We know that finding the minimum in a list of integers is a fairly simple task, but what about finding the i-th smallest element? Then the task isn’t that trivial and we have to think for a different approach.
First of all there are some very basic and intuitive approaches. Since finding the minimum is so easy, can we just find the minimum, than exclude it from the list and then search the minimum again until we find the i-th smallest element.
Algorithms always depend on the input. We saw that general purpose sorting algorithms as insertion sort, bubble sort and quicksort can be very efficient in some cases and inefficient in other. Indeed insertion and bubble sort are considered slow, with best-case complexity of O(n2), but they are quite effective when the input is fairly sorted. Thus when you have a sorted array and you add some “new” values to the array you can sort it quite effectively with insertion sort. On the other hand quicksort is considered one of the best general purpose sorting algorithms, but while it’s a great algorithm when the data is randomized it’s practically as slow as bubble sort when the input is almost or fully sorted.
Now we see that depending on the input algorithms may be effective or not. For almost sorted input insertion sort may be preferred instead of quicksort, which in general is a faster algorithm.
Just because the input is so important for an algorithm efficiency we may ask are there any sorting algorithms that are faster than O(n.log(n)), which is the average-case complexity for merge sort and quicksort. And the answer is yes there are faster, linear complexity algorithms, that can sort data faster than quicksort, merge sort and heapsort. But there are some constraints!
Everything sounds great but the thing is that we can’t sort any particular data with linear complexity, so the question is what rules the input must follow in order to be sorted in linear time.
Such an algorithm that is capable of sorting data in linear O(n) time is radix sort and the domain of the input is restricted – it must consist only of integers.
Let’s say we have an array of integers which is not sorted. Just because it consists only of integers and because array keys are integers in programming languages we can implement radix sort.
First for each value of the input array we put the value of “1” on the key-th place of the temporary array as explained on the following diagram.
When it comes to sorting items by comparing them merge sort is one very natural approach. It is natural, because simply divides the list into two equal sub-lists then sort these two partitions applying the same rule. That is a typical divide and conquer algorithm and it just follows the intuitive approach of speeding up the sorting process by reducing the number of comparisons. However there are other “divide and conquer” sorting algorithms that do not follow the merge sort scheme, while they have practically the same success. Such an algorithm is quicksort.
Back in 1960 C. A. R. Hoare comes with a brilliant sorting algorithm. In general quicksort consists of some very simple steps. First we’ve to choose an element from the list (called a pivot) then we must put all the elements with value less than the pivot on the left side of the pivot and all the items with value greater than the pivot on its right side. After that we must repeat these steps for the left and the right sub-lists. That is quicksort! Simple and elegant!
Basically sorting algorithms can be divided into two main groups. Such based on comparisons and such that are not. I already posted about some of the algorithms of the first group. Insertion sort, bubble sort and Shell sort are based on the comparison model. The problem with these three algorithms is that their complexity is O(n2) so they are very slow.
So is it possible to sort a list of items by comparing their items faster than O(n2)? The answer is yes and here’s how we can do it.
The nature of those three algorithms mentioned above is that we almost compared each two items from initial list.
This, of course, is not the best approach and we don’t need to do that. Instead we can try to divide the list into smaller lists and then sort them. After sorting the smaller lists, which is supposed to be easier than sorting the entire initial list, we can try to merge the result into one sorted list. This technique is typically known as “divide and conquer”.
Normally if a problem is too difficult to solve, we can try to break it apart into smaller sub-sets of this problem and try to solve them. Then somehow we can merge the results of the solved problems.