Add CHOLMOD docs about materializing; list all components

Author: danielwe

src/solvers/cholmod.jl

@@ -1567,6 +1567,12 @@ using just `L` without accounting for `P` will give incorrect answers.
 To include the effects of permutation,
 it's typically preferable to extract "combined" factors like `PtL = F.PtL`
 (the equivalent of `P'*L`) and `LtP = F.UP` (the equivalent of `L'*P`).
+The complete list of supported factors is `:L, :PtL, :UP, :U`.
+The permutation vector is available as `F.p`, defined such that `L*L' == A[p, p]`,
+
+The `L` component can be materialized as a sparse matrix using `sparse(F.L)`.
+Other components cannot be materialized directly, but can be reconstructed
+from `sparse(F.L)` and `F.p` if needed.
 
 When `check = true`, an error is thrown if the decomposition fails.
 When `check = false`, responsibility for checking the decomposition's
@@ -1744,6 +1750,11 @@ To include the effects of permutation, it is typically preferable to extract
 "combined" factors like `PtL = F.PtL` (the equivalent of
 `P'*L`) and `LtP = F.UP` (the equivalent of `L'*P`).
 The complete list of supported factors is `:L, :PtL, :D, :UP, :U, :LD, :DU, :PtLD, :DUP`.
+The permutation vector is available as `F.p`, defined such that `L*D*L' == A[p, p]`,
+
+The `LD` component can be materialized as a sparse matrix using `sparse(F.LD)`,
+Other components cannot be materialized directly, but can be reconstructed from
+`sparse(F.LD)` and `F.p` if needed.
 
 Unlike the related Cholesky factorization, the ``LDL'`` factorization does not
 require `A` to be positive definite. However, it still requires all leading