedicated to the study of a special class of first order differential equations for which there exists an analytic method of solving. These include a subset of the separable equations, linear equations, exact equations, etc. However, as it turns out, a vast majority of differential equations do not have an analytic solution. So what happens when we encounter a differential equation that cannot be solved analytically? Well, we can either (a) pack up our bags and head home or (b) look for a method that yields an approximation which we consider to be “good enough” for the application to any real world problem. For those interested in option b, the field of Numerical Analysis has proven to be incredibly useful in the field of engineering. Below is a description of a simple method for numerically integrating differential equations which was presented by Leonhard Euler in the 18th century.

The Problem

Suppose we have the first order ODE: $\frac{dy}{dx} = f(x,y)$ with some known initial value y(x0) = y0. We assume the necessary conditions for the existence of a unique solution, namely that f and fy are continuous.

What we know

  1. The value of the function at x0
  2. The derivative of the function at x0, $\frac{dy}{dx}|_{x = x_0}$
  3. Putting the two together, we also know the equation of the line tangent to the solution curve at x0. i.e. y = y0 + f(x0, y0)(x − x0)

The Tangent Line Approximation

Observe the following figure. In the red is the graph of f(x) = x2 and in the blue is the line tangent to f(x), call it g(x) at the point x = 1. Note that f(1) = g(1), by the definition of a tangent line. However, a more interesting property of the tangent line arises in the context of values near x = 1. It turns out, as you can probably convince yourself from the figure below, that for the values of x proximal to the point of tangency, the values of g(x) are approximately equal to the values of f(x). Stated more formally, let x0 be the point of tangency and let 0 < ε < 1. Then…
g(x′) ≈ f(x′) ∀ x′ ∈ [x0 − ε, x0 + ε]
$$\lim_{\varepsilon\to0} \frac{f(x_0)}{g(x_0 + \epsilon)} = 1$$
i.e. The smaller the ε, the more accurate the approximation

The Method

Using the tangent line, we now have a function that takes a point on the solution curve (x0, y0) and the derivative at that point, and returns a nearby point on the solution curve, namely (x0 + ε, y1). Now that we have an approximate nearby solution, the question then remains: how do we approximate the solution for values farther away from the point of tangency? As evident by the figure above, the tangent line is not a very good estimator of values far from the point of tangency.

The trick is to take an iterative approach. We start by using the initial conditions (x0, y0) to approximate a nearby point on the solution curve, namely (x0 + ε, y1). The crucial step is to note that if y1 is a good approximation of the actual solution curve at x = x0 + ε, then we can use it to form another line tangent to f(x) at the point x0 + ε. We then use this new tangent line to approximate the value of the solution curve nearby the new point of tangency, namely (x0 + 2ε, y2). We continue this process of using the previous step’s estimate to make a new estimate for as many steps as we see fit. We call varepsilon our step size. After running the algorithm, we end up with the following set of points…
{(x0, y0), (x0 + ε, y1), (x0 + 2ε, y2)...(x0 + nε, yn)}
Where n is the number of iterations. We can then plot the solution curve by connecting these points.

We can apply Euler’s method to our example, $\frac{dy}{dx} = x^2$, letting ε = 0.1 for n = 50 steps. In the figure above, the orange points are found via Euler’s method and the black curve is the actual solution, namely $y = \frac{1}{3}x^3$ (surprise!). Note the similarity between the two sets of values.

Below is a snippet of python code that implements Euler’s method, the full code is available here.

Euler's Numerical Integration Method
x0: Initial x-value 
y0: Initial y-value
epsilon: Step Size
n: Number Of Steps

def euler(f,x0,y0,epsilon,n):
  x = x0
  y = y0
  for i in range(n):
    print "({},{})".format(x,y)
    y += epsilon * f(x, y)
    x += epsilon
    return y

# Example function f(x) = x^2
def f(x,y):
  return x*x

x0 = y0 = 0
epsilon = 0.1
n = 50


While Euler’s method is useful for approximating values of trivial ODEs like the one above, it is not considered to be sufficiently accurate. This is due to the fact that as the number of steps increases, error begins to accumulate and the estimates tend to diverge from the actual value. See here for a complete discussion on the shortcomings of Euler’s method. That being said, Euler’s method is still desirable in light of its simplicity and has been used as a basis for more complicated numerical integration techniques including that of Heun.