## CSparse.jl

A Julia implementation of functions in the CSparse and CXSparse libraries
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Updated Last
3 Years Ago
Started In
November 2012

# CSparse.jl

A Julia implementation of some of the functions in the CSparse and CXSparse libraries developed by Tim Davis

The Julia functions stay very close to the C originals. Most of the differences are in changing 0-based indexing to 1-based indexing and in using the Julia `CompositeKind` rather than a pointer to a C `struct`. This also allows for checking consistency of dimensions.

For example, the C function `cs_lsolve`

```#include "cs.h"
/* solve Lx=b where x and b are dense.  x=b on input, solution on output. */
csi cs_lsolve (const cs *L, double *x)
{
csi p, j, n, *Lp, *Li ;
double *Lx ;
if (!CS_CSC (L) || !x) return (0) ;                     /* check inputs */
n = L->n ; Lp = L->p ; Li = L->i ; Lx = L->x ;
for (j = 0 ; j < n ; j++)
{
x [j] /= Lx [Lp [j]] ;
for (p = Lp [j]+1 ; p < Lp [j+1] ; p++)
{
x [Li [p]] -= Lx [p] * x [j] ;
}
}
return (1) ;
}```

becomes

```## solve Lx=b where x and b are dense.  x=b on input, solution on output.
function js_lsolve!{T}(L::SparseMatrixCSC{T}, x::StridedVector{T})
m,n = size(L)
if m != n error("Matrix L is \$m by \$n and should be square") end
if length(x) != n error("Dimension mismatch") end
Lp = L.colptr; Li = L.rowval; Lx = L.nzval
for j in 1:n
x[j] /= Lx[Lp[j]]
for p in (Lp[j] + 1):(Lp[j+1] - 1)
x[Li[p]] -= Lx[p] * x[j]
end
end
x
end

js_lsolve{T}(L::SparseMatrixCSC{T}, x::StridedVector{T}) = js_lsolve!(L, copy(x))```