Hybrid Sorting Algorithms
Hybrid Sorting Algorithms
The best versions of quick sort are competitive with both heap sort and merge sort on the vast majority of inputs. However, quick sort has a very bad worst case — $ O(n^2) $ running time and $ O(n) $ stack usage. By comparison, both heap sort and merge sort have $ O(n \lg n) $ worst-case running time, together with a stack usage of $ O(1) $ for heap sort or $ O(\lg n) $ for merge sort. Furthermore, insertion sort performs better than any of these algorithms on small data sets. In this section, we look at ways to combine some of these algorithms to obtain a sorting algorithm that has the advantages of each of them.
We will start with quick sort, which gives the best performance for most inputs. One way of improving its performance is to make use of the fact that insertion sort is more efficient for small data sets. Improving the performance on small portions can lead to significant performance improvements for large arrays because quick sort breaks large arrays into many small portions. Hence, when the portion we are sorting becomes small enough, rather than finding a pivot and splitting, we instead call insertion sort.
An alternative to the above improvement is to use the fact that insertion sort runs in $ O(n) $ time when the number of inversions is linear in the number of array elements. To accomplish this, we modify quick sort slightly so that instead of sorting the array, it brings each element near where it belongs. We will refer to this modified algorithm as a partial sort. After we have done the partial sort, we then sort the array using insertion sort. The modification we make to quick sort to obtain the partial sort is simply to change when we stop sorting. We only sort portions that are larger than some threshold — we leave other portions unsorted.
Suppose, for example, that we choose a threshold of $ 10 $. Once the partial sort reaches an array portion with nine or fewer elements, we do nothing with it. Note, however, that these elements are all larger than the elements that precede this portion, and they are all smaller than the elements that follow this portion; hence, each element can form an inversion with at most eight other elements — the other elements in the same portion. Because each inversion contains two elements, this means that there can be no more than $ 4n $ inversions in the entire array once the partial sort finishes. The subsequent call to insertion sort will therefore finish the sorting in linear time.
Both of the above techniques yield performance improvements over quick sort alone. In fact, for many years, such combinations of an optimized version of quick sort with insertion sort were so efficient for most inputs that they were the most commonly-used algorithms for general-purpose sorting. On modern hardware architectures, the first approach above tends to give the better performance.
Nevertheless, neither of the above approaches can guarantee $ O(n \lg n) $ performance — in the worst case, they are all still in $ O(n^2) $. Furthermore, the bad cases still use linear stack space. To overcome these shortfalls, we can put a limit on the depth of recursion. Once this limit is reached, we can finish sorting this portion with an $ O(n \lg n) $ algorithm such as heap sort. The idea is to pick a limit that is large enough that it is rarely reached, but still small enough that bad cases will cause the alternative sort to be invoked before too much time is spent. A limit of about $ 2 \lg n $, where $ n $ is the size of the entire array, has been suggested. Because arrays in C# must have fewer than $ 2^{31} $ elements, this value is always less than $ 62 $; hence, it is also safe to use a constant for the limit. The resulting algorithm has a worst-case running time in $ O(n \lg n) $ and a worst-case stack usage of $ O(\lg n) $. This logarithmic bound on the stack usage is sufficient to avoid a StackOverflowException.
The combination of quick sort using median-of-three partitioning with insertion sort for small portions and heap sort when the recursion depth limit is reached is known as introsort (short for introspective sort). Other improvements exist, but we will not discuss them here. The best versions of introsort are among the best sorting algorithms available, unless the array is nearly sorted. Of course, if the data won’t fit in an array, we can’t use introsort — we should use external merge sort instead. Furthermore, like quick sort and heap sort, introsort is not stable. When a stable sort is not needed, however, and when none of the above special cases applies, introsort is one of the best choices available.