Algorithms Overview

This section contains comprehensive documentation for all algorithms implemented in the algo-py library.

Available Algorithms

Sorting

Simple Sorting

• Bubble Sort: Simple comparison-based algorithm with O(n²) complexity
• Selection Sort: Finds minimum element and places it at the beginning
• Insertion Sort: Builds sorted array one element at a time

Efficient Comparison-Based Sorts

• Merge Sort: Divide-and-conquer algorithm with guaranteed O(n log n) performance
• Quick Sort: Fast average-case O(n log n) with in-place partitioning
• Heap Sort: Uses binary heap data structure for O(n log n) performance

Non-Comparison Based Sorts

• Counting Sort: Linear time sorting for integers in a known range
• Radix Sort: Sorts integers by processing individual digits

Searching

• Linear Search: Sequential search through unsorted data
• Binary Search: Efficient search in sorted arrays

Hashing

• Hash Functions: Various hash function implementations for different use cases

Graph Algorithms

• Dijkstra's Algorithm: Single-source shortest paths for non-negative weights
• Floyd-Warshall Algorithm: All-pairs shortest paths with cycle detection

String Algorithms

• Autocomplete: Text completion using various data structures and strategies
• Edit Distance: Computing minimum edit distance between strings
• Longest Repeated Substring (LRS): Finding the longest substring that appears multiple times in a text
• Longest Common Substring (LCS): Finding the longest substring shared between two strings
• Palindrome Detection: Multiple approaches for detecting palindromes

Dynamic Programming

• Fibonacci: Multiple implementations including recursive, memoized, tabulated, space-optimized, and matrix exponentiation approaches

Algorithms Features

Each algorithm implementation includes:

• Multiple implementation approaches where applicable
• Time and space complexity analysis
• Performance comparisons
• Practical use cases and examples