simplify dolinsolve

Author: oscarddssmith

lib/OrdinaryDiffEqBDF/src/algorithms.jl

@@ -6,11 +6,10 @@ an Adaptive BDF2 Formula and Comparison with The MATLAB Ode15s. Procedia Compute
 ABDF2: Multistep Method
 An adaptive order 2 L-stable fixed leading coefficient multistep BDF method.
 """
-struct ABDF2{CS, AD, F, F2, P, FDT, ST, CJ, K, T, StepLimiter} <:
+struct ABDF2{CS, AD, F, F2, FDT, ST, CJ, K, T, StepLimiter} <:
        OrdinaryDiffEqNewtonAdaptiveAlgorithm{CS, AD, FDT, ST, CJ}
     linsolve::F
     nlsolve::F2
-    precs::P
     κ::K
     tol::T
     smooth_est::Bool
@@ -20,14 +19,14 @@ struct ABDF2{CS, AD, F, F2, P, FDT, ST, CJ, K, T, StepLimiter} <:
 end
 function ABDF2(; chunk_size = Val{0}(), autodiff = true, standardtag = Val{true}(),
         concrete_jac = nothing, diff_type = Val{:forward},
-        κ = nothing, tol = nothing, linsolve = nothing, precs = DEFAULT_PRECS,
+        κ = nothing, tol = nothing, linsolve = nothing,
         nlsolve = NLNewton(),
         smooth_est = true, extrapolant = :linear,
         controller = :Standard, step_limiter! = trivial_limiter!)
     ABDF2{
         _unwrap_val(chunk_size), _unwrap_val(autodiff), typeof(linsolve), typeof(nlsolve),
-        typeof(precs), diff_type, _unwrap_val(standardtag), _unwrap_val(concrete_jac),
-        typeof(κ), typeof(tol), typeof(step_limiter!)}(linsolve, nlsolve, precs, κ, tol,
+        diff_type, _unwrap_val(standardtag), _unwrap_val(concrete_jac),
+        typeof(κ), typeof(tol), typeof(step_limiter!)}(linsolve, nlsolve, κ, tol,
         smooth_est, extrapolant, controller, step_limiter!)
 end
 
@@ -36,11 +35,10 @@ Uri M. Ascher, Steven J. Ruuth, Brian T. R. Wetton. Implicit-Explicit Methods fo
 Dependent Partial Differential Equations. 1995 Society for Industrial and Applied Mathematics
 Journal on Numerical Analysis, 32(3), pp 797-823, 1995. doi: https://doi.org/10.1137/0732037
 """
-struct SBDF{CS, AD, F, F2, P, FDT, ST, CJ, K, T} <:
+struct SBDF{CS, AD, F, F2, FDT, ST, CJ, K, T} <:
        OrdinaryDiffEqNewtonAlgorithm{CS, AD, FDT, ST, CJ}
     linsolve::F
     nlsolve::F2
-    precs::P
     κ::K
     tol::T
     extrapolant::Symbol
@@ -50,14 +48,13 @@ end
 
 function SBDF(order; chunk_size = Val{0}(), autodiff = Val{true}(),
         standardtag = Val{true}(), concrete_jac = nothing, diff_type = Val{:forward},
-        linsolve = nothing, precs = DEFAULT_PRECS, nlsolve = NLNewton(), κ = nothing,
+        linsolve = nothing, nlsolve = NLNewton(), κ = nothing,
         tol = nothing,
         extrapolant = :linear, ark = false)
     SBDF{_unwrap_val(chunk_size), _unwrap_val(autodiff), typeof(linsolve), typeof(nlsolve),
-        typeof(precs), diff_type, _unwrap_val(standardtag), _unwrap_val(concrete_jac),
+        diff_type, _unwrap_val(standardtag), _unwrap_val(concrete_jac),
         typeof(κ), typeof(tol)}(linsolve,
         nlsolve,
-        precs,
         κ,
         tol,
         extrapolant,
@@ -68,15 +65,14 @@ end
 # All keyword form needed for remake
 function SBDF(; chunk_size = Val{0}(), autodiff = Val{true}(), standardtag = Val{true}(),
         concrete_jac = nothing, diff_type = Val{:forward},
-        linsolve = nothing, precs = DEFAULT_PRECS, nlsolve = NLNewton(), κ = nothing,
+        linsolve = nothing, nlsolve = NLNewton(), κ = nothing,
         tol = nothing,
         extrapolant = :linear,
         order, ark = false)
     SBDF{_unwrap_val(chunk_size), _unwrap_val(autodiff), typeof(linsolve), typeof(nlsolve),
-        typeof(precs), diff_type, _unwrap_val(standardtag), _unwrap_val(concrete_jac),
+        diff_type, _unwrap_val(standardtag), _unwrap_val(concrete_jac),
         typeof(κ), typeof(tol)}(linsolve,
         nlsolve,
-        precs,
         κ,
         tol,
         extrapolant,
@@ -136,11 +132,10 @@ Optional parameter kappa defaults to Shampine's accuracy-optimal -0.1850.
 
 See also `QNDF`.
 """
-struct QNDF1{CS, AD, F, F2, P, FDT, ST, CJ, κType, StepLimiter} <:
+struct QNDF1{CS, AD, F, F2, FDT, ST, CJ, κType, StepLimiter} <:
        OrdinaryDiffEqNewtonAdaptiveAlgorithm{CS, AD, FDT, ST, CJ}
     linsolve::F
     nlsolve::F2
-    precs::P
     extrapolant::Symbol
     kappa::κType
     controller::Symbol
@@ -149,15 +144,14 @@ end
 
 function QNDF1(; chunk_size = Val{0}(), autodiff = Val{true}(), standardtag = Val{true}(),
         concrete_jac = nothing, diff_type = Val{:forward},
-        linsolve = nothing, precs = DEFAULT_PRECS, nlsolve = NLNewton(),
+        linsolve = nothing, nlsolve = NLNewton(),
         extrapolant = :linear, kappa = -37//200,
         controller = :Standard, step_limiter! = trivial_limiter!)
     QNDF1{
         _unwrap_val(chunk_size), _unwrap_val(autodiff), typeof(linsolve), typeof(nlsolve),
-        typeof(precs), diff_type, _unwrap_val(standardtag), _unwrap_val(concrete_jac),
+        diff_type, _unwrap_val(standardtag), _unwrap_val(concrete_jac),
         typeof(kappa), typeof(step_limiter!)}(linsolve,
         nlsolve,
-        precs,
         extrapolant,
         kappa,
         controller,
@@ -170,11 +164,10 @@ An adaptive order 2 quasi-constant timestep L-stable numerical differentiation f
 
 See also `QNDF`.
 """
-struct QNDF2{CS, AD, F, F2, P, FDT, ST, CJ, κType, StepLimiter} <:
+struct QNDF2{CS, AD, F, F2, FDT, ST, CJ, κType, StepLimiter} <:
        OrdinaryDiffEqNewtonAdaptiveAlgorithm{CS, AD, FDT, ST, CJ}
     linsolve::F
     nlsolve::F2
-    precs::P
     extrapolant::Symbol
     kappa::κType
     controller::Symbol
@@ -183,15 +176,14 @@ end
 
 function QNDF2(; chunk_size = Val{0}(), autodiff = Val{true}(), standardtag = Val{true}(),
         concrete_jac = nothing, diff_type = Val{:forward},
-        linsolve = nothing, precs = DEFAULT_PRECS, nlsolve = NLNewton(),
+        linsolve = nothing, nlsolve = NLNewton(),
         extrapolant = :linear, kappa = -1 // 9,
         controller = :Standard, step_limiter! = trivial_limiter!)
     QNDF2{
         _unwrap_val(chunk_size), _unwrap_val(autodiff), typeof(linsolve), typeof(nlsolve),
-        typeof(precs), diff_type, _unwrap_val(standardtag), _unwrap_val(concrete_jac),
+        diff_type, _unwrap_val(standardtag), _unwrap_val(concrete_jac),
         typeof(kappa), typeof(step_limiter!)}(linsolve,
         nlsolve,
-        precs,
         extrapolant,
         kappa,
         controller,
@@ -214,12 +206,11 @@ year={1997},
 publisher={SIAM}
 }
 """
-struct QNDF{MO, CS, AD, F, F2, P, FDT, ST, CJ, K, T, κType, StepLimiter} <:
+struct QNDF{MO, CS, AD, F, F2, FDT, ST, CJ, K, T, κType, StepLimiter} <:
        OrdinaryDiffEqNewtonAdaptiveAlgorithm{CS, AD, FDT, ST, CJ}
     max_order::Val{MO}
     linsolve::F
     nlsolve::F2
-    precs::P
     κ::K
     tol::T
     extrapolant::Symbol
@@ -231,15 +222,15 @@ end
 function QNDF(; max_order::Val{MO} = Val{5}(), chunk_size = Val{0}(),
         autodiff = Val{true}(), standardtag = Val{true}(), concrete_jac = nothing,
         diff_type = Val{:forward},
-        linsolve = nothing, precs = DEFAULT_PRECS, nlsolve = NLNewton(), κ = nothing,
+        linsolve = nothing, nlsolve = NLNewton(), κ = nothing,
         tol = nothing,
         extrapolant = :linear, kappa = (-37//200, -1//9, -823//10000, -83//2000, 0//1),
         controller = :Standard, step_limiter! = trivial_limiter!) where {MO}
     QNDF{MO, _unwrap_val(chunk_size), _unwrap_val(autodiff), typeof(linsolve),
-        typeof(nlsolve), typeof(precs), diff_type, _unwrap_val(standardtag),
+        typeof(nlsolve), diff_type, _unwrap_val(standardtag),
         _unwrap_val(concrete_jac),
         typeof(κ), typeof(tol), typeof(kappa), typeof(step_limiter!)}(
-        max_order, linsolve, nlsolve, precs, κ, tol,
+        max_order, linsolve, nlsolve, κ, tol,
         extrapolant, kappa, controller, step_limiter!)
 end
 
@@ -250,22 +241,20 @@ MEBDF2: Multistep Method
 The second order Modified Extended BDF method, which has improved stability properties over the standard BDF.
 Fixed timestep only.
 """
-struct MEBDF2{CS, AD, F, F2, P, FDT, ST, CJ} <:
+struct MEBDF2{CS, AD, F, F2, FDT, ST, CJ} <:
        OrdinaryDiffEqNewtonAlgorithm{CS, AD, FDT, ST, CJ}
     linsolve::F
     nlsolve::F2
-    precs::P
     extrapolant::Symbol
 end
 function MEBDF2(; chunk_size = Val{0}(), autodiff = true, standardtag = Val{true}(),
         concrete_jac = nothing, diff_type = Val{:forward},
-        linsolve = nothing, precs = DEFAULT_PRECS, nlsolve = NLNewton(),
+        linsolve = nothing, nlsolve = NLNewton(),
         extrapolant = :constant)
     MEBDF2{_unwrap_val(chunk_size), _unwrap_val(autodiff), typeof(linsolve),
-        typeof(nlsolve), typeof(precs), diff_type, _unwrap_val(standardtag),
+        typeof(nlsolve), diff_type, _unwrap_val(standardtag),
         _unwrap_val(concrete_jac)}(linsolve,
         nlsolve,
-        precs,
         extrapolant)
 end
 
@@ -282,12 +271,11 @@ year={2002},
 publisher={Walter de Gruyter GmbH \\& Co. KG}
 }
 """
-struct FBDF{MO, CS, AD, F, F2, P, FDT, ST, CJ, K, T, StepLimiter} <:
+struct FBDF{MO, CS, AD, F, F2, FDT, ST, CJ, K, T, StepLimiter} <:
        OrdinaryDiffEqNewtonAdaptiveAlgorithm{CS, AD, FDT, ST, CJ}
     max_order::Val{MO}
     linsolve::F
     nlsolve::F2
-    precs::P
     κ::K
     tol::T
     extrapolant::Symbol
@@ -298,14 +286,14 @@ end
 function FBDF(; max_order::Val{MO} = Val{5}(), chunk_size = Val{0}(),
         autodiff = Val{true}(), standardtag = Val{true}(), concrete_jac = nothing,
         diff_type = Val{:forward},
-        linsolve = nothing, precs = DEFAULT_PRECS, nlsolve = NLNewton(), κ = nothing,
+        linsolve = nothing, nlsolve = NLNewton(), κ = nothing,
         tol = nothing,
         extrapolant = :linear, controller = :Standard, step_limiter! = trivial_limiter!) where {MO}
     FBDF{MO, _unwrap_val(chunk_size), _unwrap_val(autodiff), typeof(linsolve),
-        typeof(nlsolve), typeof(precs), diff_type, _unwrap_val(standardtag),
+        typeof(nlsolve), diff_type, _unwrap_val(standardtag),
         _unwrap_val(concrete_jac),
         typeof(κ), typeof(tol), typeof(step_limiter!)}(
-        max_order, linsolve, nlsolve, precs, κ, tol, extrapolant,
+        max_order, linsolve, nlsolve, κ, tol, extrapolant,
         controller, step_limiter!)
 end
 
@@ -389,41 +377,39 @@ See also `SBDF`, `IMEXEuler`.
 """
 IMEXEulerARK(; kwargs...) = SBDF(1; ark = true, kwargs...)
 
-struct DImplicitEuler{CS, AD, F, F2, P, FDT, ST, CJ} <: DAEAlgorithm{CS, AD, FDT, ST, CJ}
+struct DImplicitEuler{CS, AD, F, F2, FDT, ST, CJ} <: DAEAlgorithm{CS, AD, FDT, ST, CJ}
     linsolve::F
     nlsolve::F2
-    precs::P
     extrapolant::Symbol
     controller::Symbol
 end
 function DImplicitEuler(;
         chunk_size = Val{0}(), autodiff = true, standardtag = Val{true}(),
         concrete_jac = nothing, diff_type = Val{:forward},
-        linsolve = nothing, precs = DEFAULT_PRECS, nlsolve = NLNewton(),
+        linsolve = nothing, nlsolve = NLNewton(),
         extrapolant = :constant,
         controller = :Standard)
     DImplicitEuler{_unwrap_val(chunk_size), _unwrap_val(autodiff), typeof(linsolve),
-        typeof(nlsolve), typeof(precs), diff_type, _unwrap_val(standardtag),
+        typeof(nlsolve), diff_type, _unwrap_val(standardtag),
         _unwrap_val(concrete_jac)}(linsolve,
-        nlsolve, precs, extrapolant, controller)
+        nlsolve, extrapolant, controller)
 end
 
-struct DABDF2{CS, AD, F, F2, P, FDT, ST, CJ} <: DAEAlgorithm{CS, AD, FDT, ST, CJ}
+struct DABDF2{CS, AD, F, F2, FDT, ST, CJ} <: DAEAlgorithm{CS, AD, FDT, ST, CJ}
     linsolve::F
     nlsolve::F2
-    precs::P
     extrapolant::Symbol
     controller::Symbol
 end
 function DABDF2(; chunk_size = Val{0}(), autodiff = Val{true}(), standardtag = Val{true}(),
         concrete_jac = nothing, diff_type = Val{:forward},
-        linsolve = nothing, precs = DEFAULT_PRECS, nlsolve = NLNewton(),
+        linsolve = nothing, nlsolve = NLNewton(),
         extrapolant = :constant,
         controller = :Standard)
     DABDF2{_unwrap_val(chunk_size), _unwrap_val(autodiff), typeof(linsolve),
-        typeof(nlsolve), typeof(precs), diff_type, _unwrap_val(standardtag),
+        typeof(nlsolve), diff_type, _unwrap_val(standardtag),
         _unwrap_val(concrete_jac)}(linsolve,
-        nlsolve, precs, extrapolant, controller)
+        nlsolve, extrapolant, controller)
 end
 
 #=
@@ -440,11 +426,10 @@ DBDF(;chunk_size=Val{0}(),autodiff=Val{true}(), standardtag = Val{true}(), concr
      linsolve,nlsolve,precs,extrapolant)
 =#
 
-struct DFBDF{MO, CS, AD, F, F2, P, FDT, ST, CJ, K, T} <: DAEAlgorithm{CS, AD, FDT, ST, CJ}
+struct DFBDF{MO, CS, AD, F, F2, FDT, ST, CJ, K, T} <: DAEAlgorithm{CS, AD, FDT, ST, CJ}
     max_order::Val{MO}
     linsolve::F
     nlsolve::F2
-    precs::P
     κ::K
     tol::T
     extrapolant::Symbol
@@ -453,13 +438,13 @@ end
 function DFBDF(; max_order::Val{MO} = Val{5}(), chunk_size = Val{0}(),
         autodiff = Val{true}(), standardtag = Val{true}(), concrete_jac = nothing,
         diff_type = Val{:forward},
-        linsolve = nothing, precs = DEFAULT_PRECS, nlsolve = NLNewton(), κ = nothing,
+        linsolve = nothing, nlsolve = NLNewton(), κ = nothing,
         tol = nothing,
         extrapolant = :linear, controller = :Standard) where {MO}
     DFBDF{MO, _unwrap_val(chunk_size), _unwrap_val(autodiff), typeof(linsolve),
-        typeof(nlsolve), typeof(precs), diff_type, _unwrap_val(standardtag),
+        typeof(nlsolve), diff_type, _unwrap_val(standardtag),
         _unwrap_val(concrete_jac),
-        typeof(κ), typeof(tol)}(max_order, linsolve, nlsolve, precs, κ, tol, extrapolant,
+        typeof(κ), typeof(tol)}(max_order, linsolve, nlsolve, κ, tol, extrapolant,
         controller)
 end
 

lib/OrdinaryDiffEqCore/src/OrdinaryDiffEqCore.jl

@@ -107,7 +107,7 @@ end
 end
 const TryAgain = SlowConvergence
 
-DEFAULT_PRECS(W, du, u, p, t, newW, Plprev, Prprev, solverdata) = nothing, nothing
+DEFAULT_PRECS(W, p) = nothing, nothing
 isdiscretecache(cache) = false
 
 include("doc_utils.jl")

lib/OrdinaryDiffEqDifferentiation/src/OrdinaryDiffEqDifferentiation.jl

@@ -27,7 +27,7 @@ import StaticArrays: SArray, MVector, SVector, @SVector, StaticArray, MMatrix, S
 using DiffEqBase: TimeGradientWrapper,
                   UJacobianWrapper, TimeDerivativeWrapper,
                   UDerivativeWrapper
-using SciMLBase: AbstractSciMLOperator
+using SciMLBase: AbstractSciMLOperator, DEIntegrator
 import OrdinaryDiffEqCore
 using OrdinaryDiffEqCore: OrdinaryDiffEqAlgorithm, OrdinaryDiffEqAdaptiveImplicitAlgorithm,
                           DAEAlgorithm,

lib/OrdinaryDiffEqDifferentiation/src/linsolve_utils.jl

@@ -3,27 +3,20 @@ issuccess_W(W::Number) = !iszero(W)
 issuccess_W(::Any) = true
 
 function dolinsolve(integrator, linsolve; A = nothing, linu = nothing, b = nothing,
-        du = nothing, u = nothing, p = nothing, t = nothing,
-        weight = nothing, solverdata = nothing,
         reltol = integrator === nothing ? nothing : integrator.opts.reltol)
-    A !== nothing && (linsolve.A = A)
     b !== nothing && (linsolve.b = b)
     linu !== nothing && (linsolve.u = linu)
 
-    Plprev = linsolve.Pl isa LinearSolve.ComposePreconditioner ? linsolve.Pl.outer :
-             linsolve.Pl
-    Prprev = linsolve.Pr isa LinearSolve.ComposePreconditioner ? linsolve.Pr.outer :
-             linsolve.Pr
-
     _alg = unwrap_alg(integrator, true)
-
-    _Pl, _Pr = _alg.precs(linsolve.A, du, u, p, t, A !== nothing, Plprev, Prprev,
-        solverdata)
-    if (_Pl !== nothing || _Pr !== nothing)
-        __Pl = _Pl === nothing ? SciMLOperators.IdentityOperator(length(integrator.u)) : _Pl
-        __Pr = _Pr === nothing ? SciMLOperators.IdentityOperator(length(integrator.u)) : _Pr
-        linsolve.Pl = __Pl
-        linsolve.Pr = __Pr
+    if !isnothing(A)
+        if integrator isa DEIntegrator
+            (;u, p, t) = integrator
+            du = hasproperty(integrator, :du) ? integrator.du : nothing
+            p = (du, u, p, t)
+            reinit!(linsolve; A, p)
+        else
+            reinit!(linsolve; A)
+        end
     end
 
     linres = solve!(linsolve; reltol)
@@ -44,16 +37,18 @@ function dolinsolve(integrator, linsolve; A = nothing, linu = nothing, b = nothi
     return linres
 end
 
-function wrapprecs(_Pl::Nothing, _Pr::Nothing, weight, u)
-    Pl = LinearSolve.InvPreconditioner(Diagonal(_vec(weight)))
-    Pr = Diagonal(_vec(weight))
-    Pl, Pr
-end
-
-function wrapprecs(_Pl, _Pr, weight, u)
-    Pl = _Pl === nothing ? SciMLOperators.IdentityOperator(length(u)) : _Pl
-    Pr = _Pr === nothing ? SciMLOperators.IdentityOperator(length(u)) : _Pr
-    Pl, Pr
+function wrapprecs(linsolver, W, weight)
+    if isnothing(linsolver)
+        linsolver = LinearSolve.defaultalg(W, weight, LinearSolve.OperatorAssumptions(true))
+    end
+    if hasproperty(linsolver, :precs) && isnothing(linsolver.precs)
+        Pl = LinearSolve.InvPreconditioner(Diagonal(_vec(weight)))
+        Pr = Diagonal(_vec(weight))
+        precs = Returns((Pl, Pr))
+        return remake(linsolver; precs)
+    else
+        return linsolver
+    end
 end
 
 Base.resize!(p::LinearSolve.LinearCache, i) = p