Bisection method

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A few steps of the bisection method applied over the starting range [a1;b1]. The bigger red dot is the root of the function.

The bisection method in mathematics is a root-finding method that repeatedly bisects an interval and then selects a subinterval in which a root must lie for further processing. It is a very simple and robust method, but it is also relatively slow. Because of this, it is often used to obtain a rough approximation to a solution which is then used as a starting point for more rapidly converging methods.[1] The method is also called the interval halving method,[2] the binary search method,[3] or the dichotomy method.[4]

The method[edit]

The method is applicable when we wish to solve the equation f(x) = 0 for the real variable x, where f is a continuous function defined on an interval [ab] and f(a) and f(b) have opposite signs. In this case a and b are said to bracket a root since, by the intermediate value theorem, the f must have at least one root in the interval (a, b).

At each step the method divides the interval in two by computing the midpoint c = (a+b) / 2 of the interval and the value of the function f(c) at that point. Unless c is itself a root (which is very unlikely, but possible) there are now two possibilities: either f(a) and f(c) have opposite signs and bracket a root, or f(c) and f(b) have opposite signs and bracket a root. The method selects the subinterval that is a bracket as a new interval to be used in the next step. In this way the interval that contains a zero of f is reduced in width by 50% at each step. The process is continued until the interval is sufficiently small.

Explicitly, if f(a) and f(c) are opposite signs, then the method sets c as the new value for b, and if f(b) and f(c) are opposite signs then the method sets c as the new a. (If f(c)=0 then c may be taken as the solution and the process stops.) In both cases, the new f(a) and f(b) have opposite signs, so the method is applicable to this smaller interval.[5]

Iteration tasks[edit]

The input for the method is a continuous function f, an interval [a, b], and the function values f(a) and f(b). The function values are of opposite sign (there is at least one zero crossing within the interval). Each iteration performs these steps:

  1. Calculate c, the mid-point of the interval, c = 0.5 * (a + b).
  2. Calculate the function value at the mid-point, f(c).
  3. If convergence is satisfactory (that is, a - c is sufficiently small, or f(c) is sufficiently small), return c and stop iterating.
  4. Examine the sign of f(c) and replace either (a, f(a)) or (b, f(b)) with (c, f(c)) so that there is a zero crossing within the new interval.

When implementing the method on a computer, there can be problems with finite precision, so there are often additional convergence tests or limits to the number of iterations. Although f is continuous, finite precision may preclude a function value ever being zero. For f(x) = x − π, there will never be a finite representation of x that gives zero. Floating point representations also have limited precision, so at some point the midpoint of [ab] will be either a or b.


The method may be written in pseudocode as follows:[6]

INPUT: Function f, endpoint values a, b, tolerance TOL, maximum iterations NMAX
CONDITIONS: a < b, either f(a) < 0 and f(b) > 0 or f(a) > 0 and f(b) < 0
OUTPUT: value which differs from a root of f(x)=0 by less than TOL
N ← 1
While NNMAX # limit iterations to prevent infinite loop
  c ← (a + b)/2 # new midpoint
  If f(c) = 0 or (ba)/2 < TOL then # solution found
  NN + 1 # increment step counter
  If sign(f(c)) = sign(f(a)) then ac else bc # new interval
Output("Method failed.") # max number of steps exceeded

Example: Finding the root of a polynomial[edit]

Suppose that the bisection method is used to find a root of the polynomial

 f(x) = x^3 - x - 2 \,.

First, two numbers  a and  b have to be found such that f(a) and f(b) have opposite signs. For the above function,  a = 1 and  b = 2 satisfy this criterion, as

 f(1) = (1)^3 - (1) - 2 = -2


 f(2) = (2)^3 - (2) - 2 = +4  \,.

Because the function is continuous, there must be a root within the interval [1, 2].

In the first iteration, the end points of the interval which brackets the root are  a_1 = 1 and  b_1 = 2 , so the midpoint is

 c_1 = \frac{2+1}{2} = 1.5

The function value at the midpoint is  f(c_1) = (1.5)^3 - (1.5) - 2 = -0.125 . Because  f(c_1) is negative,  a = 1 is replaced with  a = 1.5 for the next iteration to ensure that  f(a) and  f(b) have opposite signs. As this continues, the interval between  a and  b will become increasingly smaller, converging on the root of the function. See this happen in the table below.

Iteration a_n b_n c_n f(c_n)
1 1 2 1.5 −0.125
2 1.5 2 1.75 1.6093750
3 1.5 1.75 1.625 0.6660156
4 1.5 1.625 1.5625 0.2521973
5 1.5 1.5625 1.5312500 0.0591125
6 1.5 1.5312500 1.5156250 −0.0340538
7 1.5156250 1.5312500 1.5234375 0.0122504
8 1.5156250 1.5234375 1.5195313 −0.0109712
9 1.5195313 1.5234375 1.5214844 0.0006222
10 1.5195313 1.5214844 1.5205078 −0.0051789
11 1.5205078 1.5214844 1.5209961 −0.0022794
12 1.5209961 1.5214844 1.5212402 −0.0008289
13 1.5212402 1.5214844 1.5213623 −0.0001034
14 1.5213623 1.5214844 1.5214233 0.0002594
15 1.5213623 1.5214233 1.5213928 0.0000780

After 13 iterations, it becomes apparent that there is a convergence to about 1.521: a root for the polynomial.


The method is guaranteed to converge to a root of f if f is a continuous function on the interval [a, b] and f(a) and f(b) have opposite signs. The absolute error is halved at each step so the method converges linearly, which is comparatively slow.

Specifically, if c1 = (a+b)/2 is the midpoint of the initial interval, and cn is the midpoint of the interval in the nth step, then the difference between cn and a solution c is bounded by[7]


This formula can be used to determine in advance the number of iterations that the bisection method would need to converge to a root to within a certain tolerance. The number of iterations needed, n, to achieve a given error (or tolerance), ε, is given by: n = \log_2\left(\frac{\epsilon_0}{\epsilon}\right)=\frac{\log\epsilon_0-\log\epsilon}{\log2} ,

where \epsilon_0 = \text{initial bracket size} = b-a .

Therefore, the linear convergence is expressed by \epsilon_{n+1} = \text{constant} \times \epsilon_n^m, \ m=1 .

See also[edit]


  1. ^ Burden & Faires 1985, p. 31
  2. ^
  3. ^ Burden & Fairies 1985, p. 28
  4. ^ Encyclopedia of Mathematics
  5. ^ Burden & Faires 1985, p. 28 for section
  6. ^ Burden & Faires 1985, p. 29. This version recomputes the function values at each iteration rather than carrying them to the next iterations.
  7. ^ Burden & Faires 1985, p. 31, Theorem 2.1

External links[edit]