phillipkerger/zero-order-bounds-lean-verification — explained in plain English
Analysis updated 2026-05-18
Verify that a research paper's lower bound claim about optimization algorithms is mathematically correct.
Study how a complex proof involving convex geometry is formalized step by step in Lean.
Reproduce the paper's verification builds to independently confirm the proof checks out.
Compare the Lean proof's statements against the paper's text using the included comparator tooling.
| phillipkerger/zero-order-bounds-lean-verification | lean-dojo/torchlean | gaearon/analysis-solutions | |
|---|---|---|---|
| Stars | 57 | 55 | 42 |
| Language | Lean | Lean | Lean |
| Last pushed | — | — | 2025-09-23 |
| Maintenance | — | — | Quiet |
| Setup difficulty | hard | hard | moderate |
| Complexity | 5/5 | 5/5 | 4/5 |
| Audience | researcher | researcher | researcher |
Figures from each repo's GitHub metadata at analysis time.
Requires installing the Elan toolchain and building large Lean and mathlib proof files, which can take significant time and disk space.
This repository holds a formal, machine checked proof written in the Lean proof assistant, built to accompany a mathematics research paper about optimization algorithms. It is aimed at researchers in mathematics and computer science rather than general software developers, and its purpose is to verify, line by line, that a specific mathematical claim in the paper is actually true. The claim itself is about a type of optimization problem called derivative free convex optimization, where an algorithm can only ask for the exact value of a function at chosen points, never its slope or direction. The paper argues there is a hard limit on how accurately any such algorithm can find the lowest point of the function within a fixed number of questions, and this repository proves that limit is real using Lean, a tool that checks every logical step of a proof so nothing can be waved through by hand. The repository contains two separate, independently checkable results. The first and simpler one is an older bound already proven and kept unchanged. The second is a sharper, more complete result from the same paper, fully verified here, including the more advanced geometric arguments the paper relies on, such as spherical averaging and a classical inequality called Brunn-Minkowski. The authors are careful to note that only the paper's lower bound claim is verified this way. Other parts of the paper, such as upper bound results, are explicitly outside what this repository checks. To build and verify the proofs yourself, you install a Lean tool called Elan, then run a series of build commands that compile each proof file and check it against a trusted, zero shortcut verification mode, meaning the proof is confirmed from first principles rather than relying on cached results. The repository also includes comparison tooling to check the proof's statements and assumptions against the paper's text. No license information is given in the README.
A machine checked Lean proof verifying a mathematical limit on how accurately derivative free optimization algorithms can find a function's minimum.
Mainly Lean. The stack also includes Lean 4, mathlib, Elan.
Setup difficulty is rated hard, with roughly 1h+ to a first successful run.
Mainly researcher.
This repo across BitVibe Labs
Don't trust strangers blindly. Verify against the repo.