Crate sprs

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Expand description

sprs is a sparse linear algebra library for Rust.

It features a sparse matrix type, CsMat, and a sparse vector type, CsVec, both based on the compressed storage scheme.

Features

  • sparse matrix/sparse matrix addition, multiplication.
  • sparse vector/sparse vector addition, dot product.
  • sparse matrix/dense matrix addition, multiplication.
  • sparse triangular solves.
  • powerful iteration over the sparse structure, enabling easy extension of the library.
  • matrix construction using the triplet format, vertical and horizontal stacking, block construction.
  • sparse cholesky solver in the separate crate sprs-ldl.
  • fully generic integer type for the storage of indices, enabling compact representations.
  • planned interoperability with existing sparse solvers such as SuiteSparse.

Quick Examples

Matrix construction:

use sprs::{CsMat, TriMat};

let mut a = TriMat::new((4, 4));
a.add_triplet(0, 0, 3.0_f64);
a.add_triplet(1, 2, 2.0);
a.add_triplet(3, 0, -2.0);

// This matrix type does not allow computations, and must to
// converted to a compatible sparse type, using for example
let b: CsMat<_> = a.to_csr();

Constructing matrix using the more efficient direct sparse constructor

use sprs::{CsMat, CsVec};
let eye : CsMat<f64> = CsMat::eye(3);
let a = CsMat::new_csc((3, 3),
                       vec![0, 2, 4, 5],
                       vec![0, 1, 0, 2, 2],
                       vec![1., 2., 3., 4., 5.]);

Matrix vector multiplication:

use sprs::{CsMat, CsVec};
let eye = CsMat::eye(5);
let x = CsVec::new(5, vec![0, 2, 4], vec![1., 2., 3.]);
let y = &eye * &x;
assert_eq!(x, y);

Matrix matrix multiplication, addition:

use sprs::{CsMat, CsVec};
let eye = CsMat::eye(3);
let a = CsMat::new_csc((3, 3),
                       vec![0, 2, 4, 5],
                       vec![0, 1, 0, 2, 2],
                       vec![1., 2., 3., 4., 5.]);
let b = &eye * &a;
assert_eq!(a, b.to_csc());

Re-exports

Modules

Structs

Enums

Traits

  • DenseVector
    A trait for types representing dense vectors, useful for expressing algorithms such as sparse-dense dot product, or linear solves.
  • DenseVectorMut
    Trait for dense vectors that can be modified, useful for expressing algorithms which compute a resulting dense vector, such as solvers.
  • MulAcc
    Trait for types that have a multiply-accumulate operation, as required in dot products and matrix products.
  • SparseMat
    A trait for common members of sparse matrices

Functions

Type Definitions