Algorithm & Complexity Tools
Free, practical tools for reasoning about algorithms — analyze Big O growth, solve recurrences with the Master Theorem, work through amortized and recursion-tree analysis, estimate interview complexity, and compare the classic algorithms side by side.
📈 Big O Complexity Analyzer
Compare the common growth classes — O(1) through O(n!) — at any input size, rank their operation counts, and see which of two complexities dominates as n grows
📐 Master Theorem Calculator
Solve divide-and-conquer recurrences T(n)=a·T(n/b)+Θ(n^d) — enter a, b, and d to get log_b(a), the case that applies, and the tight Θ bound with validation
🧮 Amortized Analysis Calculator
Use the aggregate method on a doubling dynamic array to see the total cost across n appends and why the amortized cost per operation stays O(1)
🌳 Recursion Tree Calculator
Expand T(n)=a·T(n/b)+n^d level by level — subproblem count, size, and work per level — then sum the total work and count the log_b(n) levels
🎯 Interview Complexity Estimator
Pick the techniques your solution uses and the input-size regime for a rules-based read on expected time and space complexity, plus a difficulty band
⚖️ Algorithm Comparison Tool
Weigh up classic sorts, searches, and graph traversals across best/average/worst time, space, and stability — and rank them by operation count at any n