Given a sorted array \(a\) of \(n\) integers and a key, we want to return the index of the key in the array or -1 if the key doesn’t exist in the array. Binary search takes advantage of the property that the array is sorted and then iteratively finds which half of the list the key will be located in. After \(O(\log(n))\) iterations, we will either correctly return that index or return -1 if the key doesn’t exist.

int binary_search(int *a, int key, int n) {
    int m, first = 0, last = n - 1;
    while (first <= last) {
        m = (first + (last - first / 2);
        if (key > a[m]) {
            first = m + 1;
        } else if (key < a[m]) {
            last = m - 1;
        } else if (key == a[m]) {
            return m;
        }
    }
    return -1; // not found
}


Correctness Proof

This is just my attempt to prove that binary search algorithm implemented above is correct. To prove binary search is correct, we want to prove the following:

  • At every iteration binary search is still searching the correct range in the array. This can be achieved with a loop invariant.
  • Binary search terminates.

If we are able to prove these two then we can conclude that binary search is correct.

Preconditions and Postconditions

In addition to loop invariants, we also need to describe the preconditions of binary search. Preconditions are just assertions that must be true at the beginning of our function. In our case, our precondition is that the array is sorted in an ascending order. If the array is not sorted, then we don’t guarantee anything about the outcome.

In addition to preconditions, we also need to describe our postconditions. Given that we met the preconditions, our loop invariant is correct and our algorithm terminates, our post condition is that we return -1 if the key is not in the array or return the index of the key in the array.

Binary Search's Loop Invariant

As a reminder from CLRS, loop invariants have three parts that we need to prove:

  • The invariant is true before the first iteration of the loop. (Initialization)
  • If the invariant is true before iteration \(i\), then it is true before iteration \(i+1\). (Maintenance)
  • When the loop terminates, the invariant gives us a property to help show that the algorithm is correct. (Termination)

Let’s define the following loop invariant:
At the start of each iteration of the while loop above the following is true:

  1. \(1 \leq first \leq last \leq n\)
  2. If the key is in the array and so there is some \(index\) such that \(a[index] = key\) and \(1 \leq index \leq n\) then \(first \leq index \leq last\). This just means that we are correctly picking the right range to search at every iteration.

Initialization:
Before the first iteration we know that \(first = 1\) and \(last = n-1\) and so (1) is trivially true. Also, if the key is in \(a\) then it must be that \(1 \leq index \leq n-1\) where \(a[index] = key\). Therefore, we are searching the correct range.

Maintenance:
Suppose the invariant holds before iteration \(i\) and that if our key is in \(a\) then for some index where \(a[index] = key\) we have \(first \leq index \leq last\). Let’s look at iteration \(i+1\). We first calculate the mid point in the range \([first, last]\). Based on this, we have three cases:

(1) \(a[m] == key\). We therefore, return \(m\) and we are done.

(2) \(a[m] > key\). Our algorithm in this case searches the new range \([first, m-1]\). We know that \(first \leq index\) by the inductive hypothesis and so we only need to prove that \(index \leq m-1\). Since \(a[m] > key\) and since the array is sorted then it must be that \(index < m\) is true otherwise \(a[index]\) comes after \(a[m]\) which indicates that \(a\) is not sorted.

(3) \(a[m] > key\). Our algorithm will then search the new range \([m+1, last]\). We can use a similar argument to (2) to prove that \(index \in [m+1, last]\).

From (1), (2), (3) we conclude that the invariant holds before iteration \(i+1\).

Termination:
Suppose we know we terminate (proof next), then by the loop invariant we know

Does Binary Search Terminate?

Will we terminate? we need to prove that if the search range is \([first_i, last_i]\) in iteration \(i\) and the search range in iteration \(i+1\) is \([first_{i+1}, last_{i+1}]\) then it must be that \(last_{i+1} - first_{i+1} < last_{i} - first_{i}\). To do so we can use a similar approach to the one we used to prove our maintenance step. We know there are three cases, in each case we can prove that the new range is shrinking.

Suppose \(m\) is the midpoint in \([first_i, last_i]\). There are three cases:

  • \(a[m] == key\). We therefore, return \(m\) and we are done (binary search terminates).
  • \(a[m] > key\). Our algorithm in this case searches the new range \([first_i, m-1]\). We see here that \(m-1-first_i < last_i-first_i\).
  • \(a[m] > key\). Our algorithm will then search the new range \([m+1, last]\). Similarly, we see here that \(last - m + 1 < last_i - first_i\).

From the three cases we conclude that binary search must terminate.

References

These are my study notes from CLRS and ?