Document precs

docs/src/basics/Preconditioners.md

@@ -4,15 +4,13 @@
 an approximation to the matrix inverse action which is cheap to evaluate. These
 can improve the numerical conditioning of the solver process and in turn improve
 the performance. LinearSolve.jl provides an interface for the definition of
-preconditioners which works with the wrapped packages.
+preconditioners which works with the wrapped iterative solver packages.
 
 ## Using Preconditioners
 
 ### Mathematical Definition
 
-Preconditioners are specified in the keyword arguments to `init` or `solve`: `Pl` for left
-and `Pr` for right preconditioner, respectively.
-The right preconditioner, ``P_r`` transforms the linear system ``Au = b`` into the form:
+A right preconditioner, ``P_r`` transforms the linear system ``Au = b`` into the form:
 
 ```math
 AP_r^{-1}(P_r u) = AP_r^{-1}y = b
@@ -31,10 +29,12 @@
 P_l^{-1}A P_r^{-1} (P_r u) = P_l^{-1}b
 ```
 
-By default, if no preconditioner is given, the preconditioner is assumed to be
+### Specifying  Preconditioners
+
+One way to specify preconditioners uses the `Pl` and `Pr`  keyword arguments to `init` or `solve`: `Pl` for left
+and `Pr` for right preconditioner, respectively. By default, if no preconditioner is given, the preconditioner is assumed to be
 the identity ``I``.
 
-### Using Preconditioners
 
 In the following, we will use the `DiagonalPreconditioner` to define a two-sided
 preconditioned system which first divides by some random numbers and then
@@ -55,6 +55,44 @@
 sol.u
 ```
 
+Alternatively, preconditioners can be specified via the  `precs`  argument to the constructor of
+an iterative solver specification. This argument shall deliver a function mapping `A` and a
+parameter `p` to a tuple `(Pl,Pr)` consisting a left and a right preconditioner.
+
+
+```@example precon2
+using LinearSolve, LinearAlgebra
+n = 4
+s = rand(n)
+
+A = rand(n, n)
+b = rand(n)
+
+prob = LinearProblem(A, b)
+sol = solve(prob, KrylovJL_GMRES(precs = (A,p)->(Diagonal(A),I)) )
+sol.u
+```
+This approach has the advantage that the specification of the preconditioner is possible without
+the knowledge of a concrete matrix `A`. It also allows to specifiy the preconditioner via a functor struct:
+
+```@example precon2
+using LinearSolve, LinearAlgebra
+
+struct DiagonalPrecs end
+
+(::DiagonalPrecs)(A,p) = (Diagonal(A),I)
+
+n = 4
+s = rand(n)
+
+A = rand(n, n)
+b = rand(n)
+
+prob = LinearProblem(A, b)
+sol = solve(prob, KrylovJL_GMRES(precs = DiagonalPrecs()) )
+sol.u
+```
+
 ## Preconditioner Interface
 
 To define a new preconditioner you define a Julia type which satisfies the