Sorting algorithms are methods used to arrange elements of a list or array in a specific order, typically numerical or lexicographical. They are fundamental in computer science for organizing data efficiently. It is an exercise in understanding how to break down a problem into steps and then implement those steps, i.e., how to create an algorithm. It's also an exercise in realizing that there are multiple methods to tackle an issue, and some are superior to others.
Why should I learn it?
It's a simple practical example for thinking recursively (see: merge sort and quick sort) and divide and conquer.
It's a simple but nontrivial example for algorithmic analysis (I.e. big O).
It's a traditional intro computer science topic that is expected to be taught.
It's a simple example to motivate why you might care about having a better algorithm than the simplest native one (I.e. bubble sort).
Here are some common sorting algorithms
Bubble Sort
Description: Repeatedly swaps adjacent elements if they are in the wrong order. Time Complexity: O(n²) Use Case: Simple but inefficient for large datasets. Bubble Sort GitHub Gist
Description: Selects the smallest element from the unsorted part and swaps it with the first unsorted element. Time Complexity: O(n²) Use Case: Inefficient for large datasets but easy to implement. Selection Sort GitHub Gist
<p>var arr = [10, 55, 20, 4, 28, 69, 22, 85, 7, 37];</p>
<p>function selectionSort(arr)<br>
{<br>
var min;<br>
for(var i = 0; i < arr.length; i++)<br>
{<br>
min = i;<br>
for(var j = i+1; j < arr.length; j++ )<br>
{<br>
if (arr[j] < arr[min])<br>
{<br>
min = j;<br>
}<br>
}</p>
<div class="highlight js-code-highlight">
<pre class="highlight plaintext"> if (min !== i) {
var s = arr[min];
arr[min] = arr[i];
arr[i] = s;
}
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}
return arr;
}
console.log(selectionSort(arr));
Insertion Sort
Description: Builds the sorted list one element at a time by inserting each element into its correct position. Time Complexity: O(n²) Use Case: Good for small datasets or nearly sorted arrays. Insertion Sort GitHub Gist
Description: Divides the array into halves, recursively sorts them, and then merges the sorted halves. Time Complexity: O(n log n) Use Case: Efficient for large datasets, uses additional space for merging. Merge Sort GitHub Gist Merge Sort 2 GitHub Gist
<p>var unsortedArr = [10, 55, 20, 4, 28, 69, 22, 85, 7, 37];</p>
<p>function merge(left, right)<br>
{<br>
const result = new Array();</p>
<div class="highlight js-code-highlight">
<pre class="highlight plaintext">let i = j = 0;
while (i < left.length && j < right.length) {
if (left[i] < right[j]){
result.push(left[i]);
i++;
}else {
result.push(right[j]);
j++;
}
}
while (i < left.length) {
result.push(left[i]);
i++;
}
while (j < right.length) {
result.push(right[j]);
j++;
}
return result;
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}
function mergeSort(arr)
{
if (arr.length <= 1)
return arr;
const mid = Math.floor(arr.length/2);
const LA = new Array();
const RA = new Array();
for(let i = 0; i< mid; i++)
LA.push(arr[i]);
for(let j = mid; j< arr.length; j++)
RA.push(arr[j]);
const leftSorted = mergeSort(LA);
const rightSorted = mergeSort(RA);
return merge(leftSorted, rightSorted);
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}
console.log(mergeSort(unsortedArr));
Quick Sort
Description: Selects a pivot element and partitions the array into two sub-arrays: elements less than the pivot and elements greater than the pivot, then recursively sorts the sub-arrays. Time Complexity: O(n log n) on average, O(n²) in the worst case. Use Case: Fast and widely used for large datasets. Quick Sort GitHub Gist
function swap(arr, i, j)
{
let temp = arr[i];
arr[i] = arr[j];
arr[j] = temp;
}
function QuickSort(arr, low, high)
{
if (low < high){
const po = partition(arr, low, high);
QuickSort(arr, low, po - 1);
QuickSort(arr, po + 1, high);
}
return arr;
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}
console.log(QuickSort(arr, 0, arr.length - 1));
Heap Sort
Description: Converts the array into a heap data structure and repeatedly extracts the maximum element to build the sorted array. Time Complexity: O(n log n) Use Case: Efficient and doesn't require extra space like merge sort.
Radix Sort
Description: Non-comparative sorting algorithm that sorts elements digit by digit, starting from the least significant digit to the most significant. Time Complexity: O(nk) where k is the number of digits. Use Case: Suitable for sorting numbers or strings with fixed-length keys.
Bucket Sort
Description: Divides elements into several buckets and then sorts each bucket individually (usually using another sorting algorithm). Time Complexity: O(n + k) where k is the number of buckets. Use Case: Effective when input is uniformly distributed over a range.
Each algorithm has its strengths and weaknesses, and the choice of which one to use depends on the size of the dataset, memory constraints, and whether the data is partially sorted.
