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All about Graph pathfinding algorithms. How they work, the data structures they use, their time and space complexity, and practical use cases. Use this article as a learning resource and a technical reference guide.
Graph traversal systematically visits vertices. Depth‑First Search dives deeply along paths; Breadth‑First Search expands layer by layer. Built‑ons like topological sort and component detection exploit these passes to order DAGs and expose connectivity.
Discover how sorting algorithms, from bubble to quick, merge, heap, shell, and non‑comparison methods like counting, radix, and bucket, organize data efficiently, highlighting speed, memory use, and stability.
Dive into graph traversal: BFS for shortest unweighted paths, DFS for exhaustive discovery, plus weighted variants—Dijkstra, A*, bidirectional, and heuristic search. Understand frontier control and edge‑cost strategy for optimal exploration.
This article will guide and help you explore search methods and algorithms tailored to core data structures: linear & binary scans in arrays, O(1) hash lookups, prefix tries, balanced BSTs, skip lists, and B‑trees, mechanics, trade‑offs, and practical use cases.
This comprehensive guide demystifies search algorithms, from simple linear scans to hash‑table lookups, tries, balanced trees, and BFS/DFS in graphs. All while showing you how each strategy slashes lookup time for specific data shapes and teaching you how to write efficient code.
