Algorithm Visualizers

A simple sorting algorithm that repeatedly steps through the list. Time: O(n^2) avg/worst, O(n) best (already sorted). Space: O(1).

Builds the final sorted array one item at a time. Time: O(n^2) avg/worst, O(n) best (nearly sorted). Space: O(1).

Repeatedly finds the minimum element and puts it at the beginning. Time: O(n^2). Space: O(1).

A divide-and-conquer sorting algorithm. Time: O(n log n) (all cases). Space: O(n).

An efficient divide-and-conquer sort. Time: O(n log n) avg/best, O(n^2) worst (bad pivots). Space: O(log n) recursion avg.

A comparison sort using a binary heap. Time: O(n log n). Space: O(1) extra (in-place).

Sequentially checks each element until a match is found. Time: O(n). Space: O(1).

Finds the position of a target within a sorted array. Time: O(log n). Space: O(1) iterative (O(log n) recursive).

Pathfinding with heuristics (admissible/consistent). Time: O(b^d) worst-case; practical depends on heuristic. Space: O(b^d).

Shortest paths in graphs with non-negative weights. Time: O((V+E) log V) with a binary heap. Space: O(V+E).

Real-time frequency bands with peak hold. Uses Web Audio API and CC sample tracks.

Scrolling time–frequency heatmap using FFT magnitudes.

Particles react to bass/mids/highs using Web Audio + three.js.

Onset detection drives pulses and estimates tempo.

12 pitch-class ring with dominant key highlight.