Sorting Algorithms

Master the fundamental sorting algorithms through interactive visualizations. Watch how different algorithms organize data step by step.

Interactive Visualizations

Code Examples

Complexity Analysis

Bubble Sort

Simple comparison-based algorithm that repeatedly steps through the list

Time Complexity:O(n²)

Space Complexity:O(1)

Difficulty:Beginner

Stable:Yes

Selection Sort

Finds the minimum element and places it at the beginning

Time Complexity:O(n²)

Space Complexity:O(1)

Difficulty:Beginner

Stable:No

Insertion Sort

Builds the sorted array one element at a time

Time Complexity:O(n²)

Space Complexity:O(1)

Difficulty:Beginner

Stable:Yes

Merge Sort

Divide and conquer algorithm that divides the array into halves

Time Complexity:O(n log n)

Space Complexity:O(n)

Difficulty:Intermediate

Stable:Yes

Quick Sort

Efficient divide and conquer algorithm using pivot element

Time Complexity:O(n log n)

Space Complexity:O(log n)

Difficulty:Intermediate

Stable:No

Heap Sort

Uses binary heap data structure to sort elements

Time Complexity:O(n log n)

Space Complexity:O(1)

Difficulty:Advanced

Stable:No

Radix Sort

Non-comparison based sorting by processing individual digits

Time Complexity:O(d × (n + k))

Space Complexity:O(n + k)

Difficulty:Advanced

Stable:Yes

Bucket Sort

Distributes elements into buckets and sorts each bucket individually

Time Complexity:O(n + k)

Space Complexity:O(n + k)

Difficulty:Advanced

Stable:Yes

Why Learn Sorting Algorithms?

Sorting algorithms are fundamental to computer science and form the foundation for understanding more complex algorithms and data structures.

Algorithm Design

Learn different algorithmic paradigms like divide-and-conquer and greedy approaches

Time Complexity

Understand Big O notation and how to analyze algorithm performance

Practical Applications

Apply sorting in real-world scenarios like databases, search engines, and data processing