About AlgoHay

What AlgoHay is

AlgoHay is a small, independent site for anyone working with algorithms and data structures — students studying for exams, developers prepping for interviews, engineers who just want a faster way to reason about Big O than a napkin sketch. It’s built around six calculators and a set of reference guides. That’s the whole premise.

The tools

Six tools live at /tools/: a Big O Complexity Analyzer, a Master Theorem Calculator, an Amortized Analysis Calculator, a Recursion Tree Calculator, an Interview Complexity Estimator, and an Algorithm Comparison Tool. Each targets one specific piece of algorithm analysis that’s easy to get wrong by hand or by memory.

How they work

The Big O Complexity Analyzer ranks growth classes from O(1) to O(n!) by computed operation count at whatever input size you enter, so you can see which curve actually wins as n grows instead of guessing. The Master Theorem Calculator takes a, b, and d from a recurrence T(n)=a·T(n/b)+n^d, works out which of the three Master Theorem cases applies, and returns the tight Θ bound. The Recursion Tree Calculator expands that same style of recurrence level by level — subproblem count, size, and work per level — up to log_b(n) levels, then sums the total work.

The Amortized Analysis Calculator walks the aggregate method across n appends to a doubling dynamic array, showing why the cost per append stays O(1) even though individual resizes are expensive. The Interview Complexity Estimator is rules-based: pick the techniques your solution uses and its input-size regime, and it returns an expected time/space complexity read plus a rough difficulty band. The Algorithm Comparison Tool lines up classic sorts, searches, and graph traversals by best/average/worst time, space, and stability, ranked by operation count at whichever n you pick.

Where the numbers come from

Nothing here is benchmarked on real hardware. Every result comes from the standard closed-form definition of each technique — the Master Theorem’s case conditions, the aggregate-cost argument for dynamic-array doubling, the textbook time and space bounds for each sort, search, and traversal. That keeps the tools consistent and repeatable, and it’s also why they can’t tell you how your specific code will perform once cache behavior, language runtime, and real input distributions get involved.

The Museum of Algorithms

/museum/is a separate, browsable timeline of the field — classic algorithms, the data structures behind them, the people who invented them, and the theory that underpins it all. Every entry, image, and fact there is sourced from Wikipedia and Wikimedia Commons, not written by us: we built the timeline, we didn’t write the history.

Guides and further reading

/blog/ and /learn/hold explainer articles and reference notes that pair with the tools above — the kind of thing worth having open in a second tab the first time you run a recurrence through the Master Theorem Calculator.

Straight talk about ads & advice

AlgoHay is free to use and supported by on-page advertising — that’s what keeps it free and keeps it online. It’s an independent site, not affiliated with any university, employer, certification body, or the authors of any algorithm, textbook, or standard it references. Everything the tools return is general information for learning and interview prep, not a guarantee about your code, your interview outcome, or your coursework grade — verify anything that matters against your own testing and authoritative references.

Getting in touch

Found a bug in a calculator, or something off on the museum timeline? See /contact/.