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Sparse column permutation based on nonzero count

`j = colperm(S)`

`j = colperm(S)` generates
a permutation vector `j` such that the columns of `S(:,j)` are
ordered according to increasing count of nonzero entries. This is
sometimes useful as a preordering for LU factorization; in this case
use `lu(S(:,j))`.

If `S` is symmetric, then `j` `=` `colperm(S)` generates
a permutation `j` so that both the rows and columns
of `S(j,j)` are ordered according to increasing count
of nonzero entries. If `S` is positive definite,
this is sometimes useful as a preordering for Cholesky factorization;
in this case use `chol(S(j,j))`.

The `n`-by-`n` *arrowhead* matrix

A = [ones(1,n); ones(n-1,1) speye(n-1,n-1)]

has a full first row and column. Its LU factorization, `lu(A)`,
is almost completely full. The statement

j = colperm(A)

returns `j` `=` `[2:n` `1]`.
So `A(j,j)` sends the full row and column to the
bottom and the rear, and `lu(A(j,j))` has the same
nonzero structure as `A` itself.

On the other hand, the Bucky ball example,

B = bucky

has exactly three nonzero elements in each row and column, so `j
= colperm(B)` is the identity permutation and is no help
at all for reducing fill-in with subsequent factorizations.

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