*Knowledge of a binary search is assumed for this writeup.*
### Definition

An interpolation search (also known as an extrapolation search or weighted binary search) is functionally identical to a binary search except in one key feature: which element is checked. While a binary search always checks the middle element of a vector, an interpolation search estimates where the sought value will lie. All other aspects of a binary search apply: the search requires a sorted list, elements that cannot possibly be the sought value are eliminated, and so on. To find the proper element to check, a very basic knowledge of Algebra is necessary.

### Derivation

The first step is to set up the equation of a line in

slope-intercept form,

y = mx + b

This line will only be a

segment drawn from the point (0, V[0]) to (size, V[size]) where size is the number of elements in vector V minus one. Using the definition of slope,

y = (V[size] - V[0]) / (size - 0) * x + b

b is the y-intercept. Since the segment includes (and ends on) point (0, V[0]) we know that is the y-intercept.

y = (V[size] - V[0]) / (size - 0) * x + V[0]

Since the equation needs to be solved for x =, one final step is needed,

x = ((y - V[0]) * (size - 0)) / (V[size] - V[0])

When searching to see if a particular value is in the vector, just plug the appropriate values into the equation (with y equal to the sought value). Solving for x should yield which element in the vector is closest to y, if all of the vector's elements lie on a line. Unfortunately, this is usually untrue, so the algorithm is not O(1). However, this still implies that if you've got data that is (nearly) linear, then perhaps an interpolation search is what you seek. Conversely, if you cannot foresee what kind of data you will have, then use a binary search.

01 int InterpolationSearch(vector<int> V, int sought)
{
02 int low, high, last, element;
03 last = V.size() - 1;
04 low = V[0];
05 high = V[last];
06 element = V[0];
07 while (low <= high)
{
08 if (sought < low || sought > high)
09 break;
10 if (low == high)
11 break;
12 element = ((sought - low) * last) / (high - low);
13 if (V[element] == sought)
14 break;
15 else if (V[element] > sought)
16 low = V[element];
17 else if (V[element] < sought)
18 high = V[element];
}
19 if (V[element] == sought)
20 return element;
21 else
22 return -1;
}

### Explanation

In line 8, one knows that, because "V" is sorted, if "sought" is less than the least element or greater than the greatest element that it exists nowhere in the array. Using that knowledge, one can then say, as in line 10, that if "low" is equal to "high", that "sought" not only exists in "V", but every element in the "V" is equal to "sought". Such a special case needs to be made because if line 12 is reached where "low == high" is true, then the line will result in a division by zero error. Line 12 is simply the equation derived above with the appropriate values used. Also note that the division in line 12 is integer division, meaning decimal values are truncated downward (chop off everything after the decimal point, inclusive).

### Example

When searching in a dictionary or a phone book, one often uses an interpolation search. Say you were searching in a paper dictionary for the word epulosity. You might open up the dictionary a third of the way and come across the word frisson. You immediately know every entry after frisson is invalid. So you flip back a few pages to exasperate. Again, every entry after exasperate is invalid. Flipping back a few more pages you come upon epitome so your search is just about done. Finding epulosity is trivial from there.

Of course, because you don't know how many words there are in the dictionary and how many words are on each page, it's not exactly an interpolation search. However, it's close enough for the purposes of an example. Finally, an interpolation search is more natural, and is often quicker, than a binary search in such cases.

#### Sources

*http://www.nist.gov/dads/ - The NIST dictionary algorithms and data structures*