Newton's method
Newton's method uses the derivative of a differentiable function to approximate its real or complex roots. For a function , the approximations are defined recursively by
To begin the recursion, an initial guess
must be chosen. Often the choice of
determines which of several possible roots is found, and in some cases the initial guess can cause the recursion to cycle or diverge instead of converging to a root.
Contents
Derivation
At each step of the recursion, we have and seek a root of
. Since all nonconstant linear functions have exactly one root, as long as
we can construct a tangent-line approximation to
and find its root as an approximation. The tangent-line approximation of
about
is
We seek the value
such that the above expression equals
; hence,
Dividing by
(as long as
),
Therefore, the desired value of
is
Worked example
Problem: Find the values of the roots of .
Solution: We could use the quadratic formula to find that the roots are , but this approach does not immediately yield a decimal value.
To form a guess, we note that is slightly larger than
, so a suitable guess for the greater root is
.
The derivative of is
by the power rule for derivatives. Therefore, the recursive formula is
Starting at , we apply this formula repeatedly:
Because as calculated, the ratio
must be very close to
. Since
is not too large,
must be quite close to
, so
is a very good estimate of the greater root.
The sum of the roots is by Vieta's formulas, so the lesser root is simply
.
Failure cases
Although powerful, Newton's method is delicate and can be very sensitive to the initial guess and type of function, even when the function is differentiable everywhere.
Zero derivative
Suppose in the above example of finding a root of , we had started with
. In the process of calculating
, then, we would have to divide by
, creating an undefined result.
Wrong root
Suppose we try to estimate the greater root as before, but start with . Applying Newton's method to
yields the following sequence of estimates:
These estimates converge to the lesser root, in this case because the initial estimate was closer to the lesser root than the greater root. In general, predicting which root Newton's method will finally converge to is difficult, and Newton's method may converge to a farther root even if a closer root is available within the interval between the initial estimate and the root.
Periodic behavior without finding a root
When finding roots of as above, the estimates eventually stabilize into a predictable pattern: in the
case,
, so every subsequent estimate will equal
; in the
case,
, so the subsequent estimates will oscillate, with every even estimate equal to
and every odd estimate equal to
. These behaviors are due to the limited precision of the calculations; with indefinite precision, the estimates would continue to approach the root without becoming periodic.
However, for some functions, Newton's method can cycle without coming close to a root.
No root (divergent)
The function is always positive, so it has no roots. Attempting to apply Newton's method yields a divergent sequence: since
satisfies the differential equation
for all
, the ratio
is always
, so the sequence of estimates becomes
, diverging in the negative direction.
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