fix: update GeneralDomain to the new ManifoldProjection API

docs/Project.toml

@@ -1,11 +1,13 @@
 [deps]
+ADTypes = "47edcb42-4c32-4615-8424-f2b9edc5f35b"
 DiffEqCallbacks = "459566f4-90b8-5000-8ac3-15dfb0a30def"
 Documenter = "e30172f5-a6a5-5a46-863b-614d45cd2de4"
 LinearAlgebra = "37e2e46d-f89d-539d-b4ee-838fcccc9c8e"
 OrdinaryDiffEq = "1dea7af3-3e70-54e6-95c3-0bf5283fa5ed"
 Plots = "91a5bcdd-55d7-5caf-9e0b-520d859cae80"
 
 [compat]
+ADTypes = "1.9.0"
 DiffEqCallbacks = "3"
 Documenter = "1"
 OrdinaryDiffEq = "6.88"

docs/src/projection.md

@@ -12,7 +12,7 @@ ManifoldProjection
 Here we solve the harmonic oscillator:
 
 ```@example manifold
-using OrdinaryDiffEq, DiffEqCallbacks, Plots
+using OrdinaryDiffEq, DiffEqCallbacks, Plots, ADTypes
 
 u0 = ones(2)
 function f(du, u, p, t)
@@ -35,7 +35,7 @@ end
 To build the callback, we just call
 
 ```@example manifold
-cb = ManifoldProjection(g)
+cb = ManifoldProjection(g; autodiff = AutoForwardDiff())
 ```
 
 Using this callback, the Runge-Kutta method `Vern7` conserves energy. Note that the

src/domain.jl

@@ -2,33 +2,35 @@
 
 abstract type AbstractDomainAffect{T, S, uType} end
 
+(f::AbstractDomainAffect)(integrator) = affect!(integrator, f)
+
 struct PositiveDomainAffect{T, S, uType} <: AbstractDomainAffect{T, S, uType}
     abstol::T
     scalefactor::S
     u::uType
 end
 
-struct GeneralDomainAffect{autonomous, F, T, S, uType} <: AbstractDomainAffect{T, S, uType}
+struct GeneralDomainAffect{F <: AbstractNonAutonomousFunction, T, S, uType, A} <:
+       AbstractDomainAffect{T, S, uType}
     g::F
     abstol::T
     scalefactor::S
     u::uType
     resid::uType
+    autonomous::A
+end
 
-    function GeneralDomainAffect{autonomous}(g::F, abstol::T, scalefactor::S, u::uType,
-            resid::uType) where {autonomous, F, T, S, uType
-    }
-        new{autonomous, F, T, S, uType}(g, abstol, scalefactor, u, resid)
+function initialize_general_domain_affect(cb, u, t, integrator)
+    return initialize_general_domain_affect(cb.affect!, u, t, integrator)
+end
+function initialize_general_domain_affect(affect!::GeneralDomainAffect, u, t, integrator)
+    if affect!.autonomous === nothing
+        autonomous = maximum(SciMLBase.numargs(affect!.g.f)) ==
+                     2 + SciMLBase.isinplace(integrator.f)
+        affect!.g.autonomous = autonomous
     end
 end
 
-# definitions of callback functions
-
-# Workaround since it is not possible to add methods to an abstract type:
-# https://github.com/JuliaLang/julia/issues/14919
-(f::PositiveDomainAffect)(integrator) = affect!(integrator, f)
-(f::GeneralDomainAffect)(integrator) = affect!(integrator, f)
-
 # general method definitions for domain callbacks
 
 """
@@ -41,6 +43,8 @@ function affect!(integrator, f::AbstractDomainAffect{T, S, uType}) where {T, S,
         throw(ArgumentError("domain callback can only be applied to adaptive algorithms"))
     end
 
+    iip = Val(SciMLBase.isinplace(integrator.f))
+
     # define array of next time step, absolute tolerance, and scale factor
     if uType <: Nothing
         if integrator.u isa Union{Number, StaticArraysCore.SArray}
@@ -55,7 +59,7 @@ function affect!(integrator, f::AbstractDomainAffect{T, S, uType}) where {T, S,
     scalefactor = S <: Nothing ? 1 // 2 : f.scalefactor
 
     # setup callback and save additional arguments for checking next time step
-    args = setup(f, integrator)
+    args = setup(f, integrator, iip)
 
     # obtain proposed next time step
     dt = get_proposed_dt(integrator)
@@ -80,7 +84,7 @@ function affect!(integrator, f::AbstractDomainAffect{T, S, uType}) where {T, S,
         end
 
         # check whether time step is accepted
-        isaccepted(u, p, t, abstol, f, args...) && break
+        isaccepted(u, p, t, abstol, f, iip, args...) && break
 
         # reduce time step
         dtcache = dt
@@ -120,20 +124,20 @@ was modified.
 modify_u!(integrator, ::AbstractDomainAffect) = false
 
 """
-    setup(f::AbstractDomainAffect, integrator)
+    setup(f::AbstractDomainAffect, integrator, ::Val{iip}) where {iip}
 
 Setup callback `f` and return an arbitrary tuple whose elements are used as additional
 arguments in checking whether time step is accepted.
 """
-setup(::AbstractDomainAffect, integrator) = ()
+setup(::AbstractDomainAffect, integrator, ::Val{iip}) where {iip} = ()
 
 """
     isaccepted(u, abstol, f::AbstractDomainAffect, args...)
 
 Return whether `u` is an acceptable state vector at the next time point given absolute
 tolerance `abstol`, callback `f`, and other optional arguments.
 """
-isaccepted(u, p, t, tolerance, ::AbstractDomainAffect, args...) = true
+isaccepted(u, p, t, tolerance, ::AbstractDomainAffect, ::Val{iip}, args...) where {iip} = true
 
 # specific method definitions for positive domain callback
 
@@ -175,27 +179,30 @@ function _set_neg_zero!(integrator, u::StaticArraysCore.SArray)
 end
 
 # state vector is accepted if its entries are greater than -abstol
-isaccepted(u, p, t, abstol::Number, ::PositiveDomainAffect) = all(ui -> ui > -abstol, u)
-function isaccepted(u, p, t, abstol, ::PositiveDomainAffect)
+function isaccepted(u, p, t, abstol::Number, ::PositiveDomainAffect, ::Val{iip}) where {iip}
+    return all(ui -> ui > -abstol, u)
+end
+function isaccepted(u, p, t, abstol, ::PositiveDomainAffect, ::Val{iip}) where {iip}
     length(u) == length(abstol) ||
         throw(DimensionMismatch("numbers of states and tolerances do not match"))
-    all(ui > -tol for (ui, tol) in zip(u, abstol))
+    return all(ui > -tol for (ui, tol) in zip(u, abstol))
 end
 
 # specific method definitions for general domain callback
 
 # create array of residuals
-function setup(f::GeneralDomainAffect, integrator)
-    f.resid isa Nothing ? (similar(integrator.u),) : (f.resid,)
+setup(f::GeneralDomainAffect, integrator, ::Val{false}) = (nothing,)
+function setup(f::GeneralDomainAffect, integrator, ::Val{true})
+    return f.resid === nothing ? (similar(integrator.u),) : (f.resid,)
 end
 
-function isaccepted(u, p, t, abstol, f::GeneralDomainAffect{autonomous, F, T, S, uType},
-        resid) where {autonomous, F, T, S, uType}
+function isaccepted(u, p, t, abstol, f::GeneralDomainAffect, ::Val{iip}, resid) where {iip}
     # calculate residuals
-    if autonomous
+    f.g.t = t
+    if iip
         f.g(resid, u, p)
     else
-        f.g(resid, u, p, t)
+        resid = f.g(u, p)
     end
 
     # accept time step if residuals are smaller than the tolerance
@@ -214,26 +221,32 @@ end
 """
     GeneralDomain(
         g, u = nothing; save = true, abstol = nothing, scalefactor = nothing,
-        autonomous = maximum(SciMLBase.numargs(g)) == 3, nlsolve_kwargs = (;
-            abstol = 10 * eps()), kwargs...)
+        autonomous = nothing, domain_jacobian = nothing,
+        nlsolve_kwargs = (; abstol = 10 * eps()), kwargs...)
 
 A `GeneralDomain` callback in DiffEqCallbacks.jl generalizes the concept of
-a `PositiveDomain` callback to arbitrary domains. Domains are specified by
-in-place functions `g(resid, u, p)` or `g(resid, u, p, t)` that calculate residuals of a
-state vector `u` at time `t` relative to that domain, with `p` the parameters of the
-corresponding integrator. As for `PositiveDomain`, steps are accepted if residuals
-of the extrapolated values at the next time step are below
-a certain tolerance. Moreover, this callback is automatically coupled with a
-`ManifoldProjection` that keeps all calculated state vectors close to the desired
-domain, but in contrast to a `PositiveDomain` callback the nonlinear solver in a
-`ManifoldProjection` cannot guarantee that all state vectors of the solution are
-actually inside the domain. Thus, a `PositiveDomain` callback should generally be
-preferred.
+a `PositiveDomain` callback to arbitrary domains.
+
+Domains are specified by
+  - in-place functions `g(resid, u, p)` or `g(resid, u, p, t)` if the corresponding
+    ODEProblem is an inplace problem, or
+  - out-of-place functions `g(u, p)` or `g(u, p, t)` if the corresponding ODEProblem is
+    an out-of-place problem.
+
+The function calculates residuals of a state vector `u` at time `t` relative to that domain,
+with `p` the parameters of the corresponding integrator.
+
+As for `PositiveDomain`, steps are accepted if residuals of the extrapolated values at the
+next time step are below a certain tolerance. Moreover, this callback is automatically
+coupled with a `ManifoldProjection` that keeps all calculated state vectors close to the
+desired domain, but in contrast to a `PositiveDomain` callback the nonlinear solver in a
+`ManifoldProjection` cannot guarantee that all state vectors of the solution are actually
+inside the domain. Thus, a `PositiveDomain` callback should generally be preferred.
 
 ## Arguments
 
-  - `g`: the implicit definition of the domain as a function `g(resid, u, p)` or
-    `g(resid, u, p, t)` which is zero when the value is in the domain.
+  - `g`: the implicit definition of the domain as a function as described above which is
+    zero when the value is in the domain.
   - `u`: A prototype of the state vector of the integrator. A copy of it is saved and
     extrapolated values are written to it. If it is not specified,
     every application of the callback allocates a new copy of the state vector.
@@ -248,9 +261,13 @@ preferred.
     specified, time steps are halved.
   - `autonomous`: Whether `g` is an autonomous function of the form `g(resid, u, p)`.
     If it is not specified, it is determined automatically.
-  - `kwargs`: All other keyword arguments are passed to `ManifoldProjection`.
+  - `kwargs`: All other keyword arguments are passed to [`ManifoldProjection`](@ref).
   - `nlsolve_kwargs`: All keyword arguments are passed to the nonlinear solver in
     `ManifoldProjection`. The default is `(; abstol = 10 * eps())`.
+  - `domain_jacobian`: The Jacobian of the domain (wrt the state). This has the same
+    signature as `g` and the first argument is the Jacobian if inplace. This corresponds to
+    the `manifold_jacobian` argument of [`ManifoldProjection`](@ref). Note that passing
+    a `manifold_jacobian` is not supported for `GeneralDomain` and results in an error.
 
 ## References
 
@@ -260,20 +277,27 @@ Non-negative solutions of ODEs. Applied Mathematics and Computation 170
 """
 function GeneralDomain(
         g, u = nothing; save = true, abstol = nothing, scalefactor = nothing,
-        autonomous = maximum(SciMLBase.numargs(g)) == 3, nlsolve_kwargs = (;
-            abstol = 10 * eps()), kwargs...)
-    _autonomous = SciMLBase._unwrap_val(autonomous)
-    if u isa Nothing
-        affect! = GeneralDomainAffect{_autonomous}(g, abstol, scalefactor, nothing, nothing)
+        autonomous = nothing, domain_jacobian = nothing, manifold_jacobian = missing,
+        nlsolve_kwargs = (; abstol = 10 * eps()), kwargs...)
+    if manifold_jacobian !== missing
+        throw(ArgumentError("`manifold_jacobian` is not supported for `GeneralDomain`. \
+                             Use `domain_jacobian` instead."))
+    end
+    manifold_projection = ManifoldProjection(
+        g; save = false, autonomous, manifold_jacobian = domain_jacobian,
+        kwargs..., nlsolve_kwargs...)
+    domain = wrap_autonomous_function(autonomous, g)
+    domain_jacobian = wrap_autonomous_function(autonomous, domain_jacobian)
+    affect! = if u === nothing
+        GeneralDomainAffect(domain, abstol, scalefactor, nothing, nothing, autonomous)
     else
-        affect! = GeneralDomainAffect{_autonomous}(g, abstol, scalefactor, deepcopy(u),
-            deepcopy(u))
+        GeneralDomainAffect(
+            domain, abstol, scalefactor, deepcopy(u), deepcopy(u), autonomous)
     end
-    condition = (u, t, integrator) -> true
-    CallbackSet(
-        ManifoldProjection(
-            g; save = false, autonomous, isinplace = Val(true), kwargs..., nlsolve_kwargs...),
-        DiscreteCallback(condition, affect!; save_positions = (false, save)))
+    domain_cb = DiscreteCallback(
+        Returns(true), affect!; initialize = initialize_general_domain_affect,
+        save_positions = (false, save))
+    return CallbackSet(manifold_projection, domain_cb)
 end
 
 @doc doc"""

src/manifold.jl

@@ -31,7 +31,8 @@ properties.
     would work in most cases (See [1] for details). Alternatively, a nonlinear solver as
     defined in the
     [NonlinearSolve.jl format](https://docs.sciml.ai/NonlinearSolve/stable/basics/solve/)
-    can be specified.
+    can be specified. Additionally if NonlinearSolve.jl is loaded and `nothing` is specified
+    a polyalgorithm is used.
   - `save`: Whether to do the standard saving (applied after the callback)
   - `autonomous`: Whether `g` is an autonomous function of the form `g(resid, u, p)` or
     `g(u, p)`. Specify it as `Val(::Bool)` to disable runtime branching. If `nothing`,
@@ -88,25 +89,8 @@ end
 
 function ManifoldProjection(
         manifold, autodiff, manifold_jacobian, nlsolve, kwargs, autonomous)
-    if autonomous isa Val{true} || autonomous isa Val{false}
-        wrapped_manifold = TypedNonAutonomousFunction{SciMLBase._unwrap_val(autonomous)}(
-            manifold, nothing)
-        wrapped_manifold_jacobian = if manifold_jacobian === nothing
-            nothing
-        else
-            TypedNonAutonomousFunction{SciMLBase._unwrap_val(autonomous)}(
-                manifold_jacobian, nothing)
-        end
-        autonomous = SciMLBase._unwrap_val(autonomous)
-    else
-        _autonomous = autonomous === nothing ? false : autonomous
-        wrapped_manifold = UntypedNonAutonomousFunction(_autonomous, manifold, nothing)
-        wrapped_manifold_jacobian = if manifold_jacobian === nothing
-            nothing
-        else
-            UntypedNonAutonomousFunction(_autonomous, manifold_jacobian, nothing)
-        end
-    end
+    wrapped_manifold = wrap_autonomous_function(autonomous, manifold)
+    wrapped_manifold_jacobian = wrap_autonomous_function(autonomous, manifold_jacobian)
     return ManifoldProjection(wrapped_manifold, wrapped_manifold_jacobian,
         autodiff, nothing, nlsolve, kwargs, autonomous)
 end
@@ -158,7 +142,20 @@ end
 export ManifoldProjection
 
 # wrapper for non-autonomous functions
-@concrete mutable struct TypedNonAutonomousFunction{autonomous}
+function wrap_autonomous_function(autonomous::Union{Val{true}, Val{false}}, g)
+    g === nothing && return nothing
+    return TypedNonAutonomousFunction{SciMLBase._unwrap_val(autonomous)}(g, nothing)
+end
+function wrap_autonomous_function(autonomous::Union{Bool, Nothing}, g)
+    g === nothing && return nothing
+    autonomous = autonomous === nothing ? false : autonomous
+    return UntypedNonAutonomousFunction(autonomous, g, nothing)
+end
+
+abstract type AbstractNonAutonomousFunction end
+
+@concrete mutable struct TypedNonAutonomousFunction{autonomous} <:
+                         AbstractNonAutonomousFunction
     f
     t::Any
 end
@@ -169,7 +166,7 @@ end
 (f::TypedNonAutonomousFunction{false})(u, p) = f.f(u, p, f.t)
 (f::TypedNonAutonomousFunction{true})(u, p) = f.f(u, p)
 
-@concrete mutable struct UntypedNonAutonomousFunction
+@concrete mutable struct UntypedNonAutonomousFunction <: AbstractNonAutonomousFunction
     autonomous::Bool
     f
     t::Any

test/domain_tests.jl

@@ -1,4 +1,4 @@
-using DiffEqCallbacks, OrdinaryDiffEq, Test
+using DiffEqCallbacks, OrdinaryDiffEq, Test, ADTypes, NonlinearSolve
 
 # Non-negative ODE examples
 #
@@ -39,7 +39,11 @@ naive_sol_absval = solve(prob_absval, BS3())
 function g(resid, u, p)
     resid[1] = u[1] < 0 ? -u[1] : 0
 end
-general_sol_absval = solve(prob_absval, BS3(); callback = GeneralDomain(g, [1.0]),
+general_sol_absval = solve(
+    prob_absval, BS3();
+    callback = GeneralDomain(g, [1.0];
+        autodiff = AutoForwardDiff(),
+        nlsolve=NewtonRaphson(; autodiff = AutoForwardDiff())),
     save_everystep = false)
 @test all(x -> x[1] ≥ 0, general_sol_absval.u)
 @test general_sol_absval.errors[:l∞] < 9.9e-5
@@ -49,7 +53,11 @@ general_sol_absval = solve(prob_absval, BS3(); callback = GeneralDomain(g, [1.0]
 # test "non-autonomous" function
 g_t(resid, u, p, t) = g(resid, u, p)
 
-general_t_sol_absval = solve(prob_absval, BS3(); callback = GeneralDomain(g_t, [1.0]),
+general_t_sol_absval = solve(
+    prob_absval, BS3();
+    callback = GeneralDomain(g_t, [1.0];
+        autodiff = AutoForwardDiff(),
+        nlsolve=NewtonRaphson(; autodiff = AutoForwardDiff())),
     save_everystep = false)
 @test general_sol_absval.t ≈ general_t_sol_absval.t
 @test general_sol_absval.u ≈ general_t_sol_absval.u