Add example for UMFPACK control vector (#449)

* Add example for UMFPACK control vector

Note that Julia modifies the UMFPACK defaults to disable iterative refinement.

Add an example to illustrate usage of the UMFPACK control vector to reenable iterative refinement

* Update umfpack.jl

src/solvers/umfpack.jl

@@ -339,9 +339,16 @@ The permutation `q` can either be a permutation vector or `nothing`. If no permu
 is provided or `q` is `nothing`, UMFPACK's default is used. If the permutation is not zero-based, a
 zero-based copy is made.
 
-The `control` vector defaults to the package's default configuration for UMFPACK, but can be changed by passing a
-vector of length `UMFPACK_CONTROL`. See the UMFPACK manual for possible configurations. The corresponding
-variables are named `JL_UMFPACK_` since Julia uses one-based indexing.
+The `control` vector defaults to the Julia SparseArrays package's default configuration for UMFPACK (NB: this is modified from the UMFPACK defaults to
+disable iterative refinement), but can be changed by passing a vector of length `UMFPACK_CONTROL`, see the UMFPACK manual for possible configurations. 
+For example to reenable iterative refinement:
+
+    umfpack_control = SparseArrays.UMFPACK.get_umfpack_control(Float64, Int64) # read Julia default configuration for a Float64 sparse matrix
+    SparseArrays.UMFPACK.show_umf_ctrl(umfpack_control) # optional - display values
+    umfpack_control[SparseArrays.UMFPACK.JL_UMFPACK_IRSTEP] = 2.0 # reenable iterative refinement (2 is UMFPACK default max iterative refinement steps)
+
+    Alu = lu(A; control = umfpack_control)
+    x = Alu \\ b   # solve Ax = b, including UMFPACK iterative refinement  
 
 The individual components of the factorization `F` can be accessed by indexing: