From 62b7388b00ffe8da7fffbe4417e894929f261ce4 Mon Sep 17 00:00:00 2001
From: Avik Pal <avikpal@mit.edu>
Date: Sun, 27 Aug 2023 18:36:19 -0400
Subject: [PATCH 1/2] Fix SteadyStateAdjoint Breakage

---
 Project.toml               |  2 +-
 src/steadystate_adjoint.jl |  3 ++-
 test/steady_state.jl       | 21 +++++++++++++++++++++
 3 files changed, 24 insertions(+), 2 deletions(-)

diff --git a/Project.toml b/Project.toml
index f6a9cb20c..4e5567a42 100644
--- a/Project.toml
+++ b/Project.toml
@@ -1,7 +1,7 @@
 name = "SciMLSensitivity"
 uuid = "1ed8b502-d754-442c-8d5d-10ac956f44a1"
 authors = ["Christopher Rackauckas <accounts@chrisrackauckas.com>", "Yingbo Ma <mayingbo5@gmail.com>"]
-version = "7.37.1"
+version = "7.37.2"
 
 [deps]
 ADTypes = "47edcb42-4c32-4615-8424-f2b9edc5f35b"
diff --git a/src/steadystate_adjoint.jl b/src/steadystate_adjoint.jl
index af73558a2..e24ba04c1 100644
--- a/src/steadystate_adjoint.jl
+++ b/src/steadystate_adjoint.jl
@@ -101,7 +101,8 @@ end
 
     if !needs_jac
         # operator = VecJac(f, y, p; Val(DiffEqBase.isinplace(sol.prob)))
-        operator = VecJac(f, y, p; autodiff = get_autodiff_from_vjp(vjp))
+        __f = y -> f(y, p, nothing)
+        operator = VecJac(__f, y; autodiff = get_autodiff_from_vjp(sensealg.autojacvec))
         linear_problem = LinearProblem(operator, vec(dgdu_val); u0 = vec(λ))
     else
         linear_problem = LinearProblem(diffcache.J', vec(dgdu_val'); u0 = vec(λ))
diff --git a/test/steady_state.jl b/test/steady_state.jl
index 1f50a11aa..b3b4c3380 100644
--- a/test/steady_state.jl
+++ b/test/steady_state.jl
@@ -424,6 +424,27 @@ end
     @test dp1≈dp6 rtol=1e-10
     @test dp1≈dp7 rtol=1e-10
     @test dp1≈dp8 rtol=1e-10
+
+    # Larger Batched Problem: For testing the Iterative Solvers Path
+    u0 = zeros(128)
+    p = [2.0, 1.0]
+
+    prob = NonlinearProblem((u, p) -> u .- p[1] .+ p[2], u0, p)
+    solve1 = solve(remake(prob, p = p), NewtonRaphson())
+
+    function test_loss(p, prob; alg = NewtonRaphson())
+        _prob = remake(prob, p = p)
+        sol = sum(solve(_prob, alg,
+            sensealg = SteadyStateAdjoint(autojacvec = ZygoteVJP())))
+        return sol
+    end
+
+    test_loss(p, prob)
+
+    dp1 = Zygote.gradient(p -> test_loss(p, prob), p)[1]
+
+    @test dp1[1] ≈ 128
+    @test dp1[2] ≈ -128
 end
 
 @testset "Continuous sensitivity tools" begin

From ed284425cf45b1db2bfa62a8b083f28a92b97611 Mon Sep 17 00:00:00 2001
From: Christopher Rackauckas <accounts@chrisrackauckas.com>
Date: Mon, 28 Aug 2023 07:11:24 -0400
Subject: [PATCH 2/2] Update Project.toml

---
 Project.toml | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/Project.toml b/Project.toml
index 4e5567a42..c850551bf 100644
--- a/Project.toml
+++ b/Project.toml
@@ -73,7 +73,7 @@ ReverseDiff = "1.9"
 SciMLBase = "1.66.0"
 SciMLOperators = "0.1, 0.2, 0.3"
 SimpleNonlinearSolve = "0.1.8"
-SparseDiffTools = "2.4"
+SparseDiffTools = "2.5"
 StaticArraysCore = "1.4"
 StochasticDiffEq = "6.20"
 Tracker = "0.2"