Merge pull request #333 from SciML/docs

Improve a few docstrings

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

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