In this section, we present three sorting algorithms: mergesort, quicksort, and heapsort. Each of these algorithms takes an input array and sorts the elements of into nondecreasing order in (expected) time. These algorithms are all comparisonbased. These algorithms don't care what type of data is being sorted; the only operation they do on the data is comparisons using the method. Recall, from Section 1.2.4, that returns a negative value if , a positive value if , and zero if .
The mergesort algorithm is a classic example of recursive divide
and conquer:
If the length of
is at most 1, then
is already
sorted, so we do nothing. Otherwise, we split
into two halves,
and
.
We recursively sort
and
, and then we merge (the now sorted)
and
to get our fully sorted array
:
An example is shown in Figure 11.1.
Compared to sorting, merging the two sorted arrays
and
is
fairly easy. We add elements to
one at a time. If
or
is empty, then we add the next elements from the other (nonempty)
array. Otherwise, we take the minimum of the next element in
and
the next element in
and add it to
:
Notice that the
algorithm performs at most
comparisons before running out of elements in one of
or
.
To understand the runningtime of mergesort, it is easiest to think of it in terms of its recursion tree. Suppose for now that is a power of two, so that , and is an integer. Refer to Figure 11.2. Mergesort turns the problem of sorting elements into two problems, each of sorting elements. These two subproblem are then turned into two problems each, for a total of four subproblems, each of size . These four subproblems become eight subproblems, each of size , and so on. At the bottom of this process, subproblems, each of size two, are converted into problems, each of size one. For each subproblem of size , the time spent merging and copying data is . Since there are subproblems of size , the total time spent working on problems of size , not counting recursive calls, is
The proof of the following theorem is based on preceding analysis, but has to be a little more careful to deal with the cases where is not a power of 2.
Merging two sorted lists of total length requires at most comparisons. Let denote the maximum number of comparisons performed by on an array of length . If is even, then we apply the inductive hypothesis to the two subproblems and obtain
The quicksort algorithm is another classic divide and conquer algorithm. Unlike mergesort, which does merging after solving the two subproblems, quicksort does all of its work upfront.
Quicksort is simple to describe: Pick a random pivot element,
, from
; partition
into the set of elements less than
, the
set of elements equal to
, and the set of elements greater than
;
and, finally, recursively sort the first and third sets in this partition.
An example is shown in Figure 11.3.
At the heart of the quicksort algorithm is the inplace partitioning algorithm. This algorithm, without using any extra space, swaps elements in and computes indices and so that
Quicksort is very closely related to the random binary search trees studied in Section 7.1. In fact, if the input to quicksort consists of distinct elements, then the quicksort recursion tree is a random binary search tree. To see this, recall that when constructing a random binary search tree the first thing we do is pick a random element and make it the root of the tree. After this, every element will eventually be compared to , with smaller elements going into the left subtree and larger elements into the right.
In quicksort, we select a random element and immediately compare everything to , putting the smaller elements at the beginning of the array and larger elements at the end of the array. Quicksort then recursively sorts the beginning of the array and the end of the array, while the random binary search tree recursively inserts smaller elements in the left subtree of the root and larger elements in the right subtree of the root.
The above correspondence between random binary search trees and quicksort means that we can translate Lemma 7.1 to a statement about quicksort:
A little summing up of harmonic numbers gives us the following theorem about the running time of quicksort:
Theorem 11.3 describes the case where the elements being sorted are all distinct. When the input array, , contains duplicate elements, the expected running time of quicksort is no worse, and can be even better; any time a duplicate element is chosen as a pivot, all occurrences of get grouped together and do not take part in either of the two subproblems.
The heapsort algorithm is another inplace sorting algorithm. Heapsort uses the binary heaps discussed in Section 10.1. Recall that the BinaryHeap data structure represents a heap using a single array. The heapsort algorithm converts the input array into a heap and then repeatedly extracts the minimum value.
More specifically, a heap stores elements in an array, , at array locations with the smallest value stored at the root, . After transforming into a BinaryHeap, the heapsort algorithm repeatedly swaps and , decrements , and calls so that once again are a valid heap representation. When this process ends (because ) the elements of are stored in decreasing order, so is reversed to obtain the final sorted order.^{11.1}Figure 11.4 shows an example of the execution of .

A key subroutine in heap sort is the constructor for turning an unsorted array into a heap. It would be easy to do this in time by repeatedly calling the BinaryHeap method, but we can do better by using a bottomup algorithm. Recall that, in a binary heap, the children of are stored at positions and . This implies that the elements have no children. In other words, each of is a subheap of size 1. Now, working backwards, we can call for each . This works, because by the time we call , each of the two children of are the root of a subheap, so calling makes into the root of its own subheap.
The interesting thing about this bottomup strategy is that it is more efficient than calling times. To see this, notice that, for elements, we do no work at all, for elements, we call on a subheap rooted at and whose height is one, for elements, we call on a subheap whose height is two, and so on. Since the work done by is proportional to the height of the subheap rooted at , this means that the total work done is at most
The following theorem describes the performance of .
We have now seen three comparisonbased sorting algorithms that each run in time. By now, we should be wondering if faster algorithms exist. The short answer to this question is no. If the only operations allowed on the elements of are comparisons, then no algorithm can avoid doing roughly comparisons. This is not difficult to prove, but requires a little imagination. Ultimately, it follows from the fact that
We will start by focusing our attention on deterministic algorithms like mergesort and heapsort and on a particular fixed value of . Imagine such an algorithm is being used to sort distinct elements. The key to proving the lowerbound is to observe that, for a deterministic algorithm with a fixed value of , the first pair of elements that are compared is always the same. For example, in , when is even, the first call to is with and the first comparison is between elements and .
Since all input elements are distinct, this first comparison has only two possible outcomes. The second comparison done by the algorithm may depend on the outcome of the first comparison. The third comparison may depend on the results of the first two, and so on. In this way, any deterministic comparisonbased sorting algorithm can be viewed as a rooted binary comparison tree. Each internal node, , of this tree is labelled with a pair of indices and . If the algorithm proceeds to the left subtree, otherwise it proceeds to the right subtree. Each leaf of this tree is labelled with a permutation of . This permutation represents the one that is required to sort if the comparison tree reaches this leaf. That is,
The comparison tree for a sorting algorithm tells us everything about the algorithm. It tells us exactly the sequence of comparisons that will be performed for any input array, , having distinct elements and it tells us how the algorithm will reorder in order to sort it. Consequently, the comparison tree must have at least leaves; if not, then there are two distinct permutations that lead to the same leaf; therefore, the algorithm does not correctly sort at least one of these permutations.
For example, the comparison tree in Figure 11.6 has only leaves. Inspecting this tree, we see that the two input arrays and both lead to the rightmost leaf. On the input this leaf correctly outputs . However, on the input , this node incorrectly outputs . This discussion leads to the primary lowerbound for comparisonbased algorithms.
Theorem 11.5 deals with deterministic algorithms like mergesort and heapsort, but doesn't tell us anything about randomized algorithms like quicksort. Could a randomized algorithm beat the lower bound on the number of comparisons? The answer, again, is no. Again, the way to prove it is to think differently about what a randomized algorithm is.
In the following discussion, we will assume that our decision trees have been ``cleaned up'' in the following way: Any node that can not be reached by some input array is removed. This cleaning up implies that the tree has exactly leaves. It has at least leaves because, otherwise, it could not sort correctly. It has at most leaves since each of the possible permutation of distinct elements follows exactly one root to leaf path in the decision tree.
We can think of a randomized sorting algorithm, , as a deterministic algorithm that takes two inputs: The input array that should be sorted and a long sequence of random real numbers in the range . The random numbers provide the randomization for the algorithm. When the algorithm wants to toss a coin or make a random choice, it does so by using some element from . For example, to compute the index of the first pivot in quicksort, the algorithm could use the formula .
Now, notice that if we fix to some particular sequence then becomes a deterministic sorting algorithm, , that has an associated comparison tree, . Next, notice that if we select to be a random permutation of , then this is equivalent to selecting a random leaf, , from the leaves of .
Exercise 11.12 asks you to prove that, if we select a random leaf from any binary tree with leaves, then the expected depth of that leaf is at least . Therefore, the expected number of comparisons performed by the (deterministic) algorithm when given an input array containing a random permutation of is at least . Finally, notice that this is true for every choice of , therefore it holds even for . This completes the proof of the lowerbound for randomized algorithms.