toolkit

Algorithm Visualizer

Runs in browser

Visualize recursion, trees, and graph algorithms

Recursion

Sorting

Binary Trees

Graphs

How to Use

Select an algorithm and run visualization.

You will see:

  • Step-by-step Execution
  • Recursion Stack or Graph Traversal
  • Variable state changes
Fibonacci Recursion Step 1 / 1

Call Stack

Execution Log

Understanding Algorithms

Algorithms are the heart of computer science. This visualizer helps you build intuition for how common algorithms execute, manage state, and process data structures.

Time Complexity (Big O)

Understanding how algorithms scale with input size (n):

Notation Name Example
O(1) Constant Array access, Hash map lookup
O(log n) Logarithmic Binary Search
O(n) Linear Linear Search, Tree Traversal
O(n log n) Linearithmic Merge Sort, Quick Sort, Heap Sort
O(n²) Quadratic Bubble Sort, Selection Sort
O(2ⁿ) Exponential Recursive Fibonacci

💡 Pro Tips

  • Recursion is elegant but can cause Stack Overflow errors if too deep
  • Binary Search requires sorted data to work
  • DFS uses a Stack (LIFO), BFS uses a Queue (FIFO)
  • Merge Sort is stable and efficient (O(n log n)) for large datasets

References & Further Reading

Algorithm Types

Tree & Graph Traversal

  • BFS (Breadth-First Search): Explores neighbors layer by layer. Great for shortest paths in unweighted graphs.
  • DFS (Depth-First Search): Explores as far as possible along each branch before backtracking. Useful for topological sorting and puzzles.

Recursion

  • Base Case: The condition that stops the recursion.
  • Recursive Step: The function calls itself with a smaller input.
  • Call Stack: Tracks active function calls (visualized in the tool).

💡 Interview Tips

  • Always start by clarifying input constraints and edge cases.
  • Explain your thought process out loud before writing code.
  • Know the time and space complexity of your solution.
  • Practice implementing common algorithms like DFS, BFS, and Binary Search from scratch.