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Illustration of the Euler method. The unknown curve is in blue, and its polygonal approximation is in red.
In
mathematics and
computational science, the 'Euler method', named after
Leonhard Euler, is a
numerical procedure for solving
ordinary differential equations (ODEs) with a given
initial value. It is the most basic kind of
explicit method for
numerical integration for ordinary differential equations.
Informal geometrical description
Consider the problem of calculating the shape of an unknown curve which starts at a given point and satisfies a given differential equation. Here, a differential equation can be thought of as a formula by which the
slope of the
tangent line to the curve can be computed at any point on the curve, once the position of that point has been calculated.
The idea is that while the curve is initially unknown, its starting point, which we denote by
is known (see the picture on top right). Then, from the differential equation, the slope to the curve at
can be computed, and so, the tangent line.
Take a small step along that tangent line up to a point
If we pretend that
is still on the curve, the same reasoning as for the point
above can be used. After several steps, a
polygonal curve is computed. In general, this curve does not diverge too far from the original unknown curve, and the error between the two curves can be made small if the step size is small enough and the interval of computation is finite (although things are more complicated for
stiff equations, as discussed below).
Derivation

Illustration of numerical integration for the equation
Blue: the Euler method, green: the
midpoint method, red: the exact solution,
The step size is

The same illustration for It is seen that the midpoint method converges faster than the Euler method.
We want to approximate the solution of the initial value problem
:
by using the first two terms of the
Taylor expansion of ''y'', which represents the linear approximation around the point (t
0,y(t
0)) . One step of the Euler method from ''t''
n to ''t''
n+1 = ''t''
n + ''h'' is
:
The Euler method is explicit, i.e. the solution
is an explicit function of
for
.
While the Euler method integrates a first order ODE, any ODE of order
can be represented as a first-order ODE in more than one variable by introducing
further variables,
,
, ...,
, and formulating
first order equations in these new variables. The Euler method can be applied to the vector
to integrate the higher-order system.
Error
The magnitude of the errors arising from the Euler method can be demonstrated by comparison with a
Taylor expansion of ''y''. If we assume that
and
are known exactly at a time
then the Euler method gives the approximate solution at time
as:
:
(the second equality holds because ''y'' satisfies the differential equation
). In comparison, the Taylor expansion in
about
gives:
:
The error introduced by the Euler method is given by the difference between these equations:
:
For small
, the dominant error per step is proportional to
. To solve the problem over a given range of
, the number of steps needed is proportional to
so it is to be expected that the total error at the end of the fixed time will be proportional to
(error per step times number of steps). For this reason, the Euler method is said to be first order. This makes the Euler method less accurate (for small
) than other higher-order techniques such as
Runge-Kutta methods and
linear multistep methods.
The Euler method can also be numerically
unstable, especially for
stiff equations. This limitation—along with its slow convergence of error with
—means that the Euler method is not often used, except as a simple example of numerical integration.
See also
★
Leonhard Euler
★
Numerical integration of ordinary differential equations
★ For numerical methods for calculating definite integrals, see
Numerical integration
★
Heun's method
★
Midpoint method
★
Runge-Kutta method
External links
★
Euler’s Method
★
Initial Value Problems: Euler’s Method
★
Euler's Method for O.D.E.'s