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Time Complexity Simulator

Visualize recursion trees and derive time complexity!

Linear Recursion

T(n) = T(n-1) + c

Example: Sum of N, Factorial

O(n)

Divide by 2

T(n) = T(n/2) + c

Example: Binary Search

O(log n)

Divide & Conquer

T(n) = 2T(n/2) + n

Example: Merge Sort

O(n log n)

Exponential

T(n) = 2T(n-1) + c

Example: Naive Fibonacci

O(2^n)

Time Complexity

O(n)

Each level does constant work, n levels total

🌳 Recursion Tree Visualization

📐 Master Theorem Quick Reference

Form
T(n) = aT(n/b) + f(n)
Case 1
f(n) < n^(log_b(a))
→ O(n^log_b(a))
Case 2
f(n) = n^(log_b(a))
→ O(n^log_b(a) · log n)
Case 3
f(n) > n^(log_b(a))
→ O(f(n))