Krylov Version for Trust Region

Project.toml

@@ -1,7 +1,7 @@
 name = "NonlinearSolve"
 uuid = "8913a72c-1f9b-4ce2-8d82-65094dcecaec"
 authors = ["SciML"]
-version = "2.8.0"
+version = "2.9.0"
 
 [deps]
 ADTypes = "47edcb42-4c32-4615-8424-f2b9edc5f35b"

src/gaussnewton.jl

@@ -6,10 +6,6 @@ An advanced GaussNewton implementation with support for efficient handling of sp
 matrices via colored automatic differentiation and preconditioned linear solvers. Designed
 for large-scale and numerically-difficult nonlinear least squares problems.
 
-!!! note
-    In most practical situations, users should prefer using `LevenbergMarquardt` instead! It
-    is a more general extension of `Gauss-Newton` Method.
-
 ### Keyword Arguments
 
   - `autodiff`: determines the backend used for the Jacobian. Note that this argument is
@@ -33,11 +29,6 @@ for large-scale and numerically-difficult nonlinear least squares problems.
   - `linesearch`: the line search algorithm to use. Defaults to [`LineSearch()`](@ref),
     which means that no line search is performed. Algorithms from `LineSearches.jl` can be
     used here directly, and they will be converted to the correct `LineSearch`.
-
-!!! warning
-
-    Jacobian-Free version of `GaussNewton` doesn't work yet, and it forces jacobian
-    construction. This will be fixed in the near future.
 """
 @concrete struct GaussNewton{CJ, AD} <: AbstractNewtonAlgorithm{CJ, AD}
     ad::AD

src/jacobian.jl

@@ -3,6 +3,8 @@
     p
 end
 
+SciMLBase.isinplace(::JacobianWrapper{iip}) where {iip} = iip
+
 # Previous Implementation did not hold onto `iip`, but this causes problems in packages
 # where we check for the presence of function signatures to check which dispatch to call
 (uf::JacobianWrapper{false})(u) = uf.f(u, uf.p)
@@ -65,7 +67,7 @@ function jacobian_caches(alg::AbstractNonlinearSolveAlgorithm, f::F, u, p, ::Val
     # NOTE: The deepcopy is needed here since we are using the resid_prototype elsewhere
     fu = f.resid_prototype === nothing ? (iip ? _mutable_zero(u) : _mutable(f(u, p))) :
          (iip ? deepcopy(f.resid_prototype) : f.resid_prototype)
-    if !has_analytic_jac && (linsolve_needs_jac || alg_wants_jac || needsJᵀJ)
+    if !has_analytic_jac && (linsolve_needs_jac || alg_wants_jac) #  || needsJᵀJ)
         sd = sparsity_detection_alg(f, alg.ad)
         ad = alg.ad
         jac_cache = iip ? sparse_jacobian_cache(ad, sd, uf, fu, _maybe_mutable(u, ad)) :
@@ -76,7 +78,7 @@ function jacobian_caches(alg::AbstractNonlinearSolveAlgorithm, f::F, u, p, ::Val
 
     # FIXME: To properly support needsJᵀJ without Jacobian, we need to implement
     #        a reverse diff operation with the seed being `Jx`, this is not yet implemented
-    J = if !(linsolve_needs_jac || alg_wants_jac || needsJᵀJ)
+    J = if !(linsolve_needs_jac || alg_wants_jac)#  || needsJᵀJ)
         # We don't need to construct the Jacobian
         JacVec(uf, u; autodiff = __get_nonsparse_ad(alg.ad))
     else
@@ -90,9 +92,9 @@ function jacobian_caches(alg::AbstractNonlinearSolveAlgorithm, f::F, u, p, ::Val
     du = _mutable_zero(u)
 
     if needsJᵀJ
-        JᵀJ = __init_JᵀJ(J)
-        # FIXME: This needs to be handled better for JacVec Operator
-        Jᵀfu = J' * _vec(fu)
+        # TODO: Pass in `jac_transpose_autodiff`
+        JᵀJ, Jᵀfu = __init_JᵀJ(J, _vec(fu), uf, u;
+            jac_autodiff = __get_nonsparse_ad(alg.ad))
     end
 
     if linsolve_init
@@ -118,21 +120,68 @@ function __setup_linsolve(A, b, u, p, alg)
             nothing)..., weight)
     return init(linprob, alg.linsolve; alias_A = true, alias_b = true, Pl, Pr)
 end
+__setup_linsolve(A::KrylovJᵀJ, b, u, p, alg) = __setup_linsolve(A.JᵀJ, b, u, p, alg)
 
 __get_nonsparse_ad(::AutoSparseForwardDiff) = AutoForwardDiff()
 __get_nonsparse_ad(::AutoSparseFiniteDiff) = AutoFiniteDiff()
 __get_nonsparse_ad(::AutoSparseZygote) = AutoZygote()
 __get_nonsparse_ad(ad) = ad
 
-__init_JᵀJ(J::Number) = zero(J)
-__init_JᵀJ(J::AbstractArray) = J' * J
-__init_JᵀJ(J::StaticArray) = MArray{Tuple{size(J, 2), size(J, 2)}, eltype(J)}(undef)
+__init_JᵀJ(J::Number, args...; kwargs...) = zero(J), zero(J)
+function __init_JᵀJ(J::AbstractArray, fu, args...; kwargs...)
+    JᵀJ = J' * J
+    Jᵀfu = J' * fu
+    return JᵀJ, Jᵀfu
+end
+function __init_JᵀJ(J::StaticArray, fu, args...; kwargs...)
+    JᵀJ = MArray{Tuple{size(J, 2), size(J, 2)}, eltype(J)}(undef)
+    return JᵀJ, J' * fu
+end
+function __init_JᵀJ(J::FunctionOperator, fu, uf, u, args...;
+        jac_transpose_autodiff = nothing, jac_autodiff = nothing, kwargs...)
+    autodiff = __concrete_jac_transpose_autodiff(jac_transpose_autodiff, jac_autodiff, uf)
+    Jᵀ = VecJac(uf, u; autodiff)
+    JᵀJ_op = SciMLOperators.cache_operator(Jᵀ * J, u)
+    JᵀJ = KrylovJᵀJ(JᵀJ_op, Jᵀ)
+    Jᵀfu = Jᵀ * fu
+    return JᵀJ, Jᵀfu
+end
+
+@concrete struct KrylovJᵀJ
+    JᵀJ
+    Jᵀ
+end
+
+SciMLBase.isinplace(JᵀJ::KrylovJᵀJ) = isinplace(JᵀJ.Jᵀ)
+
+function __concrete_jac_transpose_autodiff(jac_transpose_autodiff, jac_autodiff, uf)
+    if jac_transpose_autodiff === nothing
+        if isinplace(uf)
+            # VecJac can be only FiniteDiff
+            return AutoFiniteDiff()
+        else
+            # Short circuit if we see that FiniteDiff was used for J computation
+            jac_autodiff isa AutoFiniteDiff && return jac_autodiff
+            # Check if Zygote is loaded then use Zygote else use FiniteDiff
+            if haskey(Base.loaded_modules,
+                Base.PkgId(Base.UUID("e88e6eb3-aa80-5325-afca-941959d7151f"), "Zygote"))
+                return AutoZygote()
+            else
+                return AutoFiniteDiff()
+            end
+        end
+    else
+        return __get_nonsparse_ad(jac_transpose_autodiff)
+    end
+end
 
 __maybe_symmetric(x) = Symmetric(x)
 __maybe_symmetric(x::Number) = x
 # LinearSolve with `nothing` doesn't dispatch correctly here
 __maybe_symmetric(x::StaticArray) = x
 __maybe_symmetric(x::SparseArrays.AbstractSparseMatrix) = x
+__maybe_symmetric(x::SciMLOperators.AbstractSciMLOperator) = x
+__maybe_symmetric(x::KrylovJᵀJ) = x
 
 ## Special Handling for Scalars
 function jacobian_caches(alg::AbstractNonlinearSolveAlgorithm, f::F, u::Number, p,
@@ -143,3 +192,37 @@ function jacobian_caches(alg::AbstractNonlinearSolveAlgorithm, f::F, u::Number,
     needsJᵀJ && return uf, nothing, u, nothing, nothing, u, u, u
     return uf, nothing, u, nothing, nothing, u
 end
+
+function __update_JᵀJ!(iip::Val, cache, sym::Symbol, J)
+    return __update_JᵀJ!(iip, cache, sym, getproperty(cache, sym), J)
+end
+__update_JᵀJ!(::Val{false}, cache, sym::Symbol, _, J) = setproperty!(cache, sym, J' * J)
+__update_JᵀJ!(::Val{true}, cache, sym::Symbol, _, J) = mul!(getproperty(cache, sym), J', J)
+__update_JᵀJ!(::Val{false}, cache, sym::Symbol, H::KrylovJᵀJ, J) = H
+__update_JᵀJ!(::Val{true}, cache, sym::Symbol, H::KrylovJᵀJ, J) = H
+
+function __update_Jᵀf!(iip::Val, cache, sym1::Symbol, sym2::Symbol, J, fu)
+    return __update_Jᵀf!(iip, cache, sym1, sym2, getproperty(cache, sym2), J, fu)
+end
+function __update_Jᵀf!(::Val{false}, cache, sym1::Symbol, sym2::Symbol, _, J, fu)
+    return setproperty!(cache, sym1, J' * fu)
+end
+function __update_Jᵀf!(::Val{true}, cache, sym1::Symbol, sym2::Symbol, _, J, fu)
+    return mul!(getproperty(cache, sym1), J', fu)
+end
+function __update_Jᵀf!(::Val{false}, cache, sym1::Symbol, sym2::Symbol, H::KrylovJᵀJ, J, fu)
+    return setproperty!(cache, sym1, H.Jᵀ * fu)
+end
+function __update_Jᵀf!(::Val{true}, cache, sym1::Symbol, sym2::Symbol, H::KrylovJᵀJ, J, fu)
+    return mul!(getproperty(cache, sym1), H.Jᵀ, fu)
+end
+
+# Left-Right Multiplication
+__lr_mul(::Val, H, g) = dot(g, H, g)
+## TODO: Use a cache here to avoid allocations
+__lr_mul(::Val{false}, H::KrylovJᵀJ, g) = dot(g, H.JᵀJ, g)
+function __lr_mul(::Val{true}, H::KrylovJᵀJ, g)
+    c = similar(g)
+    mul!(c, H.JᵀJ, g)
+    return dot(g, c)
+end

src/trustRegion.jl

@@ -239,15 +239,19 @@ function SciMLBase.__init(prob::NonlinearProblem{uType, iip}, alg_::TrustRegion,
     fu_prev = zero(fu1)
 
     loss = get_loss(fu1)
-    uf, linsolve, J, fu2, jac_cache, du = jacobian_caches(alg, f, u, p, Val(iip);
-        linsolve_kwargs)
+    # uf, linsolve, J, fu2, jac_cache, du = jacobian_caches(alg, f, u, p, Val(iip);
+    # linsolve_kwargs)
+    uf, _, J, fu2, jac_cache, du, H, g = jacobian_caches(alg, f, u, p, Val(iip);
+        linsolve_kwargs, linsolve_with_JᵀJ = Val(true), lininit = Val(false))
+    linsolve = u isa Number ? nothing : __setup_linsolve(J, fu2, u, p, alg)
+
     u_tmp = zero(u)
     u_cauchy = zero(u)
     u_gauss_newton = _mutable_zero(u)
 
     loss_new = loss
-    H = zero(J' * J)
-    g = _mutable_zero(fu1)
+    # H = zero(J' * J)
+    # g = _mutable_zero(fu1)
     shrink_counter = 0
     fu_new = zero(fu1)
     make_new_J = true
@@ -346,8 +350,10 @@ function perform_step!(cache::TrustRegionCache{true})
     @unpack make_new_J, J, fu, f, u, p, u_gauss_newton, alg, linsolve = cache
     if cache.make_new_J
         jacobian!!(J, cache)
-        mul!(cache.H, J', J)
-        mul!(_vec(cache.g), J', _vec(fu))
+        __update_JᵀJ!(Val{true}(), cache, :H, J)
+        # mul!(cache.H, J', J)
+        __update_Jᵀf!(Val{true}(), cache, :g, :H, J, fu)
+        # mul!(_vec(cache.g), J', _vec(fu))
         cache.stats.njacs += 1
 
         # do not use A = cache.H, b = _vec(cache.g) since it is equivalent
@@ -376,8 +382,8 @@ function perform_step!(cache::TrustRegionCache{false})
 
     if make_new_J
         J = jacobian!!(cache.J, cache)
-        cache.H = J' * J
-        cache.g = _restructure(fu, J' * _vec(fu))
+        __update_JᵀJ!(Val{false}(), cache, :H, J)
+        __update_Jᵀf!(Val{false}(), cache, :g, :H, J, fu)
         cache.stats.njacs += 1
 
         if cache.linsolve === nothing
@@ -431,7 +437,7 @@ function trust_region_step!(cache::TrustRegionCache)
 
     # Compute the ratio of the actual reduction to the predicted reduction.
     cache.r = -(loss - cache.loss_new) /
-              (dot(_vec(du), _vec(g)) + dot(_vec(du), H, _vec(du)) / 2)
+              (dot(_vec(du), _vec(g)) + __lr_mul(Val(isinplace(cache)), H, _vec(du)) / 2)
     @unpack r = cache
 
     if radius_update_scheme === RadiusUpdateSchemes.Simple
@@ -594,7 +600,7 @@ function dogleg!(cache::TrustRegionCache{true})
 
     # Take intersection of steepest descent direction and trust region if Cauchy point lies outside of trust region
     l_grad = norm(cache.g) # length of the gradient
-    d_cauchy = l_grad^3 / dot(_vec(cache.g), cache.H, _vec(cache.g)) # distance of the cauchy point from the current iterate
+    d_cauchy = l_grad^3 / __lr_mul(Val{true}(), cache.H, _vec(cache.g)) # distance of the cauchy point from the current iterate
     if d_cauchy >= trust_r
         @. cache.du = -(trust_r / l_grad) * cache.g # step to the end of the trust region
         return
@@ -624,7 +630,7 @@ function dogleg!(cache::TrustRegionCache{false})
 
     ## Take intersection of steepest descent direction and trust region if Cauchy point lies outside of trust region
     l_grad = norm(cache.g)
-    d_cauchy = l_grad^3 / dot(_vec(cache.g), cache.H, _vec(cache.g)) # distance of the cauchy point from the current iterate
+    d_cauchy = l_grad^3 / __lr_mul(Val{false}(), cache.H, _vec(cache.g)) # distance of the cauchy point from the current iterate
     if d_cauchy > trust_r # cauchy point lies outside of trust region
         cache.du = -(trust_r / l_grad) * cache.g # step to the end of the trust region
         return