abstract type FIRKMutableCache <: OrdinaryDiffEqMutableCache end
get_fsalfirstlast(cache::FIRKMutableCache, u) = (cache.fsalfirst, cache.k)

mutable struct RadauIIA3ConstantCache{F, Tab, Tol, Dt, U, JType} <:
    OrdinaryDiffEqConstantCache
    uf::F
    tab::Tab
    κ::Tol
    ηold::Tol
    iter::Int
    cont1::U
    cont2::U
    dtprev::Dt
    W_γdt::Dt
    status::NLStatus
    J::JType
end

function alg_cache(
        alg::RadauIIA3, u, rate_prototype, ::Type{uEltypeNoUnits},
        ::Type{uBottomEltypeNoUnits},
        ::Type{tTypeNoUnits}, uprev, uprev2, f, t, dt, reltol, p, calck,
        ::Val{false}, verbose
    ) where {uEltypeNoUnits, uBottomEltypeNoUnits, tTypeNoUnits}
    uf = UDerivativeWrapper(f, t, p)
    uToltype = constvalue(uBottomEltypeNoUnits)
    tab = RadauIIA3Tableau(uToltype, constvalue(tTypeNoUnits))

    κ = convert(uToltype, 1 // 100)
    J = false .* _vec(rate_prototype) .* _vec(rate_prototype)'

    return RadauIIA3ConstantCache(
        uf, tab, κ, one(uToltype), 10000, u, u, dt, dt,
        Convergence, J
    )
end

mutable struct RadauIIA3Cache{
        uType, cuType, uNoUnitsType, rateType, JType, W1Type, UF, JC,
        F1, Tab, Tol, Dt, rTol, aTol, StepLimiter,
    } <: FIRKMutableCache
    u::uType
    uprev::uType
    z1::uType
    z2::uType
    w1::uType
    w2::uType
    dw12::cuType
    cubuff::cuType
    du1::rateType
    fsalfirst::rateType
    k::rateType
    k2::rateType
    fw1::rateType
    fw2::rateType
    J::JType
    W1::W1Type
    uf::UF
    tab::Tab
    κ::Tol
    ηold::Tol
    iter::Int
    tmp::uType
    atmp::uNoUnitsType
    jac_config::JC
    linsolve::F1
    rtol::rTol
    atol::aTol
    dtprev::Dt
    W_γdt::Dt
    status::NLStatus
    step_limiter!::StepLimiter
end

function alg_cache(
        alg::RadauIIA3, u, rate_prototype, ::Type{uEltypeNoUnits},
        ::Type{uBottomEltypeNoUnits},
        ::Type{tTypeNoUnits}, uprev, uprev2, f, t, dt, reltol, p, calck,
        ::Val{true}, verbose
    ) where {uEltypeNoUnits, uBottomEltypeNoUnits, tTypeNoUnits}
    uf = UJacobianWrapper(f, t, p)
    uToltype = constvalue(uBottomEltypeNoUnits)
    tab = RadauIIA3Tableau(uToltype, constvalue(tTypeNoUnits))

    κ = alg.κ !== nothing ? convert(uToltype, alg.κ) : convert(uToltype, 1 // 100)

    z1 = zero(u)
    z2 = zero(u)
    w1 = zero(u)
    w2 = zero(u)
    dw12 = similar(u, Complex{eltype(u)})
    recursivefill!(dw12, false)
    cubuff = similar(u, Complex{eltype(u)})
    recursivefill!(cubuff, false)

    fsalfirst = zero(rate_prototype)
    k = zero(rate_prototype)
    k2 = zero(rate_prototype)
    fw1 = zero(rate_prototype)
    fw2 = zero(rate_prototype)

    du1 = zero(rate_prototype)

    tmp = zero(u)
    atmp = similar(u, uEltypeNoUnits)
    recursivefill!(atmp, false)
    jac_config = build_jac_config(alg, f, uf, du1, uprev, u, tmp, dw12)

    J, W1 = build_J_W(alg, u, uprev, p, t, dt, f, jac_config, uEltypeNoUnits, Val(true))
    W1 = similar(J, Complex{eltype(W1)})
    recursivefill!(W1, false)

    linprob = LinearProblem(W1, _vec(cubuff); u0 = _vec(dw12))
    linsolve = init(
        linprob, alg.linsolve, alias = LinearAliasSpecifier(alias_A = true, alias_b = true),
        abstol = reltol, reltol = reltol,
        assumptions = LinearSolve.OperatorAssumptions(true), verbose = verbose.linear_verbosity
    )
    #Pl = LinearSolve.InvPreconditioner(Diagonal(_vec(weight))),
    #Pr = Diagonal(_vec(weight)))

    rtol = reltol isa Number ? reltol : zero(reltol)
    atol = reltol isa Number ? reltol : zero(reltol)

    return RadauIIA3Cache(
        u, uprev,
        z1, z2, w1, w2,
        dw12, cubuff,
        du1, fsalfirst, k, k2, fw1, fw2,
        J, W1,
        uf, tab, κ, one(uToltype), 10000,
        tmp, atmp, jac_config, linsolve, rtol, atol, dt, dt,
        Convergence, alg.step_limiter!
    )
end

mutable struct RadauIIA5ConstantCache{F, Tab, Tol, Dt, U, JType} <:
    OrdinaryDiffEqConstantCache
    uf::F
    tab::Tab
    κ::Tol
    ηold::Tol
    iter::Int
    cont1::U
    cont2::U
    cont3::U
    dtprev::Dt
    W_γdt::Dt
    status::NLStatus
    J::JType
end

function alg_cache(
        alg::RadauIIA5, u, rate_prototype, ::Type{uEltypeNoUnits},
        ::Type{uBottomEltypeNoUnits},
        ::Type{tTypeNoUnits}, uprev, uprev2, f, t, dt, reltol, p, calck,
        ::Val{false}, verbose
    ) where {uEltypeNoUnits, uBottomEltypeNoUnits, tTypeNoUnits}
    uf = UDerivativeWrapper(f, t, p)
    uToltype = constvalue(uBottomEltypeNoUnits)
    tab = RadauIIA5Tableau(uToltype, constvalue(tTypeNoUnits))

    κ = alg.κ !== nothing ? convert(uToltype, alg.κ) : convert(uToltype, 1 // 100)
    J = false .* _vec(rate_prototype) .* _vec(rate_prototype)'

    return RadauIIA5ConstantCache(
        uf, tab, κ, one(uToltype), 10000, u, u, u, dt, dt,
        Convergence, J
    )
end

mutable struct RadauIIA5Cache{
        uType, cuType, uNoUnitsType, rateType, JType, W1Type, W2Type,
        UF, JC, F1, F2, Tab, Tol, Dt, rTol, aTol, StepLimiter,
    } <:
    FIRKMutableCache
    u::uType
    uprev::uType
    z1::uType
    z2::uType
    z3::uType
    w1::uType
    w2::uType
    w3::uType
    dw1::uType
    ubuff::uType
    dw23::cuType
    cubuff::cuType
    du1::rateType
    fsalfirst::rateType
    k::rateType
    k1::rateType
    k2::rateType
    k3::rateType
    fw1::rateType
    fw2::rateType
    fw3::rateType
    J::JType
    W1::W1Type
    W2::W2Type # complex
    uf::UF
    tab::Tab
    κ::Tol
    ηold::Tol
    iter::Int
    tmp::uType
    atmp::uNoUnitsType
    jac_config::JC
    linsolve1::F1
    linsolve2::F2
    rtol::rTol
    atol::aTol
    dtprev::Dt
    W_γdt::Dt
    status::NLStatus
    step_limiter!::StepLimiter
end

function alg_cache(
        alg::RadauIIA5, u, rate_prototype, ::Type{uEltypeNoUnits},
        ::Type{uBottomEltypeNoUnits},
        ::Type{tTypeNoUnits}, uprev, uprev2, f, t, dt, reltol, p, calck,
        ::Val{true}, verbose
    ) where {uEltypeNoUnits, uBottomEltypeNoUnits, tTypeNoUnits}
    uf = UJacobianWrapper(f, t, p)
    uToltype = constvalue(uBottomEltypeNoUnits)
    tab = RadauIIA5Tableau(uToltype, constvalue(tTypeNoUnits))

    κ = alg.κ !== nothing ? convert(uToltype, alg.κ) : convert(uToltype, 1 // 100)

    z1 = zero(u)
    z2 = zero(u)
    z3 = zero(u)
    w1 = zero(u)
    w2 = zero(u)
    w3 = zero(u)
    dw1 = zero(u)
    ubuff = zero(u)
    dw23 = similar(u, Complex{eltype(u)})
    recursivefill!(dw23, false)
    cubuff = similar(u, Complex{eltype(u)})
    recursivefill!(cubuff, false)

    fsalfirst = zero(rate_prototype)
    k = zero(rate_prototype)
    k1 = zero(rate_prototype)
    k2 = zero(rate_prototype)
    k3 = zero(rate_prototype)
    fw1 = zero(rate_prototype)
    fw2 = zero(rate_prototype)
    fw3 = zero(rate_prototype)

    du1 = zero(rate_prototype)

    tmp = zero(u)
    atmp = similar(u, uEltypeNoUnits)
    recursivefill!(atmp, false)
    jac_config = build_jac_config(alg, f, uf, du1, uprev, u, tmp, dw1)

    J, W1 = build_J_W(alg, u, uprev, p, t, dt, f, jac_config, uEltypeNoUnits, Val(true))
    if J isa AbstractSciMLOperator
        error("Non-concrete Jacobian not yet supported by RadauIIA5.")
    end
    W2 = similar(J, Complex{eltype(W1)})
    recursivefill!(W2, false)

    linprob = LinearProblem(W1, _vec(ubuff); u0 = _vec(dw1))
    linsolve1 = init(
        linprob, alg.linsolve, alias = LinearAliasSpecifier(alias_A = true, alias_b = true),
        assumptions = LinearSolve.OperatorAssumptions(true), verbose = verbose.linear_verbosity
    )
    #Pl = LinearSolve.InvPreconditioner(Diagonal(_vec(weight))),
    #Pr = Diagonal(_vec(weight)))
    linprob = LinearProblem(W2, _vec(cubuff); u0 = _vec(dw23))
    linsolve2 = init(
        linprob, alg.linsolve, alias = LinearAliasSpecifier(alias_A = true, alias_b = true),
        assumptions = LinearSolve.OperatorAssumptions(true), verbose = verbose.linear_verbosity
    )
    #Pl = LinearSolve.InvPreconditioner(Diagonal(_vec(weight))),
    #Pr = Diagonal(_vec(weight)))

    rtol = reltol isa Number ? reltol : zero(reltol)
    atol = reltol isa Number ? reltol : zero(reltol)

    return RadauIIA5Cache(
        u, uprev,
        z1, z2, z3, w1, w2, w3,
        dw1, ubuff, dw23, cubuff,
        du1, fsalfirst, k, k1, k2, k3, fw1, fw2, fw3,
        J, W1, W2,
        uf, tab, κ, one(uToltype), 10000,
        tmp, atmp, jac_config, linsolve1, linsolve2, rtol, atol, dt, dt,
        Convergence, alg.step_limiter!
    )
end

mutable struct RadauIIA9ConstantCache{F, Tab, Tol, Dt, U, JType} <:
    OrdinaryDiffEqConstantCache
    uf::F
    tab::Tab
    κ::Tol
    ηold::Tol
    iter::Int
    cont1::U
    cont2::U
    cont3::U
    cont4::U
    cont5::U
    dtprev::Dt
    W_γdt::Dt
    status::NLStatus
    J::JType
end

function alg_cache(
        alg::RadauIIA9, u, rate_prototype, ::Type{uEltypeNoUnits},
        ::Type{uBottomEltypeNoUnits},
        ::Type{tTypeNoUnits}, uprev, uprev2, f, t, dt, reltol, p, calck,
        ::Val{false}, verbose
    ) where {uEltypeNoUnits, uBottomEltypeNoUnits, tTypeNoUnits}
    uf = UDerivativeWrapper(f, t, p)
    uToltype = constvalue(uBottomEltypeNoUnits)
    tab = RadauIIA9Tableau(uToltype, constvalue(tTypeNoUnits))

    κ = alg.κ !== nothing ? convert(uToltype, alg.κ) : convert(uToltype, 1 // 100)
    J = false .* _vec(rate_prototype) .* _vec(rate_prototype)'

    return RadauIIA9ConstantCache(
        uf, tab, κ, one(uToltype), 10000, u, u, u, u, u, dt, dt,
        Convergence, J
    )
end

mutable struct RadauIIA9Cache{
        uType, cuType, uNoUnitsType, rateType, JType, W1Type, W2Type,
        UF, JC, F1, F2, Tab, Tol, Dt, rTol, aTol, StepLimiter,
    } <:
    FIRKMutableCache
    u::uType
    uprev::uType
    z1::uType
    z2::uType
    z3::uType
    z4::uType
    z5::uType
    w1::uType
    w2::uType
    w3::uType
    w4::uType
    w5::uType
    dw1::uType
    ubuff::uType
    dw23::cuType
    dw45::cuType
    cubuff1::cuType
    cubuff2::cuType
    du1::rateType
    fsalfirst::rateType
    k::rateType
    k1::rateType
    k2::rateType
    k3::rateType
    k4::rateType
    k5::rateType
    fw1::rateType
    fw2::rateType
    fw3::rateType
    fw4::rateType
    fw5::rateType
    J::JType
    W1::W1Type
    W2::W2Type # complex
    W3::W2Type
    uf::UF
    tab::Tab
    κ::Tol
    ηold::Tol
    iter::Int
    tmp::uType
    tmp2::uType
    tmp3::uType
    tmp4::uType
    tmp5::uType
    tmp6::uType
    tmp7::uType
    tmp8::uType
    tmp9::uType
    tmp10::uType
    atmp::uNoUnitsType
    jac_config::JC
    linsolve1::F1
    linsolve2::F2
    linsolve3::F2
    rtol::rTol
    atol::aTol
    dtprev::Dt
    W_γdt::Dt
    status::NLStatus
    step_limiter!::StepLimiter
end

function alg_cache(
        alg::RadauIIA9, u, rate_prototype, ::Type{uEltypeNoUnits},
        ::Type{uBottomEltypeNoUnits},
        ::Type{tTypeNoUnits}, uprev, uprev2, f, t, dt, reltol, p, calck,
        ::Val{true}, verbose
    ) where {uEltypeNoUnits, uBottomEltypeNoUnits, tTypeNoUnits}
    uf = UJacobianWrapper(f, t, p)
    uToltype = constvalue(uBottomEltypeNoUnits)
    tab = RadauIIA9Tableau(uToltype, constvalue(tTypeNoUnits))

    κ = alg.κ !== nothing ? convert(uToltype, alg.κ) : convert(uToltype, 1 // 100)

    z1 = zero(u)
    z2 = zero(u)
    z3 = zero(u)
    z4 = zero(u)
    z5 = zero(u)
    w1 = zero(u)
    w2 = zero(u)
    w3 = zero(u)
    w4 = zero(u)
    w5 = zero(u)
    dw1 = zero(u)
    ubuff = zero(u)
    dw23 = similar(u, Complex{eltype(u)})
    dw45 = similar(u, Complex{eltype(u)})
    recursivefill!(dw23, false)
    recursivefill!(dw45, false)
    cubuff1 = similar(u, Complex{eltype(u)})
    cubuff2 = similar(u, Complex{eltype(u)})
    recursivefill!(cubuff1, false)
    recursivefill!(cubuff2, false)

    fsalfirst = zero(rate_prototype)
    k = zero(rate_prototype)
    k1 = zero(rate_prototype)
    k2 = zero(rate_prototype)
    k3 = zero(rate_prototype)
    k4 = zero(rate_prototype)
    k5 = zero(rate_prototype)
    fw1 = zero(rate_prototype)
    fw2 = zero(rate_prototype)
    fw3 = zero(rate_prototype)
    fw4 = zero(rate_prototype)
    fw5 = zero(rate_prototype)

    du1 = zero(rate_prototype)

    tmp = zero(u)
    tmp2 = zero(u)
    tmp3 = zero(u)
    tmp4 = zero(u)
    tmp5 = zero(u)
    tmp6 = zero(u)
    tmp7 = zero(u)
    tmp8 = zero(u)
    tmp9 = zero(u)
    tmp10 = zero(u)
    atmp = similar(u, uEltypeNoUnits)
    recursivefill!(atmp, false)
    jac_config = build_jac_config(alg, f, uf, du1, uprev, u, tmp, dw1)

    J, W1 = build_J_W(alg, u, uprev, p, t, dt, f, jac_config, uEltypeNoUnits, Val(true))
    if J isa AbstractSciMLOperator
        error("Non-concrete Jacobian not yet supported by RadauIIA5.")
    end
    W2 = similar(J, Complex{eltype(W1)})
    W3 = similar(J, Complex{eltype(W1)})
    recursivefill!(W2, false)
    recursivefill!(W3, false)

    linprob = LinearProblem(W1, _vec(ubuff); u0 = _vec(dw1))
    linsolve1 = init(
        linprob, alg.linsolve, alias = LinearAliasSpecifier(alias_A = true, alias_b = true),
        assumptions = LinearSolve.OperatorAssumptions(true), verbose = verbose.linear_verbosity
    )
    #Pl = LinearSolve.InvPreconditioner(Diagonal(_vec(weight))),
    #Pr = Diagonal(_vec(weight)))
    linprob = LinearProblem(W2, _vec(cubuff1); u0 = _vec(dw23))
    linsolve2 = init(
        linprob, alg.linsolve, alias = LinearAliasSpecifier(alias_A = true, alias_b = true),
        assumptions = LinearSolve.OperatorAssumptions(true), verbose = verbose.linear_verbosity
    )
    #Pl = LinearSolve.InvPreconditioner(Diagonal(_vec(weight))),
    #Pr = Diagonal(_vec(weight)))
    linprob = LinearProblem(W3, _vec(cubuff2); u0 = _vec(dw45))
    linsolve3 = init(
        linprob, alg.linsolve, alias = LinearAliasSpecifier(alias_A = true, alias_b = true),
        assumptions = LinearSolve.OperatorAssumptions(true), verbose = verbose.linear_verbosity
    )
    #Pl = LinearSolve.InvPreconditioner(Diagonal(_vec(weight))),
    #Pr = Diagonal(_vec(weight)))

    rtol = reltol isa Number ? reltol : zero(reltol)
    atol = reltol isa Number ? reltol : zero(reltol)

    return RadauIIA9Cache(
        u, uprev,
        z1, z2, z3, z4, z5, w1, w2, w3, w4, w5,
        dw1, ubuff, dw23, dw45, cubuff1, cubuff2,
        du1, fsalfirst, k, k1, k2, k3, k4, k5, fw1, fw2, fw3, fw4, fw5,
        J, W1, W2, W3,
        uf, tab, κ, one(uToltype), 10000,
        tmp, tmp2, tmp3, tmp4, tmp5, tmp6, tmp7, tmp8, tmp9, tmp10, atmp, jac_config,
        linsolve1, linsolve2, linsolve3, rtol, atol, dt, dt,
        Convergence, alg.step_limiter!
    )
end

mutable struct AdaptiveRadauConstantCache{F, Tab, Tol, Dt, U, JType} <:
    OrdinaryDiffEqConstantCache
    uf::F
    tabs::Vector{Tab}
    κ::Tol
    ηold::Tol
    iter::Int
    cont::Vector{U}
    dtprev::Dt
    W_γdt::Dt
    status::NLStatus
    J::JType
    num_stages::Int
    step::Int
    hist_iter::Float64
    index::Int
end

function alg_cache(
        alg::AdaptiveRadau, u, rate_prototype, ::Type{uEltypeNoUnits},
        ::Type{uBottomEltypeNoUnits},
        ::Type{tTypeNoUnits}, uprev, uprev2, f, t, dt, reltol, p, calck,
        ::Val{false}, verbose
    ) where {uEltypeNoUnits, uBottomEltypeNoUnits, tTypeNoUnits}
    uf = UDerivativeWrapper(f, t, p)
    uToltype = constvalue(uBottomEltypeNoUnits)

    max_order = alg.max_order
    min_order = alg.min_order
    max_stages = (max_order - 1) ÷ 4 * 2 + 1
    min_stages = (min_order - 1) ÷ 4 * 2 + 1
    if (alg.min_order < 5)
        error("min_order choice $min_order below 5 is not compatible with the algorithm")
    elseif (max_stages < min_stages)
        error("max_order $max_order is below min_order $min_order")
    end
    num_stages = min_stages

    tabs = [
        RadauIIATableau(uToltype, constvalue(tTypeNoUnits), i)
            for i in min_stages:2:max_stages
    ]
    cont = Vector{typeof(u)}(undef, max_stages)
    for i in 1:max_stages
        cont[i] = zero(u)
    end

    index = 1

    κ = alg.κ !== nothing ? convert(uToltype, alg.κ) : convert(uToltype, 1 // 100)
    J = false .* _vec(rate_prototype) .* _vec(rate_prototype)'
    return AdaptiveRadauConstantCache(
        uf, tabs, κ, one(uToltype), 10000, cont, dt, dt,
        Convergence, J, num_stages, 1, 0.0, index
    )
end

mutable struct AdaptiveRadauCache{
        uType, cuType, tType, uNoUnitsType, rateType, JType, W1Type, W2Type,
        UF, JC, F1, F2, Tab, Tol, Dt, rTol, aTol, StepLimiter,
    } <:
    FIRKMutableCache
    u::uType
    uprev::uType
    z::Vector{uType}
    w::Vector{uType}
    c_prime::Vector{tType}
    αdt::Vector{tType}
    βdt::Vector{tType}
    dw1::uType
    ubuff::uType
    dw2::Vector{cuType}
    cubuff::Vector{cuType}
    dw::Vector{uType}
    derivatives::Matrix{uType}
    du1::rateType
    fsalfirst::rateType
    ks::Vector{rateType}
    k::rateType
    fw::Vector{rateType}
    J::JType
    W1::W1Type #real
    W2::Vector{W2Type} #complex
    uf::UF
    tabs::Vector{Tab}
    κ::Tol
    ηold::Tol
    iter::Int
    tmp::uType
    atmp::uNoUnitsType
    jac_config::JC
    linsolve1::F1 #real
    linsolve2::Vector{F2} #complex
    rtol::rTol
    atol::aTol
    dtprev::Dt
    W_γdt::Dt
    status::NLStatus
    step_limiter!::StepLimiter
    num_stages::Int
    step::Int
    hist_iter::Float64
    index::Int
end

function alg_cache(
        alg::AdaptiveRadau, u, rate_prototype, ::Type{uEltypeNoUnits},
        ::Type{uBottomEltypeNoUnits},
        ::Type{tTypeNoUnits}, uprev, uprev2, f, t, dt, reltol, p, calck,
        ::Val{true}, verbose
    ) where {uEltypeNoUnits, uBottomEltypeNoUnits, tTypeNoUnits}
    uf = UJacobianWrapper(f, t, p)
    uToltype = constvalue(uBottomEltypeNoUnits)

    max_order = alg.max_order
    min_order = alg.min_order
    max_stages = (max_order - 1) ÷ 4 * 2 + 1
    min_stages = (min_order - 1) ÷ 4 * 2 + 1
    if (alg.min_order < 5)
        error("min_order choice $min_order below 5 is not compatible with the algorithm")
    elseif (max_stages < min_stages)
        error("max_order $max_order is below min_order $min_order")
    end
    num_stages = min_stages

    tabs = [
        RadauIIATableau(uToltype, constvalue(tTypeNoUnits), i)
            for i in min_stages:2:max_stages
    ]

    index = 1

    κ = alg.κ !== nothing ? convert(uToltype, alg.κ) : convert(uToltype, 1 // 100)

    z = Vector{typeof(u)}(undef, max_stages)
    w = Vector{typeof(u)}(undef, max_stages)
    for i in 1:max_stages
        z[i] = zero(u)
        w[i] = zero(u)
    end

    αdt = [zero(t) for i in 1:max_stages]
    βdt = [zero(t) for i in 1:max_stages]
    c_prime = Vector{typeof(t)}(undef, max_stages) #time stepping
    for i in 1:max_stages
        c_prime[i] = zero(t)
    end

    dw1 = zero(u)
    ubuff = zero(u)
    dw2 = [similar(u, Complex{eltype(u)}) for _ in 1:((max_stages - 1) ÷ 2)]
    recursivefill!.(dw2, false)
    cubuff = [similar(u, Complex{eltype(u)}) for _ in 1:((max_stages - 1) ÷ 2)]
    recursivefill!.(cubuff, false)
    dw = [zero(u) for i in 1:max_stages]

    derivatives = Matrix{typeof(u)}(undef, max_stages, max_stages)
    for i in 1:max_stages, j in 1:max_stages

        derivatives[i, j] = zero(u)
    end
    fsalfirst = zero(rate_prototype)
    fw = [zero(rate_prototype) for i in 1:max_stages]
    ks = [zero(rate_prototype) for i in 1:max_stages]

    k = ks[1]

    du1 = zero(rate_prototype)

    tmp = zero(u)

    atmp = similar(u, uEltypeNoUnits)
    recursivefill!(atmp, false)

    jac_config = build_jac_config(alg, f, uf, du1, uprev, u, zero(u), dw1)

    J, W1 = build_J_W(alg, u, uprev, p, t, dt, f, jac_config, uEltypeNoUnits, Val(true))
    if J isa AbstractSciMLOperator
        error("Non-concrete Jacobian not yet supported by AdaptiveRadau.")
    end

    W2 = [similar(J, Complex{eltype(W1)}) for _ in 1:((max_stages - 1) ÷ 2)]
    recursivefill!.(W2, false)

    linprob = LinearProblem(W1, _vec(ubuff); u0 = _vec(dw1))
    linsolve1 = init(
        linprob, alg.linsolve, alias = LinearAliasSpecifier(alias_A = true, alias_b = true),
        assumptions = LinearSolve.OperatorAssumptions(true), verbose = verbose.linear_verbosity
    )

    linsolve2 = [
        init(
                LinearProblem(W2[i], _vec(cubuff[i]); u0 = _vec(dw2[i])),
                alg.linsolve, alias = LinearAliasSpecifier(
                    alias_A = true, alias_b = true
                ),
                assumptions = LinearSolve.OperatorAssumptions(true), verbose = verbose.linear_verbosity
            )
            for i in 1:((max_stages - 1) ÷ 2)
    ]

    rtol = reltol isa Number ? reltol : zero(reltol)
    atol = reltol isa Number ? reltol : zero(reltol)

    return AdaptiveRadauCache(
        u, uprev,
        z, w, c_prime, αdt, βdt, dw1, ubuff, dw2, cubuff, dw, derivatives,
        du1, fsalfirst, ks, k, fw,
        J, W1, W2,
        uf, tabs, κ, one(uToltype), 10000, tmp,
        atmp, jac_config,
        linsolve1, linsolve2, rtol, atol, dt, dt,
        Convergence, alg.step_limiter!, num_stages, 1, 0.0, index
    )
end
