Tag Archives: Sorting algorithms

Friday Algorithms: Input Data and Complexity

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Size of ..

As we all know most of the cases a program execution time depends most from the input data. As I wrote in my post the degree of the input data estimates the algorithm complexity.

Of course 3*n² is a bit faster than 5*n², but in general both functions are similar and have the same complexity. In this case we tend to say that the size of the input data is n.

Size of the input data
The size of the input data affects the complexity of the algorithm

It is easy to bind this to arrays or other simple data structures. For example when sorting an array with n elements, the size of the input data is n, but sometimes there is not such an obvious relation between an algorithm and the size of the input data.

For instance when we’ve to search a path into a graph, than maybe the best way to describe the input data is by summing both the number of vertices and the number of edges.

Conclusion

Think, think, think
Think, think, think

However this is important when dealing with algorithms, because a mistake in the estimation of the input data size will result in wrong algorithm estimation at all

Friday Algorithms: JavaScript Bubble Sort

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Bubble Sort

Unsorted Array

This is one of the most slowest algorithms for sorting, but it’s extremely well known because of its easy to implement nature. However as I wrote past Fridays there are lots of sorting algorithms which are really fast, like the quicksort or mergesort. In the case of bubble sort the nature of the algorithm is described in its name. The smaller element goes to the top (beginning) of the array as a bubble goes to the top of the water.

There is a cool animation showing how bubble sort works in compare to the quick sort and you can practically see how slow is bubble sort because of all the comparing.

QuickSort vs. BubbleSort

Pseudo Code

Actually what I’d like to show you is how you can move from pseudo code to code in practice. Here’s the pseudo code from Wikipedia.

procedure bubbleSort( A : list of sortable items ) defined as:
  do
    swapped := false
    for each i in 0 to length(A) - 2 inclusive do:
      if A[i] > A[i+1] then
        swap( A[i], A[i+1] )
        swapped := true
      end if
    end for
  while swapped
end procedure

JavaScript Source

var a = [34, 203, 3, 746, 200, 984, 198, 764, 9];
 
function bubbleSort(a)
{
    var swapped;
    do {
        swapped = false;
        for (var i=0; i < a.length-1; i++) {
            if (a[i] > a[i+1]) {
                var temp = a[i];
                a[i] = a[i+1];
                a[i+1] = temp;
                swapped = true;
            }
        }
    } while (swapped);
}
 
bubbleSort(a);
console.log(a);

As a result you’ve a sorted array!

Sorted Array

Friday Algorithms: JavaScript Merge Sort

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Merge Sort

This week I’m going to cover one very popular sorting algorithm – the merge sort. It’s very intuitive and simple as it’s described in Wikipedia:

  • If the list is of length 0 or 1, then it is already sorted. Otherwise:
  • Divide the unsorted list into two sublists of about half the size.
  • Sort each sublist recursively by re-applying merge sort.
  • Merge the two sublists back into one sorted list.

Here’s the Source

(JavaScript)

var a = [34, 203, 3, 746, 200, 984, 198, 764, 9];
 
function mergeSort(arr)
{
    if (arr.length < 2)
        return arr;
 
    var middle = parseInt(arr.length / 2);
    var left   = arr.slice(0, middle);
    var right  = arr.slice(middle, arr.length);
 
    return merge(mergeSort(left), mergeSort(right));
}
 
function merge(left, right)
{
    var result = [];
 
    while (left.length && right.length) {
        if (left[0] <= right[0]) {
            result.push(left.shift());
        } else {
            result.push(right.shift());
        }
    }
 
    while (left.length)
        result.push(left.shift());
 
    while (right.length)
        result.push(right.shift());
 
    return result;
}
 
console.log(mergeSort(a));

It’s interesting to see what happens in the Firebug’s console:

[34, 203, 3, 746] [200, 984, 198, 764, 9]
 
[34, 203] [3, 746]
 
[34] [203]
 
[3] [746]
 
[200, 984] [198, 764, 9]
 
[200] [984]
 
[198] [764, 9]
 
[764] [9]
 
[3, 9, 34, 198, 200, 203, 746, 764, 984]

Actually the tricky part in this algorithm is the merge function – it does all the work.

Friday Algorithms: Sorting a Set of Integers – Far Quicker than Quicksort!

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Yes! It’s really really fast, and it’s far quicker than the quicksort algorithm, which is considered as the fastest sorting algorithm in practice. However how it’s possible to be faster than the quicksort, which is the fastest algorithm?! Is that true? Actually it’s true, but only in few cases. It works with integers, you’ve to know the first and the last element from that set and you’ve to be sure that every element is unique

Imagine you’ve a set of numbers all of them greater than 1 and lesser than 1000. Of course you’re not suppose to have all of the integers between 1 and 1000, but only few of them – think of 500 numbers between 1 and 1000! Here’s important to note – that this is only an example, you can have far more than only few numbers between 1 and 1000 – what about the numbers between 1 and 1,000,000 – this is a big set, isn’t it.

The question is – if there are so many constraints, why should I use that algorithm instead of quicksort, or another sorting algorithm, that works with everything. The answer is clear – yes, you’d prefer quicksort if you’ve to sort some arbitrary data, but when it comes to integers, and you’ve, let’s say, 1,000,000 integers, my advice is – use this algorithm!

Sorting the Set

1. First Pass

First we have an unsorted array, but we know the minimum and maximum of the set.

an unsorted array of integers

On the first pass initialize an empty array with as many elements, as they are between the first and the last element of the set – for a set between 1 and 1000 – that will be an array with 1000 elements – each of which will be a zero in the beginning.

temporary array with 0 on every element

Than loop trough the set and for every element in the set – you should put a 1 on it’s place

sort a set - first pass

Now we have an array of 0 and 1.

2. Second Pass

After the first pass, you’d guess what you’ve to do – loop trough the second array and print the keys of the elements different from 0 – those that are 1.

a sorted set of integers

Now the array is sorted!

Source Code

var a = [34, 203, 3, 746, 200, 984, 198, 764];
 
function setSort(arr)
{
    var t = [], len = arr.length;
    for (var i = 0; i < 1000; i++) {
        t[i] = 0;
    }
 
    for (i = 0; i < len; i++) {
        t[arr[i]] = 1;
    }
 
    for (i = 0; i < 1000; i++) {
        if (1 == t[i]) {
            console.log(i);
        }
    }
}
 
setSort(a);

Now that you’ve seen that algorithm, perhaps you’d guess that it’s no so difficult to change from integers to any other set, and once again I should say that in many cases this is the best algorithm for sorting! Very often quicksort is preferred, but not always there isn’t something faster!

Array merge in JavaScript

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What makes array to merge in JavaScript?

Actually there aren’t so many array functions in JavaScript. As you know you can join, sort, etc. which methods are pretty basic. What I’d like to do is something similar to PHPs’ array_merge and than array_unique.

With JavaScript that seems pretty impossible. You can merge them with the .concat function as in the example bellow:

var a = [1,2];
var b = [3,4];
a.concat(b);

this gives you array with four elements. But when it comes to trim all the duplicates as it’s array_unique in PHP, well that’s really impossible. You’ve to do it by yourself, and I don’t thing this is the best technique. That slows down the code and you should avoid it!