13.
Binary Search
Written by Jonathan Sande
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Binary search is one of the most efficient searching algorithms with a time complexity of O(log n). You’ve already implemented a binary search once using a binary search tree. In this chapter, you’ll reimplement binary search on a sorted list.
Two conditions need to be met for the type of binary search that this chapter describes:
- The collection must be sorted.
- The underlying collection must be able to perform random index lookup in constant time.
As long as the elements are sorted, a Dart List meets both requirements.
Linear Search vs. Binary Search
The benefits of binary search are best illustrated by comparing it with linear search. Dart’s List type uses a linear search to implement its indexOf method. It traverses through the whole collection until it finds the first element:
Binary search handles things differently by taking advantage of the fact that the collection is already sorted. Here’s an example of applying binary search to find the value 31:
Instead of eight steps to find 31, it only takes three. Here’s how it works:
Step 1: Find the Middle Index
The first step is to find the middle index of the collection.
Step 2: Check the Element at the Middle Index
The next step is to check the element stored at the middle index. If it matches the value you’re looking for, return the index. Otherwise, continue to Step 3.
Step 3: Split and Repeat
The final step is to cut the list in half and start over. This time, you’ll only consider the elements exclusively to the left or to the right of the middle index, depending on the value you’re searching for. If the value you’re searching for is less than the middle value, you search the left subsequence. If it’s greater than the middle value, you search the right subsequence.
Implementation
Open the starter project for this chapter. Create a new lib folder in the root of your project. Then add a new file to it called binary_search.dart.
Adding an Extension on List
Add the following List extension to binary_search.dart:
extension SortedList<E extends Comparable<E>> on List<E> {
int? binarySearch(E value) {
// more to come
}
}
Writing the Algorithm
Next, fill in the logic for binarySearch by replacing // more to come in the code above with the following:
// 1
var start = 0;
var end = length;
// 2
while (start < end) {
// 3
final size = end - start;
final middle = start + size ~/ 2;
// 4
if (this[middle] == value) {
return middle;
// 5
} else if (this[middle].compareTo(value) < 0) {
start = middle + 1;
} else {
end = middle;
}
}
// 6
return null;
Testing it Out
That wraps up the implementation of binary search! Open bin/starter.dart to test it out. Replace the contents of the file with the following:
import 'package:starter/binary_search.dart';
void main() {
final list = <num>[1, 5, 15, 17, 19, 22, 24, 31, 105, 150];
final search31 = list.indexOf(31);
final binarySearch31 = list.binarySearch(31);
print('indexOf: $search31');
print('binarySearch: $binarySearch31');
}
indexOf: 7
binarySearch: 7
Challenges
Try out the challenges below to further strengthen your understanding of binary searches. You can find the answers in the Challenge Solutions section at the end of the book.
Challenge 1: Binary Search as a Free Function
In this chapter, you implemented binary search as an extension of List. Since binary search only works on sorted lists, exposing binarySearch for every list (including unsorted ones) opens it up to being misused.
Challenge 2: Recursive Search
Since a sorted list isn’t a tree-like data structure, you didn’t need to use recursion to perform the binary search. But that doesn’t mean you can’t. Just for fun, write binarySearch as a recursive function.
Challenge 3: Searching for a Range
Write a function that searches a sorted list and finds the range of indices for a particular element. You can start by creating a class named Range that holds the start and end indices.
final list = [1, 2, 3, 3, 3, 4, 5, 5];
final range = findRange(list, value: 3);
Key Points
- Binary search is only a valid algorithm on sorted collections.
- Binary search guesses the middle value in a list. If it’s wrong, it continues cutting the list in half and guessing the middle value until it finds the value or discovers it doesn’t exist.
- Sometimes it may be beneficial to sort a collection to leverage the binary search capability for looking up elements.
- The
indexOfmethod onListuses a linear search with O(n) time complexity. Binary search has O(log n) time complexity, which scales much better for large data sets if you do repeated lookups.