Lean (proof assistant)

Lean is a proof assistant and a functional programming language. It is based on the calculus of constructions with inductive types. It is an open-source project hosted on GitHub. It was developed primarily by Leonardo de Moura while employed by Microsoft Research and now Amazon Web Services, and has had significant contributions from other coauthors and collaborators during its history. Development is currently supported by the non-profit Lean Focused Research Organization (FRO).

History
Lean was launched by Leonardo de Moura at Microsoft Research in 2013. The initial versions of the language, later known as Lean 1 and 2, were experimental and contained features such as support for homotopy type theory – based foundations that were later dropped.

Lean 3 (first released Jan 20, 2017) was the first moderately stable version of Lean. It was implemented primarily in C++ with some features written in Lean itself. After version 3.4.2 Lean 3 was officially end-of-lifed while development of Lean 4 began. In this interim period members of the Lean community developed and released unofficial versions up to 3.51.1.

In 2021, Lean 4 was released, which was a reimplementation of the Lean theorem prover capable of producing C code which is then compiled, enabling the development of efficient domain-specific automation. Lean 4 also contains a macro system and improved type class synthesis and memory management procedures over the previous version. Another benefit compared to Lean 3 is the ability to avoid touching C++ code in order to modify the frontend and other key parts of the core system, as they are now all implemented in Lean and available to the end user to be overridden as needed.

Lean 4 is not backwards-compatible with Lean 3.

In 2023, the Lean FRO was formed, with the goals of improving the language's scalability and usability, and implementing proof automation.

Libraries
The official lean package includes a standard library std4, which implements common data structures that may be used for both mathematical research and more conventional software development.

In 2017, a community-maintained project to develop a Lean library mathlib began, with the goal to digitize as much of pure mathematics as possible in one large cohesive library, up to research level mathematics. As of November 2023, mathlib had formalized over 127,000 theorems and 70,000 definitions in Lean.

Editors integration
Lean integrates with:


 * Visual Studio Code
 * Neovim
 * Emacs

Interfacing is done via a client-extension and Language Server Protocol server.

It has native support for Unicode symbols, which can be typed using LaTeX-like sequences, such as "\times" for "×". Lean can also be compiled to JavaScript and accessed in a web browser and has extensive support for meta-programming.

Examples (Lean 4)
The natural numbers can be defined as an inductive type. This definition is based on the Peano axioms and states that every natural number is either zero or the successor of some other natural number.

Addition of natural numbers can be defined recursively, using pattern matching.

This is a simple proof in Lean using tactic mode:

This same proof in term mode:

Mathematics
Lean has received attention from mathematicians such as Thomas Hales and Kevin Buzzard. Hales is using it for his project, Formal Abstracts. Buzzard uses it for the Xena project. One of the Xena Project's goals is to rewrite every theorem and proof in the undergraduate math curriculum of Imperial College London in Lean.

In 2021, a team of researchers used Lean to verify the correctness of a proof by Peter Scholze in the area of condensed mathematics. The project garnered attention for formalizing a result at the cutting edge of mathematical research. In 2023, Terence Tao used Lean to formalize a proof of the Polynomial Freiman-Ruzsa (PFR) conjecture, a result published by Tao and collaborators in the same year.

Artificial intelligence
In 2022, OpenAI created a neural network-based theorem prover for Lean, which used a language model to generate proofs of various high-school-level olympiad problems.

Later that year, Meta AI created an AI model that has solved 10 International Mathematical Olympiad problems; this model is available for public use with the Lean environment.