Computer Science / Subcategory
Algorithms
Algorithm calculators and formulas for time complexity, recurrence relations, and sorting. Explore Big O notation, master theorem solutions, and comparisons between sorting and searching algorithms.
9Formulas
All levels Difficulty
Updated Oct 2026
← All Computer Science (60) Algorithms (9)
Fundamental
Brute-Force Key Space Cracking Time Estimate
Time = 2^(bits-1) / Guesses per Second
Fundamental
Password Entropy (Bits of Security)
Entropy = L * log2(N)
Fundamental
Amortized Time Complexity (Dynamic Array Doubling)
Amortized Cost per Insert = O(1), Total Cost = O(n)
Fundamental
Linear Search Average Comparisons
Avg Comparisons = (n+1) / 2
Fundamental
Quick Sort Average-Case Time Complexity
T(n) = n*log2(n)
Fundamental
Bubble Sort Worst-Case Time Complexity
T(n) = n*(n-1)/2 comparisons
Fundamental
Recurrence Relation (Master Theorem)
T(n) = aT(n/b) + f(n)
Fundamental
Merge Sort Time Complexity
O(n log n)
Fundamental
Binary Search Time Complexity
O(log n)