Kosaraju scc#268
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Pull Request Overview
This PR adds implementations of Kosaraju's Strongly Connected Components (SCC) algorithm along with numerous other graph algorithms, dynamic programming solutions, mathematics functions, and machine learning implementations in R. The PR includes comprehensive examples and documentation for each algorithm.
- Implements Kosaraju's SCC algorithm for finding strongly connected components in directed graphs
- Adds multiple graph algorithms (DFS, BFS, Dijkstra, Bellman-Ford, Floyd-Warshall, Prim's MST, Kruskal's MST, topological sort, Hamiltonian cycle detection, bridge detection)
- Includes dynamic programming solutions (0/1 knapsack, coin change, LCS, LIS, matrix chain multiplication, minimum path sum, subset sum)
- Provides mathematics utilities (Armstrong numbers, amicable numbers, bisection method, Catalan numbers, Euclidean distance)
- Implements machine learning algorithms (gradient boosting)
Reviewed Changes
Copilot reviewed 139 out of 213 changed files in this pull request and generated 4 comments.
Show a summary per file
| File | Description |
|---|---|
| graph_algorithms/kosaraju_scc.r | Implements Kosaraju's algorithm for finding strongly connected components |
| graph_algorithms/*.r | Multiple graph algorithm implementations (DFS, BFS, shortest paths, MST, etc.) |
| dynamic_programming/*.r | Dynamic programming solutions for classic problems |
| mathematics/*.r | Mathematical utility functions and number theory algorithms |
| machine_learning/gradient_boosting.r | Gradient boosting regressor implementation using R6 classes |
| documentation/.md/.html | Documentation and examples for various algorithms |
| kruskal_mst.r | Duplicate Kruskal's MST implementation in root directory |
Member
|
This algorithm is already implemented in the repository |
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Kosaraju’s Strongly Connected Components (SCC) Algorithm
Kosaraju’s algorithm finds all strongly connected components in a directed graph.
It performs two depth-first searches (DFS): first on the original graph to compute finishing times, then on the transposed graph in decreasing order of finish times to identify SCCs.
Time Complexity: O(V + E)
Space Complexity: O(V + E)
Input: Directed graph as an adjacency list
Output: List of SCCs, each as a vector of vertices
The code also prints the graph and the detected SCCs for easy visualization.