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solve.jl
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## Common Interface Solve Functions
function DiffEqBase.__solve(
prob::Union{DiffEqBase.AbstractODEProblem, DiffEqBase.AbstractDAEProblem},
alg::algType,
timeseries = [],
ts = [],
ks = [],
recompile::Type{Val{recompile_flag}} = Val{true};
kwargs...,
) where {algType <: Union{SundialsODEAlgorithm, SundialsDAEAlgorithm}, recompile_flag}
integrator = DiffEqBase.__init(prob, alg, timeseries, ts, ks; kwargs...)
if integrator.sol.retcode == :Default
solve!(integrator)
end
integrator.sol
end
function DiffEqBase.__init(
prob::DiffEqBase.AbstractODEProblem{uType, tupType, isinplace},
alg::SundialsODEAlgorithm{Method, LinearSolver},
timeseries = [],
ts = [],
ks = [];
verbose = true,
callback = nothing,
abstol = 1 / 10^6,
reltol = 1 / 10^3,
saveat = Float64[],
tstops = Float64[],
maxiters = Int(1e5),
dt = nothing,
dtmin = 0.0,
dtmax = 0.0,
timeseries_errors = true,
dense_errors = false,
save_everystep = isempty(saveat),
save_on = true,
save_start = save_everystep || isempty(saveat) || typeof(saveat) <: Number ? true :
prob.tspan[1] in saveat,
save_end = save_everystep || isempty(saveat) || typeof(saveat) <: Number ? true :
prob.tspan[2] in saveat,
dense = save_everystep && isempty(saveat),
progress = false,
progress_name = "ODE",
progress_message = DiffEqBase.ODE_DEFAULT_PROG_MESSAGE,
save_timeseries = nothing,
advance_to_tstop = false,
stop_at_next_tstop = false,
userdata = nothing,
alias_u0 = false,
kwargs...,
) where {uType, tupType, isinplace, Method, LinearSolver}
tType = eltype(tupType)
if verbose
warned = !isempty(kwargs) && DiffEqBase.check_keywords(alg, kwargs, warnlist)
warned && DiffEqBase.warn_compat()
end
if prob.f.mass_matrix != LinearAlgebra.I
error("This solver is not able to use mass matrices.")
end
if typeof(reltol) <: AbstractArray
error("Sundials only allows scalar reltol.")
end
progress && Logging.@logmsg(-1, progress_name, _id = _id = :Sundials, progress = 0)
callbacks_internal = DiffEqBase.CallbackSet(callback)
max_len_cb = DiffEqBase.max_vector_callback_length(callbacks_internal)
if max_len_cb isa VectorContinuousCallback
callback_cache = DiffEqBase.CallbackCache(max_len_cb.len, Float64, Float64)
else
callback_cache = nothing
end
tspan = prob.tspan
t0 = tspan[1]
tdir = sign(tspan[2] - tspan[1])
tstops_internal, saveat_internal =
tstop_saveat_disc_handling(tstops, saveat, tdir, tspan, tType)
if typeof(prob.u0) <: Number
u0 = [prob.u0]
else
if alias_u0
u0 = vec(prob.u0)
else
u0 = vec(copy(prob.u0))
end
end
sizeu = size(prob.u0)
### Fix the more general function to Sundials allowed style
if !isinplace && typeof(prob.u0) <: Number
f! = (du, u, p, t) -> (du .= prob.f(first(u), p, t); Cint(0))
elseif !isinplace && typeof(prob.u0) <: Vector{Float64}
f! = (du, u, p, t) -> (du .= prob.f(u, p, t); Cint(0))
elseif !isinplace && typeof(prob.u0) <: AbstractArray
f! = (du, u, p, t) -> (du .= vec(prob.f(reshape(u, sizeu), p, t)); Cint(0))
elseif typeof(prob.u0) <: Vector{Float64}
f! = prob.f
else # Then it's an in-place function on an abstract array
f! = (du, u, p, t) -> (prob.f(reshape(du, sizeu), reshape(u, sizeu), p, t);
du = vec(du);
0)
end
if typeof(alg) <: CVODE_BDF
alg_code = CV_BDF
elseif typeof(alg) <: CVODE_Adams
alg_code = CV_ADAMS
end
#if Method == :Newton
# method_code = CV_NEWTON
#elseif Method == :Functional
# method_code = CV_FUNCTIONAL
#end
mem_ptr = CVodeCreate(alg_code)
(mem_ptr == C_NULL) && error("Failed to allocate CVODE solver object")
mem = Handle(mem_ptr)
!verbose && CVodeSetErrHandlerFn(
mem,
@cfunction(null_error_handler, Nothing, (Cint, Char, Char, Ptr{Cvoid})),
C_NULL,
)
ures = Vector{uType}()
dures = Vector{uType}()
save_start ? ts = [t0] : ts = Float64[]
u0nv = NVector(u0)
_u0 = copy(u0)
utmp = NVector(_u0)
userfun = FunJac(
f!,
prob.f.jac,
prob.p,
nothing,
prob.f.jac_prototype,
alg.prec,
alg.psetup,
u0,
_u0,
)
function getcfunf(::T) where {T}
@cfunction(cvodefunjac, Cint, (realtype, N_Vector, N_Vector, Ref{T}))
end
flag = CVodeInit(mem, getcfunf(userfun), t0, convert(N_Vector, utmp))
dt != nothing && (flag = CVodeSetInitStep(mem, dt))
flag = CVodeSetMinStep(mem, dtmin)
flag = CVodeSetMaxStep(mem, dtmax)
flag = CVodeSetUserData(mem, userfun)
if typeof(abstol) <: Array
flag = CVodeSVtolerances(mem, reltol, abstol)
else
flag = CVodeSStolerances(mem, reltol, abstol)
end
flag = CVodeSetMaxNumSteps(mem, maxiters)
flag = CVodeSetMaxOrd(mem, alg.max_order)
flag = CVodeSetMaxHnilWarns(mem, alg.max_hnil_warns)
flag = CVodeSetStabLimDet(mem, alg.stability_limit_detect)
flag = CVodeSetMaxErrTestFails(mem, alg.max_error_test_failures)
flag = CVodeSetMaxNonlinIters(mem, alg.max_nonlinear_iters)
flag = CVodeSetMaxConvFails(mem, alg.max_convergence_failures)
nojacobian = true
if Method == :Newton # Only use a linear solver if it's a Newton-based method
if LinearSolver in (:Dense, :LapackDense)
nojacobian = false
A = SUNDenseMatrix(length(u0), length(u0))
_A = MatrixHandle(A, DenseMatrix())
if LinearSolver === :Dense
LS = SUNLinSol_Dense(u0, A)
_LS = LinSolHandle(LS, Dense())
else
LS = SUNLinSol_LapackDense(u0, A)
_LS = LinSolHandle(LS, LapackDense())
end
elseif LinearSolver in (:Band, :LapackBand)
nojacobian = false
A = SUNBandMatrix(length(u0), alg.jac_upper, alg.jac_lower)
_A = MatrixHandle(A, BandMatrix())
if LinearSolver === :Band
LS = SUNLinSol_Band(u0, A)
_LS = LinSolHandle(LS, Band())
else
LS = SUNLinSol_LapackBand(u0, A)
_LS = LinSolHandle(LS, LapackBand())
end
elseif LinearSolver == :Diagonal
nojacobian = false
flag = CVDiag(mem)
_A = nothing
_LS = nothing
elseif LinearSolver == :GMRES
LS = SUNLinSol_SPGMR(u0, alg.prec_side, alg.krylov_dim)
_A = nothing
_LS = Sundials.LinSolHandle(LS, Sundials.SPGMR())
elseif LinearSolver == :FGMRES
LS = SUNLinSol_SPFGMR(u0, alg.prec_side, alg.krylov_dim)
_A = nothing
_LS = LinSolHandle(LS, SPFGMR())
elseif LinearSolver == :BCG
LS = SUNLinSol_SPBCGS(u0, alg.prec_side, alg.krylov_dim)
_A = nothing
_LS = LinSolHandle(LS, SPBCGS())
elseif LinearSolver == :PCG
LS = SUNLinSol_PCG(u0, alg.prec_side, alg.krylov_dim)
_A = nothing
_LS = LinSolHandle(LS, PCG())
elseif LinearSolver == :TFQMR
LS = SUNLinSol_SPTFQMR(u0, alg.prec_side, alg.krylov_dim)
_A = nothing
_LS = LinSolHandle(LS, PTFQMR())
elseif LinearSolver == :KLU
nojacobian = false
nnz = length(SparseArrays.nonzeros(prob.f.jac_prototype))
A = SUNSparseMatrix(length(u0), length(u0), nnz, CSC_MAT)
LS = SUNLinSol_KLU(u0, A)
_A = MatrixHandle(A, SparseMatrix())
_LS = LinSolHandle(LS, KLU())
end
if LinearSolver !== :Diagonal
flag = CVodeSetLinearSolver(mem, LS, _A === nothing ? C_NULL : A)
end
NLS = SUNNonlinSol_Newton(u0)
else
_A = nothing
_LS = nothing
# TODO: Anderson Acceleration
anderson_m = 0
NLS = SUNNonlinSol_FixedPoint(u0, anderson_m)
end
CVodeSetNonlinearSolver(mem, NLS)
if DiffEqBase.has_jac(prob.f) && Method == :Newton
function getcfunjac(::T) where {T}
@cfunction(
cvodejac,
Cint,
(
realtype,
N_Vector,
N_Vector,
SUNMatrix,
Ref{T},
N_Vector,
N_Vector,
N_Vector,
)
)
end
jac = getcfunjac(userfun)
flag = CVodeSetUserData(mem, userfun)
nojacobian || (flag = CVodeSetJacFn(mem, jac))
else
jac = nothing
end
if typeof(prob.f.jac_prototype) <: DiffEqBase.AbstractDiffEqLinearOperator
function getcfunjtimes(::T) where {T}
@cfunction(
jactimes,
Cint,
(N_Vector, N_Vector, realtype, N_Vector, N_Vector, Ref{T}, N_Vector)
)
end
jtimes = getcfunjtimes(userfun)
CVodeSetJacTimes(mem, C_NULL, jtimes)
end
if alg.prec !== nothing
function getpercfun(::T) where {T}
@cfunction(
precsolve,
Cint,
(
Float64,
N_Vector,
N_Vector,
N_Vector,
N_Vector,
Float64,
Float64,
Int,
Ref{T},
)
)
end
precfun = getpercfun(userfun)
function getpsetupfun(::T) where {T}
@cfunction(
precsetup,
Cint,
(Float64, N_Vector, N_Vector, Int, Ptr{Int}, Float64, Ref{T})
)
end
psetupfun = alg.psetup === nothing ? C_NULL : getpsetupfun(userfun)
CVodeSetPreconditioner(mem, psetupfun, precfun)
end
callbacks_internal == nothing ? tmp = nothing : tmp = similar(u0)
callbacks_internal == nothing ? uprev = nothing : uprev = similar(u0)
tout = [tspan[1]]
if save_start
save_value!(ures, u0, uType, sizeu)
if dense
f!(_u0, u0, prob.p, tspan[1])
save_value!(dures, utmp, uType, sizeu)
end
end
sol = DiffEqBase.build_solution(
prob,
alg,
ts,
ures,
dense = dense,
interp = dense ? DiffEqBase.HermiteInterpolation(ts, ures, dures) :
DiffEqBase.LinearInterpolation(ts, ures),
timeseries_errors = timeseries_errors,
destats = DiffEqBase.DEStats(0),
calculate_error = false,
)
opts = DEOptions(
saveat_internal,
tstops_internal,
save_everystep,
dense,
timeseries_errors,
dense_errors,
save_on,
save_end,
callbacks_internal,
abstol,
reltol,
verbose,
advance_to_tstop,
stop_at_next_tstop,
progress,
progress_name,
progress_message,
maxiters,
)
integrator = CVODEIntegrator(
u0,
prob.p,
t0,
t0,
mem,
_LS,
_A,
sol,
alg,
f!,
userfun,
jac,
opts,
tout,
tdir,
sizeu,
false,
tmp,
uprev,
Cint(flag),
false,
0,
1,
callback_cache,
0.0,
)
initialize_callbacks!(integrator)
integrator
end # function solve
function DiffEqBase.__init(
prob::DiffEqBase.AbstractODEProblem{uType, tupType, isinplace},
alg::ARKODE{Method, LinearSolver, MassLinearSolver},
timeseries = [],
ts = [],
ks = [];
verbose = true,
callback = nothing,
abstol = 1 / 10^6,
reltol = 1 / 10^3,
saveat = Float64[],
tstops = Float64[],
maxiters = Int(1e5),
dt = nothing,
dtmin = 0.0,
dtmax = 0.0,
timeseries_errors = true,
dense_errors = false,
save_everystep = isempty(saveat),
dense = save_everystep,
save_on = true,
save_start = true,
save_end = true,
save_timeseries = nothing,
progress = false,
progress_name = "ODE",
progress_message = DiffEqBase.ODE_DEFAULT_PROG_MESSAGE,
advance_to_tstop = false,
stop_at_next_tstop = false,
userdata = nothing,
alias_u0 = false,
kwargs...,
) where {uType, tupType, isinplace, Method, LinearSolver, MassLinearSolver}
tType = eltype(tupType)
if verbose
warned = !isempty(kwargs) && DiffEqBase.check_keywords(alg, kwargs, warnlist)
warned && DiffEqBase.warn_compat()
end
if typeof(reltol) <: AbstractArray
error("Sundials only allows scalar reltol.")
end
progress && Logging.@logmsg(-1, progress_name, _id = _id = :Sundials, progress = 0)
callbacks_internal = DiffEqBase.CallbackSet(callback)
max_len_cb = DiffEqBase.max_vector_callback_length(callbacks_internal)
if max_len_cb isa VectorContinuousCallback
callback_cache = DiffEqBase.CallbackCache(max_len_cb.len, Float64, Float64)
else
callback_cache = nothing
end
tspan = prob.tspan
t0 = tspan[1]
tdir = sign(tspan[2] - tspan[1])
tstops_internal, saveat_internal =
tstop_saveat_disc_handling(tstops, saveat, tdir, tspan, tType)
if typeof(prob.u0) <: Number
u0 = [prob.u0]
else
if alias_u0
u0 = vec(prob.u0)
else
u0 = vec(copy(prob.u0))
end
end
sizeu = size(prob.u0)
ures = Vector{uType}()
dures = Vector{uType}()
save_start ? ts = [t0] : ts = Float64[]
u0nv = NVector(u0)
_u0 = copy(u0)
utmp = NVector(_u0)
function arkodemem(; fe = C_NULL, fi = C_NULL, t0 = t0, u0 = convert(N_Vector, u0nv))
mem_ptr = ARKStepCreate(fe, fi, t0, u0)
(mem_ptr == C_NULL) && error("Failed to allocate ARKODE solver object")
mem = Handle(mem_ptr)
!verbose && ARKStepSetErrHandlerFn(
mem,
@cfunction(null_error_handler, Nothing, (Cint, Char, Char, Ptr{Cvoid})),
C_NULL,
)
return mem
end
### Fix the more general function to Sundials allowed style
if !isinplace && typeof(prob.u0) <: Number
f! = (du, u, p, t) -> (du .= prob.f(first(u), p, t); Cint(0))
elseif !isinplace && typeof(prob.u0) <: Vector{Float64}
f! = (du, u, p, t) -> (du .= prob.f(u, p, t); Cint(0))
elseif !isinplace && typeof(prob.u0) <: AbstractArray
f! = (du, u, p, t) -> (du .= vec(prob.f(reshape(u, sizeu), p, t)); Cint(0))
elseif typeof(prob.u0) <: Vector{Float64}
f! = prob.f
else # Then it's an in-place function on an abstract array
f! = (du, u, p, t) -> (prob.f(reshape(du, sizeu), reshape(u, sizeu), p, t);
du = vec(du);
Cint(0))
end
if typeof(prob.problem_type) <: SplitODEProblem
### Fix the more general function to Sundials allowed style
if !isinplace && typeof(prob.u0) <: Number
f1! = (du, u, p, t) -> (du .= prob.f.f1(first(u), p, t); Cint(0))
f2! = (du, u, p, t) -> (du .= prob.f.f2(first(u), p, t); Cint(0))
elseif !isinplace && typeof(prob.u0) <: Vector{Float64}
f1! = (du, u, p, t) -> (du .= prob.f.f1(u, p, t); Cint(0))
f2! = (du, u, p, t) -> (du .= prob.f.f2(u, p, t); Cint(0))
elseif !isinplace && typeof(prob.u0) <: AbstractArray
f1! = (du, u, p, t) -> (du .= vec(prob.f.f1(reshape(u, sizeu), p, t)); Cint(0))
f2! = (du, u, p, t) -> (du .= vec(prob.f.f2(reshape(u, sizeu), p, t)); Cint(0))
elseif typeof(prob.u0) <: Vector{Float64}
f1! = prob.f.f1
f2! = prob.f.f2
else # Then it's an in-place function on an abstract array
f1! =
(du, u, p, t) -> (prob.f.f1(reshape(du, sizeu), reshape(u, sizeu), p, t);
du = vec(du);
Cint(0))
f2! =
(du, u, p, t) -> (prob.f.f2(reshape(du, sizeu), reshape(u, sizeu), p, t);
du = vec(du);
Cint(0))
end
userfun = FunJac(
f1!,
f2!,
prob.f.f1.jac,
prob.p,
prob.f.mass_matrix,
prob.f.f1.jac_prototype,
alg.prec,
alg.psetup,
u0,
_u0,
nothing,
)
function getcfunjac(::T) where {T}
@cfunction(cvodefunjac, Cint, (realtype, N_Vector, N_Vector, Ref{T}))
end
function getcfunjac2(::T) where {T}
@cfunction(cvodefunjac2, Cint, (realtype, N_Vector, N_Vector, Ref{T}))
end
cfj1 = getcfunjac(userfun)
cfj2 = getcfunjac2(userfun)
mem = arkodemem(fi = cfj1, fe = cfj2)
else
userfun = FunJac(
f!,
prob.f.jac,
prob.p,
prob.f.mass_matrix,
prob.f.jac_prototype,
alg.prec,
alg.psetup,
u0,
_u0,
)
if alg.stiffness == Explicit()
function getcfun1(::T) where {T}
@cfunction(cvodefunjac, Cint, (realtype, N_Vector, N_Vector, Ref{T}))
end
cfj1 = getcfun1(userfun)
mem = arkodemem(fe = cfj1)
elseif alg.stiffness == Implicit()
function getcfun2(::T) where {T}
@cfunction(cvodefunjac, Cint, (realtype, N_Vector, N_Vector, Ref{T}))
end
cfj2 = getcfun2(userfun)
mem = arkodemem(fi = cfj2)
end
end
dt != nothing && (flag = ARKStepSetInitStep(mem, dt))
flag = ARKStepSetMinStep(mem, dtmin)
flag = ARKStepSetMaxStep(mem, dtmax)
flag = ARKStepSetUserData(mem, userfun)
if typeof(abstol) <: Array
flag = ARKStepSVtolerances(mem, reltol, abstol)
else
flag = ARKStepSStolerances(mem, reltol, abstol)
end
flag = ARKStepSetMaxNumSteps(mem, maxiters)
flag = ARKStepSetMaxHnilWarns(mem, alg.max_hnil_warns)
flag = ARKStepSetMaxErrTestFails(mem, alg.max_error_test_failures)
flag = ARKStepSetMaxNonlinIters(mem, alg.max_nonlinear_iters)
flag = ARKStepSetMaxConvFails(mem, alg.max_convergence_failures)
flag = ARKStepSetPredictorMethod(mem, alg.predictor_method)
flag = ARKStepSetNonlinConvCoef(mem, alg.nonlinear_convergence_coefficient)
flag = ARKStepSetDenseOrder(mem, alg.dense_order)
#=
Reference from Manual on ARKODE
To choose an explicit table, set itable to a negative value. This automatically calls ARKStepSetExplicit(). However, if the problem is posed in explicit form, i.e. 𝑦 ̇ = 𝑓 (𝑡, 𝑦), then we recommend that the ERKStep time- stepper module be used instead of ARKStep.
To select an implicit table, set etable to a negative value. This automatically calls ARKStepSetImplicit(). If both itable and etable are non-negative, then these should match an existing implicit/explicit pair, listed in the section Additive Butcher tables. This automatically calls ARKStepSetImEx().
=#
if alg.itable == nothing && alg.etable == nothing
flag = ARKStepSetOrder(mem, alg.order)
elseif alg.itable == nothing && alg.etable != nothing
flag = ARKStepSetTableNum(mem, -1, alg.etable)
elseif alg.itable != nothing && alg.etable == nothing
flag = ARKStepSetTableNum(mem, alg.itable, -1)
else
flag = ARKStepSetTableNum(mem, alg.itable, alg.etable)
end
flag = ARKStepSetNonlinCRDown(mem, alg.crdown)
flag = ARKStepSetNonlinRDiv(mem, alg.rdiv)
flag = ARKStepSetDeltaGammaMax(mem, alg.dgmax)
flag = ARKStepSetMaxStepsBetweenLSet(mem, alg.msbp)
#flag = ARKStepSetAdaptivityMethod(mem,alg.adaptivity_method,1,0)
#flag = ARKStepSetFixedStep(mem,)
alg.set_optimal_params && (flag = ARKStepSetOptimalParams(mem))
if Method == :Newton # Only use a linear solver if it's a Newton-based method
if LinearSolver in (:Dense, :LapackDense)
nojacobian = false
A = SUNDenseMatrix(length(u0), length(u0))
_A = MatrixHandle(A, DenseMatrix())
if LinearSolver === :Dense
LS = SUNLinSol_Dense(u0, A)
_LS = LinSolHandle(LS, Dense())
else
LS = SUNLinSol_LapackDense(u0, A)
_LS = LinSolHandle(LS, LapackDense())
end
elseif LinearSolver in (:Band, :LapackBand)
nojacobian = false
A = SUNBandMatrix(length(u0), alg.jac_upper, alg.jac_lower)
_A = MatrixHandle(A, BandMatrix())
if LinearSolver === :Band
LS = SUNLinSol_Band(u0, A)
_LS = LinSolHandle(LS, Band())
else
LS = SUNLinSol_LapackBand(u0, A)
_LS = LinSolHandle(LS, LapackBand())
end
elseif LinearSolver == :GMRES
LS = SUNLinSol_SPGMR(u0, alg.prec_side, alg.krylov_dim)
_A = nothing
_LS = Sundials.LinSolHandle(LS, Sundials.SPGMR())
elseif LinearSolver == :FGMRES
LS = SUNLinSol_SPFGMR(u0, alg.prec_side, alg.krylov_dim)
_A = nothing
_LS = LinSolHandle(LS, SPFGMR())
elseif LinearSolver == :BCG
LS = SUNLinSol_SPBCGS(u0, alg.prec_side, alg.krylov_dim)
_A = nothing
_LS = LinSolHandle(LS, SPBCGS())
elseif LinearSolver == :PCG
LS = SUNLinSol_PCG(u0, alg.prec_side, alg.krylov_dim)
_A = nothing
_LS = LinSolHandle(LS, PCG())
elseif LinearSolver == :TFQMR
LS = SUNLinSol_SPTFQMR(u0, alg.prec_side, alg.krylov_dim)
_A = nothing
_LS = LinSolHandle(LS, PTFQMR())
elseif LinearSolver == :KLU
nnz = length(SparseArrays.nonzeros(prob.f.jac_prototype))
A = SUNSparseMatrix(length(u0), length(u0), nnz, CSC_MAT)
LS = SUNLinSol_KLU(u0, A)
_A = MatrixHandle(A, SparseMatrix())
_LS = LinSolHandle(LS, KLU())
end
flag = ARKStepSetLinearSolver(mem, LS, _A === nothing ? C_NULL : A)
elseif Method == :Functional
ARKStepSetFixedPoint(mem, Clong(alg.krylov_dim))
else
_A = nothing
_LS = nothing
end
if (
typeof(prob.problem_type) <: SplitODEProblem &&
typeof(prob.f.f1.jac_prototype) <: DiffEqBase.AbstractDiffEqLinearOperator
) || (
!(typeof(prob.problem_type) <: SplitODEProblem) &&
typeof(prob.f.jac_prototype) <: DiffEqBase.AbstractDiffEqLinearOperator
)
function getcfunjtimes(::T) where {T}
@cfunction(
jactimes,
Cint,
(N_Vector, N_Vector, realtype, N_Vector, N_Vector, Ref{T}, N_Vector)
)
end
jtimes = getcfunjtimes(userfun)
ARKStepSetJacTimes(mem, C_NULL, jtimes)
end
if prob.f.mass_matrix != LinearAlgebra.I
if MassLinearSolver in (:Dense, :LapackDense)
nojacobian = false
M = SUNDenseMatrix(length(u0), length(u0))
_M = MatrixHandle(M, DenseMatrix())
if MassLinearSolver === :Dense
MLS = SUNLinSol_Dense(u0, M)
_MLS = LinSolHandle(MLS, Dense())
else
MLS = SUNLinSol_LapackDense(u0, M)
_MLS = LinSolHandle(MLS, LapackDense())
end
elseif MassLinearSolver in (:Band, :LapackBand)
nojacobian = false
M = SUNBandMatrix(length(u0), alg.jac_upper, alg.jac_lower)
_M = MatrixHandle(M, BandMatrix())
if MassLinearSolver === :Band
MLS = SUNLinSol_Band(u0, M)
_MLS = LinSolHandle(MLS, Band())
else
MLS = SUNLinSol_LapackBand(u0, M)
_MLS = LinSolHandle(MLS, LapackBand())
end
elseif MassLinearSolver == :GMRES
MLS = SUNLinSol_SPGMR(u0, alg.prec_side, alg.mass_krylov_dim)
_M = nothing
_MLS = LinSolHandle(MLS, SPGMR())
elseif MassLinearSolver == :FGMRES
MLS = SUNLinSol_SPGMR(u0, alg.prec_side, alg.mass_krylov_dim)
_M = nothing
_MLS = LinSolHandle(MLS, SPFGMR())
elseif MassLinearSolver == :BCG
MLS = SUNLinSol_SPGMR(u0, alg.prec_side, alg.mass_krylov_dim)
_M = nothing
_MLS = LinSolHandle(MLS, SPBCGS())
elseif MassLinearSolver == :PCG
MLS = SUNLinSol_SPGMR(u0, alg.prec_side, alg.mass_krylov_dim)
_M = nothing
_MLS = LinSolHandle(MLS, PCG())
elseif MassLinearSolver == :TFQMR
MLS = SUNLinSol_SPGMR(u0, alg.prec_side, alg.mass_krylov_dim)
_M = nothing
_MLS = LinSolHandle(MLS, PTFQMR())
elseif MassLinearSolver == :KLU
nnz = length(SparseArrays.nonzeros(prob.f.mass_matrix))
M = SUNSparseMatrix(length(u0), length(u0), nnz, CSC_MAT)
MLS = SUNLinSol_KLU(u0, M)
_M = MatrixHandle(M, SparseMatrix())
_MLS = LinSolHandle(MLS, KLU())
end
flag = ARKStepSetMassLinearSolver(mem, MLS, _M === nothing ? C_NULL : M, false)
function getmatfun(::T) where {T}
@cfunction(
massmat,
Cint,
(realtype, SUNMatrix, Ref{T}, N_Vector, N_Vector, N_Vector)
)
end
matfun = getmatfun(userfun)
ARKStepSetMassFn(mem, matfun)
else
_M = nothing
_MLS = nothing
end
if DiffEqBase.has_jac(prob.f)
function getfunjac(::T) where {T}
@cfunction(
cvodejac,
Cint,
(
realtype,
N_Vector,
N_Vector,
SUNMatrix,
Ref{T},
N_Vector,
N_Vector,
N_Vector,
)
)
end
jac = getfunjac(userfun)
flag = ARKStepSetUserData(mem, userfun)
flag = ARKStepSetJacFn(mem, jac)
else
jac = nothing
end
if alg.prec !== nothing
function getpercfun(::T) where {T}
@cfunction(
precsolve,
Cint,
(
Float64,
N_Vector,
N_Vector,
N_Vector,
N_Vector,
Float64,
Float64,
Int,
Ref{T},
)
)
end
precfun = getpercfun(userfun)
function getpsetupfun(::T) where {T}
@cfunction(
precsetup,
Cint,
(Float64, N_Vector, N_Vector, Int, Ptr{Int}, Float64, Ref{T})
)
end
psetupfun = alg.psetup === nothing ? C_NULL : getpsetupfun(userfun)
ARKStepSetPreconditioner(mem, psetupfun, precfun)
end
callbacks_internal == nothing ? tmp = nothing : tmp = similar(u0)
callbacks_internal == nothing ? uprev = nothing : uprev = similar(u0)
tout = [tspan[1]]
if save_start
save_value!(ures, u0, uType, sizeu)
if dense
f!(_u0, u0, prob.p, tspan[1])
save_value!(dures, utmp, uType, sizeu)
end
end
sol = DiffEqBase.build_solution(
prob,
alg,
ts,
ures,
dense = dense,
interp = dense ? DiffEqBase.HermiteInterpolation(ts, ures, dures) :
DiffEqBase.LinearInterpolation(ts, ures),
timeseries_errors = timeseries_errors,
destats = DiffEqBase.DEStats(0),
calculate_error = false,
)
opts = DEOptions(
saveat_internal,
tstops_internal,
save_everystep,
dense,
timeseries_errors,
dense_errors,
save_on,
save_end,
callbacks_internal,
abstol,
reltol,
verbose,
advance_to_tstop,
stop_at_next_tstop,
progress,
progress_name,
progress_message,
maxiters,
)
integrator = ARKODEIntegrator(
utmp,
prob.p,
t0,
t0,
mem,
_LS,
_A,
_MLS,
_M,
sol,
alg,
f!,
userfun,
jac,
opts,
tout,
tdir,
sizeu,
false,
tmp,
uprev,
Cint(flag),
false,
0,
1,
callback_cache,
0.0,
)
initialize_callbacks!(integrator)
integrator
end # function solve
function tstop_saveat_disc_handling(tstops, saveat, tdir, tspan, tType)
if isempty(tstops) # TODO: Specialize more
tstops_vec = [tspan[2]]
else
tstops_vec = vec(collect(
tType,
Iterators.filter(
x -> tdir * tspan[1] < tdir * x ≤ tdir * tspan[end],
Iterators.flatten((tstops, tspan[end])),
),
))
end
if tdir > 0
tstops_internal = DataStructures.BinaryMinHeap(tstops_vec)
else
tstops_internal = DataStructures.BinaryMaxHeap(tstops_vec)
end
if typeof(saveat) <: Number
if (tspan[1]:saveat:tspan[end])[end] == tspan[end]
saveat_vec = convert(
Vector{tType},
collect(tType, (tspan[1] + saveat):saveat:tspan[end]),
)
else
saveat_vec = convert(
Vector{tType},
collect(tType, (tspan[1] + saveat):saveat:(tspan[end] - saveat)),
)
end
elseif isempty(saveat)
saveat_vec = saveat
else
saveat_vec = vec(collect(
tType,
Iterators.filter(x -> tdir * tspan[1] < tdir * x < tdir * tspan[end], saveat),
))
end
if tdir > 0
saveat_internal = DataStructures.BinaryMinHeap(saveat_vec)
else
saveat_internal = DataStructures.BinaryMaxHeap(saveat_vec)
end
tstops_internal, saveat_internal
end
## Solve for DAEs uses IDA
function DiffEqBase.__init(
prob::DiffEqBase.AbstractDAEProblem{uType, duType, tupType, isinplace},
alg::SundialsDAEAlgorithm{LinearSolver},
timeseries = [],
ts = [],
ks = [];
verbose = true,
dt = nothing,
dtmax = 0.0,
save_on = true,
save_start = true,
callback = nothing,
abstol = 1 / 10^6,
reltol = 1 / 10^3,
saveat = Float64[],
tstops = Float64[],
maxiters = Int(1e5),
timeseries_errors = true,
dense_errors = false,
save_everystep = isempty(saveat),
dense = save_everystep,
save_timeseries = nothing,
save_end = true,
progress = false,
progress_name = "ODE",
progress_message = DiffEqBase.ODE_DEFAULT_PROG_MESSAGE,
advance_to_tstop = false,
stop_at_next_tstop = false,
userdata = nothing,
kwargs...,
) where {uType, duType, tupType, isinplace, LinearSolver}
tType = eltype(tupType)
if verbose
warned = !isempty(kwargs) && DiffEqBase.check_keywords(alg, kwargs, warnida)
warned && DiffEqBase.warn_compat()
end
if typeof(reltol) <: AbstractArray
error("Sundials only allows scalar reltol.")
end
progress && Logging.@logmsg(-1, progress_name, _id = _id = :Sundials, progress = 0)
callbacks_internal = DiffEqBase.CallbackSet(callback)