$ show dimforge/nalgebra
nalgebra is a high-performance linear algebra library for Rust, providing generic static and dynamic matrices/vectors, geometric transformations (rotations, isometries, quaternions, dual quaternions), and a comprehensive set of matrix decompositions (LU, QR, Cholesky, SVD, eigenvalue, Schur, Hessenberg, and more) implemented in pure Rust or backed by LAPACK via the nalgebra-lapack subcrate. The core library emphasizes type-safe dimensions at compile time, generic storage abstractions that support stack-allocated, heap-allocated, and view-based memory, and SIMD acceleration through the simba crate. Additional subcrates extend functionality: nalgebra-sparse handles CSR/CSC/COO sparse matrix formats with operations and IO (Matrix Market), nalgebra-glm provides a GLM-compatible graphics API, and nalgebra-macros offers procedural macros for ergonomic matrix construction. The library is production-grade, with extensive unit and property-based tests (proptest), benchmarks, and CI covering no-std and WASM targets. Its architecture is modular: the base module defines the core matrix type, storage traits, and dimension system; geometry module provides high-level wrappers; linalg module implements decompositions; and each subcrate is independently versioned and tested. The design exhibits strong engineering practices—clean trait boundaries, careful use of unsafe code (documented invariants), and macro-driven operator overloading to reduce boilerplate. While some modules (sparse, GLM) are less mature than the core, the library remains actively maintained and widely adopted in robotics, graphics, and scientific computing. Notable reusable components include the SparsityPattern structure for CSR/CSC index management, the Matrix Market parser (PEG-based), and the procedural macros for compile-time validated matrix literals. These components encapsulate non-trivial domain logic and could be adapted for other Rust linear algebra libraries with minimal modification.
The LLM identified 3 files worth highlighting for reuse:
Parser for the Matrix Market file format using a pest PEG grammar. It handles both coordinate (COO) and array (dense) formats for real and complex scalar types. The implementation uses sealed traits and macro-generated scalar handlers to support multiple numeric types while keeping the parsing logic generic. This module is self-contained and could be extracted as a standalone crate for reading/writing Matrix Market files in any Rust project that needs sparse/dense matrix IO.
Encapsulates the invariant index structure of CSR/CSC sparse matrices (sorted row/column indices, no duplicates). Provides safe construction from sorted arrays and implements pattern-union and pattern-multiplication algorithms needed for sparse-sparse addition and multiplication. This component abstracts away the low-level index manipulation and is reusable in any sparse linear algebra library.
Procedural macro that enables ergonomic construction of static matrices (e.g., `matrix![1.0, 2.0; 3.0, 4.0]`) with compile-time dimension validation. It generates efficient code by directly initializing the underlying array storage and includes thorough error handling (via trybuild tests) for mismatched dimensions. This demonstrates advanced Rust macro techniques and can be reused or adapted for other linear algebra crates that support const-generic dimensions.