# 23.5. Optimal Sorting¶

What if we would like to find the sorting algorithm with the absolute fewest possible comparisons? It might well be that the result will not be practical for a general-purpose use. But consider this analogy to sports tournaments. In sports, a “comparison” between two teams or individuals means doing a competition between the two. This is fairly expensive (at least compared to some minor book keeping in a computer), and it might be worth trading a fair amount of book keeping to cut down on the number of games that need to be played. What if we want to figure out how to hold a tournament that will give us the exact ordering for all teams in the fewest number of total games? Of course, we are assuming that the results of each game will be “accurate” in that we assume not only that the outcome of A playing B would always be the same (at least over the time period of the tournament), but that transitivity in the results also holds. In practice these are unrealistic assumptions, but such assumptions are implicitly part of many tournament organizations. Like most tournament organizers, we can simply accept these assumptions and come up with an algorithm for playing the games that gives us some rank ordering based on the results we obtain.

Recall Insertion Sort, where we put
element \(i\) into a sorted sublist of the first \(i-1\)
elements.
What if we modify the standard Insertion Sort algorithm to use binary
search to locate where the \(i\) th element goes in the sorted
sublist?
This algorithm is called binary insert sort.
As a general-purpose sorting algorithm, this is not practical because
we then have to (on average) move about \(i/2\) elements to make
room for the newly inserted element in the sorted sublist.
But if we count *only* comparisons, binary insert sort is pretty
good.
And we can use some ideas from binary insert sort to get closer to an
algorithm that uses the absolute minimum number of comparisons needed
to sort.

Consider what happens when we run binary insert sort on five elements. How many comparisons do we need to do? We can insert the second element with one comparison, the third with two comparisons, and the fourth with 2 comparisons. When we insert the fifth element into the sorted list of four elements, we need to do three comparisons in the worst case. Notice exactly what happens when we attempt to do this insertion. We compare the fifth element against the second. If the fifth is bigger, we have to compare it against the third, and if it is bigger we have to compare it against the fourth. In general, when is binary search most efficient? When we have \(2^i - 1\) elements in the list. It is least efficient when we have \(2^i\) elements in the list. So, we can do a bit better if we arrange our insertions to avoid inserting an element into a list of size \(2^i\) if possible.

Figure 23.5.1 illustrates a different organization for the comparisons that we might do. First we compare the first and second element, and the third and fourth elements. The two winners are then compared, yielding a binomial tree. We can view this as a (sorted) chain of three elements, with element \(A\) hanging off from the root. If we then insert element \(B\) into the sorted chain of three elements, we will end up with one of the two posets shown on the right side of Figure 23.5.1, at a cost of 2 comparisons. We can then merge \(A\) into the chain, for a cost of two comparisons (because we already know that it is smaller then either one or two elements, we are actually merging it into a list of two or three elements). Thus, the total number of comparisons needed to sort the five elements is at most seven instead of eight.

If we have ten elements to sort, we can first make five pairs of elements (using five compares) and then sort the five winners using the algorithm just described (using seven more compares). Now all we need to do is to deal with the original losers. We can generalize this process for any number of elements as:

Pair up all the nodes with \(\lfloor \frac{n}{2} \rfloor\) comparisons.

Recursively sort the winners.

Fold in the losers.

We use binary insert to place the losers. However, we are free to choose the best ordering for inserting, keeping in mind the fact that binary search has the same cost for \(2^i\) through \(2^{i+1} -1\) items. For example, binary search requires three comparisons in the worst case for lists of size 4, 5, 6, or 7. So we pick the order of inserts to optimize the binary searches, which means picking an order that avoids growing a sublist size such that it crosses the boundary on list size to require an additional comparison. This sort is called merge insert sort, and also known as the Ford and Johnson sort.

For ten elements, given the poset shown in Figure 23.5.2 we fold in the last four elements (labeled 1 to 4) in the order Element 3, Element 4, Element 1, and finally Element 2. Element 3 will be inserted into a list of size three, costing two comparisons. Depending on where Element 3 then ends up in the list, Element 4 will now be inserted into a list of size 2 or 3, costing two comparisons in either case. Depending on where Elements 3 and 4 are in the list, Element 1 will now be inserted into a list of size 5, 6, or 7, all of which requires three comparisons to place in sort order. Finally, Element 2 will be inserted into a list of size 5, 6, or 7.

Merge insert sort is pretty good, but is it optimal? We know from the sorting lower bound proof that no sorting algorithm can be faster than \(\Omega(n \log n)\). To be precise, the information theoretic lower bound for sorting can be proved to be \(\lceil \log n!\rceil\). That is, we can prove a lower bound of exactly \(\lceil \log n!\rceil\) comparisons. Merge insert sort gives us a number of comparisons equal to this information theoretic lower bound for all values up to \(n = 12\). At \(n = 12\), merge insert sort requires 30 comparisons while the information theoretic lower bound is only 29 comparisons. However, for such a small number of elements, it is possible to do an exhaustive study of every possible arrangement of comparisons. It turns out that there is in fact no possible arrangement of comparisons that makes the lower bound less than 30 comparisons when \(n=12\). Thus, the information theoretic lower bound is an underestimate in this case, because 30 really is the best that can be done.

Call the optimal worst cost for \(n\) elements \(S(n)\). We know that \(S(n+1) \leq S(n) + \lceil \log (n+1)\rceil\) because we could sort \(n\) elements and use binary insert for the last one. For all \(n\) and \(m\), \(S(n+m) \leq S(n) + S(m) + M(m, n)\) where \(M(m, n)\) is the best time to merge two sorted lists. For \(n = 47\), it turns out that we can do better by splitting the list into pieces of size 5 and 42, and then merging. Thus, merge sort is not quite optimal. But it is extremely good, and nearly optimal for smallish numbers of elements.