Static Math in Rust programming language

• This crate take advantage of the static arrays in Rust for fast operations in stack memory.

• We use a tuple to indexing elements: m[(i, j)] allowing nice interface with the match feature of Rust

• No unsafe code

• Could be optimize more with the use of SIMD

• This crate could be used in an no-std environment.

• The determinant of the matrixs are evaluated "in-place" without loops and code bifurcations

• The use cases can be: Robotics, Game programming, Simulations ...etc.

The matrix types Mnn (where n=2..6) implements the Methods from the LinearAlgebra trait:

• det(): Determinant of the matrix
• inverse(): Inverse of the matrix
• qr(): QR decomposition of the matrix
• norm2(): norm of the matrix
• transpose(): transpose of the matrix
• trace(): trace of the matrix
• shape(): shape of the matrix

Benchmarks

Using the criterion crate:

https://github.com/bheisler/criterion.rs

this are the results for matrixs inverse operations(in a very old machine)

```text inverse 6x6 time: [9.6090 us 9.6128 us 9.6172 us] change: [-3.2723% -3.0278% -2.8038%] (p = 0.00 < 0.05) Performance has improved. Found 5 outliers among 100 measurements (5.00%) 1 (1.00%) low mild 1 (1.00%) high mild 3 (3.00%) high severe

inverse 4x4 time: [98.560 ns 98.605 ns 98.677 ns] change: [-5.4359% -3.2101% -1.4680%] (p = 0.00 < 0.05) Performance has improved. Found 15 outliers among 100 measurements (15.00%) 5 (5.00%) high mild 10 (10.00%) high severe ```

you can look the bench here: bench

The same Matrix and test but in Julia language:

```text BenchmarkTools.Trial: memory estimate: 33.48 KiB allocs estimate: 455

minimum time: 1.536 ms (0.00% GC) median time: 1.566 ms (0.00% GC) mean time: 1.643 ms (0.62% GC) maximum time: 20.027 ms (78.89% GC)

samples: 3040 evals/sample: 1 ```

TODOS:

• [ ] Quaternion type and methods
• [ ] expm(): Exponential matrix implementation
• [X] Eigenvalues
• [X] QR decomposition