From 10758e807972d539515784bb9ce9e5f4426aec18 Mon Sep 17 00:00:00 2001
From: Chris Rackauckas <accounts@chrisrackauckas.com>
Date: Sun, 18 Jun 2023 10:18:53 -0400
Subject: [PATCH 1/2] Improve a few docstrings

---
 src/factorization.jl | 32 ++++++++++++++++----------------
 1 file changed, 16 insertions(+), 16 deletions(-)

diff --git a/src/factorization.jl b/src/factorization.jl
index 1fc7ef6a2..e6feb6121 100644
--- a/src/factorization.jl
+++ b/src/factorization.jl
@@ -30,13 +30,13 @@ end
 
 Julia's built in `lu`. Equivalent to calling `lu!(A)`
     
-      * On dense matrices, this uses the current BLAS implementation of the user's computer,
-        which by default is OpenBLAS but will use MKL if the user does `using MKL` in their
-        system.
-      * On sparse matrices, this will use UMFPACK from SuiteSparse. Note that this will not
-        cache the symbolic factorization.
-      * On CuMatrix, it will use a CUDA-accelerated LU from CuSolver.
-      * On BandedMatrix and BlockBandedMatrix, it will use a banded LU.
+* On dense matrices, this uses the current BLAS implementation of the user's computer,
+which by default is OpenBLAS but will use MKL if the user does `using MKL` in their
+system.
+* On sparse matrices, this will use UMFPACK from SuiteSparse. Note that this will not
+cache the symbolic factorization.
+* On CuMatrix, it will use a CUDA-accelerated LU from CuSolver.
+* On BandedMatrix and BlockBandedMatrix, it will use a banded LU.
 
 ## Positional Arguments
 
@@ -136,12 +136,12 @@ end
 
 Julia's built in `qr`. Equivalent to calling `qr!(A)`.
     
-      * On dense matrices, this uses the current BLAS implementation of the user's computer
-        which by default is OpenBLAS but will use MKL if the user does `using MKL` in their
-        system.
-      * On sparse matrices, this will use SPQR from SuiteSparse
-      * On CuMatrix, it will use a CUDA-accelerated QR from CuSolver.
-      * On BandedMatrix and BlockBandedMatrix, it will use a banded QR.
+* On dense matrices, this uses the current BLAS implementation of the user's computer
+which by default is OpenBLAS but will use MKL if the user does `using MKL` in their
+system.
+* On sparse matrices, this will use SPQR from SuiteSparse
+* On CuMatrix, it will use a CUDA-accelerated QR from CuSolver.
+* On BandedMatrix and BlockBandedMatrix, it will use a banded QR.
 """
 struct QRFactorization{P} <: AbstractFactorization
     pivot::P
@@ -324,9 +324,9 @@ end
 
 Julia's built in `svd`. Equivalent to `svd!(A)`.
     
-      * On dense matrices, this uses the current BLAS implementation of the user's computer
-        which by default is OpenBLAS but will use MKL if the user does `using MKL` in their
-        system.
+* On dense matrices, this uses the current BLAS implementation of the user's computer
+which by default is OpenBLAS but will use MKL if the user does `using MKL` in their
+system.
 """
 struct SVDFactorization{A} <: AbstractFactorization
     full::Bool

From 9c85e96694fcc6779c98eb6d6476b3d8fb28f6bc Mon Sep 17 00:00:00 2001
From: Chris Rackauckas <accounts@chrisrackauckas.com>
Date: Sun, 18 Jun 2023 10:19:28 -0400
Subject: [PATCH 2/2] don't forget pardiso

---
 src/extension_algs.jl | 22 ++++++++++++++++++++++
 1 file changed, 22 insertions(+)

diff --git a/src/extension_algs.jl b/src/extension_algs.jl
index 43d5a25b8..2bdb9d2da 100644
--- a/src/extension_algs.jl
+++ b/src/extension_algs.jl
@@ -180,6 +180,28 @@ MKLPardisoIterate(; kwargs...) = PardisoJL(; solver_type = 1, kwargs...)
         end
     end
 else
+    """
+    ```julia
+    PardisoJL(; nprocs::Union{Int, Nothing} = nothing,
+                solver_type = nothing,
+                matrix_type = nothing,
+                iparm::Union{Vector{Tuple{Int, Int}}, Nothing} = nothing,
+                dparm::Union{Vector{Tuple{Int, Int}}, Nothing} = nothing)
+    ```
+
+    A generic method using MKL Pardiso. Specifying `solver_type` is required.
+
+    !!! note
+
+        Using this solver requires adding the package Pardiso.jl, i.e. `using Pardiso`
+
+    ## Keyword Arguments
+
+    For the definition of the keyword arguments, see the Pardiso.jl documentation.
+    All values default to `nothing` and the solver internally determines the values
+    given the input types, and these keyword arguments are only for overriding the
+    default handling process. This should not be required by most users.
+    """
     Base.@kwdef struct PardisoJL <: LinearSolve.SciMLLinearSolveAlgorithm
         nprocs::Union{Int, Nothing} = nothing
         solver_type::Any = nothing