🌲 Algorithms

Algorithms let us think about computation independently of the limitations of a computer or programming language.

1. 💧 Breadth First Search explores a graph level by level, ideal for finding shortest paths in unweighted graphs.
2. 🚟 Depth First Search dives deep along one branch before backtracking, useful for connectivity and topological sort.
3. 🗺️ Dijkstra finds shortest paths from a source in a non-negative weighted graph using a priority queue.
4. 🚢 Prim’s and Kruskal’s Algorithm greedily builds a minimum spanning tree by selecting the lightest edges without creating cycles.
5. 🥽 Union-Find manages disjoint sets with near‑constant time union and find operations (useful for connectivity).
6. 📍 Hashmaps store key–value pairs for average $O(1)$ lookup, insertion, and deletion.
7. 🍽️ Stacks provide a LIFO structure supporting push and pop at one end, handy for backtracking and parsing.
8. 🏁 Queues provide a FIFO structure supporting enqueue at rear and dequeue from front, ideal for BFS.
9. ⛓️ Linked Lists provide a sequence of nodes where each node points to the next, allowing $O(1)$ insertion/deletion with a pointer.
10. 📊 Graphs model entities (nodes) and their relationships (edges), foundational for network and connectivity problems.
11. 🌳 Binary Search Trees are binary trees where left child < node < right child, giving $O(\log n)$ search on average.
12. ⛰️ Heaps are a tree‑based priority queue that lets us extract the max (or min) in $O(\log n)$.
13. 📐 K-Dimensional Trees partition k‑dimensional points recursively, useful for nearest neighbor searches.
14. 📂 B-Trees and B+ Trees provide wide, multi‑way trees optimized for disk and block storage, minimizing I/O operations.