vectormatrix

Idiomatic Matrix and Vector types for Rust.

This crate implements small stack-allocated Vector and Matrix types with the obvious semantics and operations. no_std support and zero dependencies.

Core Types

• Vector<T, N> - An N-dimensional vector with elements of type T
• NormalizedVector<T, N> - A unit vector (length = 1.0) with compile-time guarantee
• Matrix<T, R, C> - An R×C matrix with elements of type T

Operations

Most obvious matrix and vector operations including:

• scalar addition, subtraction, multiplication, and division with operator overloading
• elementwise addition, subtraction, multiplication, and division with operator overloading
• map operations for transforming elements
• approximate equality for floating point types

Vector operations

• dot product
• cross product (3D vectors only)
• length, square length
• normalization to unit vector
• euclidean distance
• convert to/from row and column matrix
• element access via x(), y(), z(), w() methods or indexing
• min/max element finding
• clamping and linear interpolation (mix)

Matrix operations

• matrix multiplication
• transpose
• determinant (for 2×2, 3×3, and 4×4)
• inverse (for 2×2, 3×3, and 4×4)
• common affine transformations for 3×3 and 4×4 matrices:
  • translation
  • rotation (2D for 3×3, 3D axis-aligned for 4×4)
  • scaling
  • shearing
• column and row access
• element access via element_at() or direct column indexing

Type design

• Use generics to support any element type with appropriate numeric traits
• Use const generics to encode dimensions in the type system and enable stack allocation
• Column-major storage for cache efficiency and graphics API compatibility
• Where practical, use generics for optimized code at any matrix size
• Where mathematically appropriate, implement particular algorithms for particular size
• All operations are inlined for maximum performance
• Use repr(Rust) so that the compiler may optimize memory layout for SIMD (Convert to your own repr(C) type if you need specific memory layout)
• API design supports future SIMD or hardware accelerated operations

Supported numeric types

The library provides trait implementations for standard numeric types:

• Floating point: f32, f64 (with additional operations like sqrt, sin, cos)
• Unsigned integers: u8, u16, u32, u64
• Signed integers: i8, i16, i32, i64

Features

• std (default): Enables standard library support for floating point functions like sqrt, sin, cos
• Without std: Core functionality works in no_std environments