Fast implementations of linear solving algorithms in Julia that satisfy the SciML common interface. LinearSolve.jl makes it easy to define high level algorithms which allow for swapping out the linear solver that is used while maintaining maximum efficiency.
For information on using the package, see the stable documentation. Use the in-development documentation for the version of the documentation which contains the unreleased features.
n = 4
A = rand(n,n)
b1 = rand(n); b2 = rand(n)
prob = LinearProblem(A, b1)
linsolve = init(prob)
sol1 = solve(linsolve)
sol1.u
#=
4-element Vector{Float64}:
-0.9247817429364165
-0.0972021708185121
0.6839050402960025
1.8385599677530706
=#
linsolve = LinearSolve.set_b(linsolve,b2)
sol2 = solve(linsolve)
sol2.u
#=
4-element Vector{Float64}:
1.0321556637762768
0.49724400693338083
-1.1696540870182406
-0.4998342686003478
=#
linsolve = LinearSolve.set_b(linsolve,b2)
sol2 = solve(linsolve,IterativeSolversJL_GMRES()) # Switch to GMRES
sol2.u
#=
4-element Vector{Float64}:
1.0321556637762768
0.49724400693338083
-1.1696540870182406
-0.4998342686003478
=#
A2 = rand(n,n)
linsolve = LinearSolve.set_A(linsolve,A2)
sol3 = solve(linsolve)
sol3.u
#=
4-element Vector{Float64}:
-6.793605395935224
2.8673042300837466
1.1665136934977371
-0.4097250749016653
=#