custom.md

[Passing in a Custom Linear Solver](@id custom)

Julia users are building a wide variety of applications in the SciML ecosystem, often requiring problem-specific handling of their linear solves. As existing solvers in LinearSolve.jl may not be optimally suited for novel applications, it is essential for the linear solve interface to be easily extendable by users. To that end, the linear solve algorithm LinearSolveFunction() accepts a user-defined function for handling the solve. A user can pass in their custom linear solve function, say my_linsolve, to LinearSolveFunction(). A contrived example of solving a linear system with a custom solver is below.

using LinearSolve, LinearAlgebra

function my_linsolve(A, b, u, p, newA, Pl, Pr, solverdata; verbose = true, kwargs...)
    if verbose == true
        println("solving Ax=b")
    end
    u = A \ b
    return u
end

prob = LinearProblem(Diagonal(rand(4)), rand(4))
alg = LinearSolveFunction(my_linsolve)
sol = solve(prob, alg)
sol.u

The inputs to the function are as follows:

• u, the solution initialized as zero(b),
• A, the linear operator
• b, the right-hand-side
• p, a set of parameters
• newA, a Bool which is true if A has been modified since last solve
• Pl, left-preconditioner
• Pr, right-preconditioner
• solverdata, solver cache set to nothing if solver hasn't been initialized
• kwargs, standard SciML keyword arguments such as verbose, maxiters, abstol, reltol

The function my_linsolve must accept the above specified arguments and modify the, and return the solution, u. As memory for u is already allocated, the user may choose to modify u in place as follows:

function my_linsolve!(A, b, u, p, newA, Pl, Pr, solverdata; verbose = true, kwargs...)
    if verbose == true
        println("solving Ax=b")
    end
    u .= A \ b # in place
    return u
end

alg = LinearSolveFunction(my_linsolve!)
sol = solve(prob, alg)
sol.u