What You’ll Find
This documentation provides implementations and explanations for algorithms spanning four major categories:- Sorting Algorithms: 11 different approaches to ordering data
- Searching Algorithms: Efficient methods for finding elements
- Graph Algorithms: Shortest path and graph traversal techniques
- Design Techniques: Fundamental algorithmic paradigms and problem-solving patterns
Algorithm Categories
Sorting Algorithms
Master 11 sorting algorithms from basic bubble sort to advanced radix sort, with complexity analysis and optimization techniques
Searching Algorithms
Learn efficient search strategies including linear and binary search with iterative and recursive implementations
Graph Algorithms
Explore shortest path algorithms like Dijkstra’s, essential for network routing, navigation, and optimization problems
Design Techniques
Understand fundamental algorithmic paradigms: divide & conquer, dynamic programming, greedy algorithms, and backtracking
Sorting Algorithms
Sorting is one of the most fundamental operations in computer science. Our collection includes 11 different sorting algorithms, each with unique characteristics and use cases: Comparison-Based Sorts- Bubble Sort: Simple but inefficient O(n²) algorithm, useful for teaching
- Selection Sort: In-place O(n²) algorithm with minimal swaps
- Insertion Sort: Efficient for small or nearly sorted datasets
- Merge Sort: Stable O(n log n) divide-and-conquer algorithm
- Quick Sort: Fast O(n log n) average-case in-place sorting
- Heap Sort: O(n log n) in-place sorting using binary heap
- Counting Sort: O(n+k) algorithm for integers in a known range
- Radix Sort: O(d×n) for sorting integers by digits
- Bucket Sort: O(n+k) for uniformly distributed data
- Cycle Sort: Minimizes writes to memory
- Topological Sort: Orders vertices in directed acyclic graphs
Explore Sorting Algorithms
View detailed implementations, complexity analysis, and use cases
Searching Algorithms
Efficient searching is critical for data retrieval and forms the basis of many complex algorithms:- Linear Search: O(n) sequential search through unsorted data
- Binary Search: O(log n) search in sorted arrays with both iterative and recursive implementations
Explore Searching Algorithms
Learn search strategies with practical examples
Graph Algorithms
Graph algorithms solve problems involving networks, relationships, and pathfinding:- Dijkstra’s Algorithm: Finds shortest paths from a source vertex to all other vertices in weighted graphs
- Topological Sort: Orders vertices in directed acyclic graphs (DAGs) for dependency resolution
- Network routing and navigation systems
- Dependency resolution in build systems
- Social network analysis
- Resource scheduling
Explore Graph Algorithms
Dive into shortest path and graph traversal techniques
Design Techniques
Understanding algorithmic design paradigms helps you solve complex problems by applying proven strategies:Divide and Conquer
Break problems into smaller subproblems, solve them independently, and combine results. Used in merge sort, quick sort, and binary search.Dynamic Programming
Solve complex problems by breaking them down into simpler overlapping subproblems and storing results to avoid redundant computation. Essential for optimization problems.Greedy Algorithms
Make locally optimal choices at each step to find global solutions. Used in Dijkstra’s algorithm, Huffman coding, and activity selection.Backtracking
Explore all possible solutions by building candidates incrementally and abandoning them when they fail to satisfy constraints. Used in constraint satisfaction problems, puzzles, and combinatorial optimization.Explore Design Techniques
Master fundamental problem-solving paradigms
Complexity Analysis
Understanding time and space complexity is crucial for selecting the right algorithm:- Time Complexity: How runtime grows with input size (O(1), O(log n), O(n), O(n log n), O(n²))
- Space Complexity: Memory usage during execution
- Best/Average/Worst Case: Performance under different conditions
- Stability: Whether equal elements maintain relative order
- In-place: Whether additional space is required
Getting Started
Choose a Category
Start with sorting if you’re new to algorithms, or jump to a specific category based on your needs
Next Steps
Sorting Algorithms
Start with fundamental sorting techniques
Searching Algorithms
Learn efficient search strategies
Graph Algorithms
Explore pathfinding and traversal
Design Techniques
Master algorithmic paradigms