Merge pull request #256 from yonatanwesen/yd/finish-docs

Finished docs for PseudoTransient

Author: ChrisRackauckas

src/pseudotransient.jl

@@ -9,8 +9,26 @@ please see the paper: [Coffey, Todd S. and Kelley, C. T. and Keyes, David E. (20
 SIAM Journal on Scientific Computing,25, 553-569.](https://doi.org/10.1137/S106482750241044X)
 
 ### Keyword Arguments
-
-- `alpha_initial` : the initial pseudo time step. it defaults to 1e-3. If it is small, you are going to need more iterations to converge.
+  - `autodiff`: determines the backend used for the Jacobian. Note that this argument is
+    ignored if an analytical Jacobian is passed, as that will be used instead. Defaults to
+    `nothing` which means that a default is selected according to the problem specification!
+    Valid choices are types from ADTypes.jl.
+  - `concrete_jac`: whether to build a concrete Jacobian. If a Krylov-subspace method is used,
+    then the Jacobian will not be constructed and instead direct Jacobian-vector products
+    `J*v` are computed using forward-mode automatic differentiation or finite differencing
+    tricks (without ever constructing the Jacobian). However, if the Jacobian is still needed,
+    for example for a preconditioner, `concrete_jac = true` can be passed in order to force
+    the construction of the Jacobian.
+  - `linsolve`: the [LinearSolve.jl](https://github.com/SciML/LinearSolve.jl) used for the
+    linear solves within the Newton method. Defaults to `nothing`, which means it uses the
+    LinearSolve.jl default algorithm choice. For more information on available algorithm
+    choices, see the [LinearSolve.jl documentation](https://docs.sciml.ai/LinearSolve/stable/).
+  - `precs`: the choice of preconditioners for the linear solver. Defaults to using no
+    preconditioners. For more information on specifying preconditioners for LinearSolve
+    algorithms, consult the
+    [LinearSolve.jl documentation](https://docs.sciml.ai/LinearSolve/stable/).
+  - `alpha_initial` : the initial pseudo time step. it defaults to 1e-3. If it is small, 
+    you are going to need more iterations to converge but it can be more stable.