linxal

Status

Description

linxal is a linear algebra package for rust. linxal uses LAPACK as a backend, (specifically with the lapack package) to execute linear algebra routines with rust-ndarray as inputs and outputs.

Documentation

Documentation can be found at https://github.masonium.io/rustdoc/linxal/.

Example

#[macro_use]
extern crate linxal;
extern crate ndarray;

use linxal::eigenvalues::{Eigen};
use linxal::types::{c32, Magnitude};
use ndarray::{Array, arr1, arr2};

fn main() {
	let m = arr2(&[[1.0f32, 2.0], [-2.0, 1.0]]);

	let r = Eigen::compute_into(m, false, true);
	assert!(r.is_ok());

	let r = r.unwrap();
	let true_evs = arr1(&[c32::new(1.0, 2.0), c32::new(1.0, -2.0)]);
	assert_eq_within_tol!(true_evs, r.values, 0.01);
}

Priorities

• Correctness: linxal will strive for correctness in all cases. Any function returning a non-Err result should return a correct result.

• Ease of Use: linxal will provide a consistent interface and should require minimal setup. Most routine should be high-level and should require no knowledge of the underlying LAPACK routines.

  linxal will minimize surprising behaviour.

• Documentation: linxal will strive to provide documentation for all functionality. Undocumented public features are a bug.

• Ergonomics: linxal will try to minimize boilerplate whenever appropriate.

• Speed

Non-Goals

• Low-dimension arithmetic: linxal is not specifically designed or optimized for {2,3,4}-D problems, as you would encounter in computer graphics, physics, or other domains. There are libraries such as nalgebra and cgmath that specialize in low-dimensional algorithms.

• Representation flexibility: ndarray is the only for standard matrices, and future representations of specialized formats (packed triangular, banded, tridiagonal, etc.) will probably not allow for user-defined formats.

Goals

• Major Linear algbra routines
  • Eigenvalues
  • Singular Value
  • Linear Solvers
  • Linear Least-Squares
  • Matrix Factorizations (QR, LU, etc.)
  • Generalized Eigenvalues
  • Generalized Singular Value Decomposition
• Multiple matrix formats
  • General (direct via ndarray)
  • Symmetric / Hermitian
  • Banded (Packed)

Contributing

Pull requests of all kinds (code, documentation, formatting, spell-checks) are welcome!