Visual Algorithm

What you can get in the app - Step-by-step execution: Key operations such as comparisons, swaps, pointer moves, push/pop, rotations and recoloring, relaxation updates, and more are highlighted in real time. - Full process control: Pause/resume, single-step, and replay (great for review and explanation). - Side-by-side learning: Compare how different algorithms behave on the same input to understand why one is faster/slower, stable/unstable, and when to use each. - Stats and metrics: In modules like sorting, display metrics such as comparison count and swap count to build complexity awareness and performance intuition. - Data structure + operations together: Not only the “algorithm,” but also how the underlying data structure changes as operations are applied. Content overview (by module) 1) Sorting Ideal for bar/column visualizations with frequent, high-contrast changes. Built-in algorithms include: - Bubble Sort, Selection Sort, Insertion Sort, Shell Sort - Merge Sort, Quick Sort, Heap Sort - Counting Sort, Radix Sort, Bucket Sort - Cocktail Shaker Sort, Comb Sort - Hybrid sorts (e.g., TimSort, for advanced extensions) Visualization focus: comparisons and swaps, partition/merge processes, heapify operations, and behavior/metrics differences across algorithms at the same input size. 2) Searching Demonstrates “search paths” and pointer movement using arrays, sorted sequences, and tree structures: - Linear Search, Binary Search, Interpolation Search - Skip List (multi-level structure + search process) Plus structure-based searching: - Search in BST/AVL/Red-Black Trees - B-Tree / B+ Tree search (useful for index and disk-structure intuition) - Hash-based search (collision handling: separate chaining, open addressing) 3) Data Structures and Core Operations Binds “structure shape” and “operation steps” together so you understand the mechanism and cost behind each operation. Linear structures - Arrays: access, insert, delete (including shifting) - Linked lists: singly/doubly/circular (insert, delete, reverse) - Stacks: push/pop, applications like parentheses matching - Queues: standard queue, circular queue, deque, priority queue Tree structures - Binary tree traversals: preorder/inorder/postorder (recursive vs iterative), level-order traversal - BST: insert/delete/search - AVL: LL/RR/LR/RL rotations and trigger conditions - Red-Black Tree: rotations and recoloring during insert/delete - Heap: insert, delete-top, heapify - Trie: insert/search/delete - Segment Tree / Fenwick Tree (BIT): point update, range query Graph structure basics - Adjacency matrix vs adjacency list - Undirected graphs, directed graphs, weighted graphs 4) Graph Algorithms Dynamic node-edge animations are especially intuitive for building graph theory intuition. Traversal and connectivity - DFS, BFS, connected components, topological sorting Shortest paths - Dijkstra, Bellman–Ford, Floyd–Warshall, A* search Minimum spanning tree - Prim, Kruskal (with Union-Find visualization) Advanced: matching and flow - Bipartite matching (augmenting path idea) - Maximum flow (Ford–Fulkerson / Edmonds–Karp) 5) String Algorithms Presented with “text + pointers/windows/tables”: - Naive matching, KMP (prefix table), Rabin–Karp (rolling hash) - Boyer–Moore / Horspool - Suffix array construction, (advanced) suffix automaton / suffix tree - LCS, longest common substring, edit distance (DP table animation) 6) Recursion and Backtracking Shows the essence of problem-solving with “recursion tree + call stack + backtracking path”: - Permutations/combinations/subsets - N-Queens, Sudoku, maze paths, Tower of Hanoi - Highlighting choice points and undo steps during backtracking 7) Dynamic Programming Uses state tables/grids to show exactly “where each transition comes from”: - Fibonacci (recursion vs memoization vs iteration) - 0/1 knapsack, unbounded knapsack - LCS, edit distance - Grid min path / max weight path - Advanced: interval DP, tree DP