Add optimization parameters to OptimizationState

ChrisRackauckas · claude · ChrisRackauckas · commit 0d9efc2b5988 · 2025-07-22T21:42:37.000-04:00
Fixes #891 - Added a `p` field to the OptimizationState struct to provide access to optimization parameters in callbacks. This enables use cases like tracking loss function progression in multi-start optimization scenarios. Changes: - Modified OptimizationState struct to include `p` parameter field - Updated constructor to accept optional `p` parameter - Updated all OptimizationState construction calls across the codebase to pass the appropriate parameter values (cache.p, d, etc.) - Maintains backward compatibility with existing code 🤖 Generated with [Claude Code](https://claude.ai/code) Co-Authored-By: Claude <noreply@anthropic.com>
diff --git a/lib/OptimizationBBO/src/OptimizationBBO.jl b/lib/OptimizationBBO/src/OptimizationBBO.jl
@@ -126,6 +126,7 @@ function SciMLBase.__solve(cache::Optimization.OptimizationCache{
             opt_state = Optimization.OptimizationState(;
                 iter = n_steps,
                 u = curr_u,
+                p = cache.p,
                 objective,
                 original = trace)
             cb_call = cache.callback(opt_state, objective)
diff --git a/lib/OptimizationCMAEvolutionStrategy/src/OptimizationCMAEvolutionStrategy.jl b/lib/OptimizationCMAEvolutionStrategy/src/OptimizationCMAEvolutionStrategy.jl
@@ -78,6 +78,7 @@ function SciMLBase.__solve(cache::OptimizationCache{
         curr_u = opt.logger.xbest[end]
         opt_state = Optimization.OptimizationState(; iter = length(opt.logger.fmedian),
             u = curr_u,
+            p = cache.p,
             objective = opt.logger.fbest[end],
             original = opt.logger)
 
diff --git a/lib/OptimizationEvolutionary/src/OptimizationEvolutionary.jl b/lib/OptimizationEvolutionary/src/OptimizationEvolutionary.jl
@@ -104,6 +104,7 @@ function SciMLBase.__solve(cache::OptimizationCache{
         opt_state = Optimization.OptimizationState(;
             iter = decompose_trace(trace).iteration,
             u = curr_u,
+            p = cache.p,
             objective = x[1],
             original = trace)
         cb_call = cache.callback(opt_state, decompose_trace(trace).value...)
diff --git a/lib/OptimizationMOI/src/nlp.jl b/lib/OptimizationMOI/src/nlp.jl
@@ -239,6 +239,7 @@ function MOI.eval_objective(evaluator::MOIOptimizationNLPEvaluator, x)
         evaluator.iteration += 1
         state = Optimization.OptimizationState(iter = evaluator.iteration,
             u = x,
+            p = evaluator.p,
             objective = l[1])
         evaluator.callback(state, l)
         return l
diff --git a/lib/OptimizationManopt/src/OptimizationManopt.jl b/lib/OptimizationManopt/src/OptimizationManopt.jl
@@ -416,6 +416,7 @@ function SciMLBase.__solve(cache::OptimizationCache{
     function _cb(x, θ)
         opt_state = Optimization.OptimizationState(iter = 0,
             u = θ,
+            p = cache.p,
             objective = x[1])
         cb_call = cache.callback(opt_state, x...)
         if !(cb_call isa Bool)
diff --git a/lib/OptimizationNLopt/src/OptimizationNLopt.jl b/lib/OptimizationNLopt/src/OptimizationNLopt.jl
@@ -156,7 +156,7 @@ function SciMLBase.__solve(cache::OptimizationCache{
 
     _loss = function (θ)
         x = cache.f(θ, cache.p)
-        opt_state = Optimization.OptimizationState(u = θ, objective = x[1])
+        opt_state = Optimization.OptimizationState(u = θ, p = cache.p, objective = x[1])
         if cache.callback(opt_state, x...)
             NLopt.force_stop!(opt_setup)
         end
diff --git a/lib/OptimizationODE/src/OptimizationODE.jl b/lib/OptimizationODE/src/OptimizationODE.jl
@@ -68,7 +68,7 @@ function SciMLBase.__solve(
         end
         function affect!(integrator)
             u_now = integrator.u
-            state = Optimization.OptimizationState(u=u_now, objective=cache.f(integrator.u, integrator.p))
+            state = Optimization.OptimizationState(u=u_now, p=integrator.p, objective=cache.f(integrator.u, integrator.p))
             Optimization.callback_function(cb, state)
         end
         cb_struct = DiscreteCallback(condition, affect!)
diff --git a/lib/OptimizationOptimJL/src/OptimizationOptimJL.jl b/lib/OptimizationOptimJL/src/OptimizationOptimJL.jl
@@ -143,6 +143,7 @@ function SciMLBase.__solve(cache::OptimizationCache{
         θ = metadata[cache.opt isa Optim.NelderMead ? "centroid" : "x"]
         opt_state = Optimization.OptimizationState(iter = trace.iteration,
             u = θ,
+            p = cache.p,
             objective = trace.value,
             grad = get(metadata, "g(x)", nothing),
             hess = get(metadata, "h(x)", nothing),
@@ -262,6 +263,7 @@ function SciMLBase.__solve(cache::OptimizationCache{
             metadata["x"]
         opt_state = Optimization.OptimizationState(iter = trace.iteration,
             u = θ,
+            p = cache.p,
             objective = trace.value,
             grad = get(metadata, "g(x)", nothing),
             hess = get(metadata, "h(x)", nothing),
@@ -348,6 +350,7 @@ function SciMLBase.__solve(cache::OptimizationCache{
         metadata = decompose_trace(trace).metadata
         opt_state = Optimization.OptimizationState(iter = trace.iteration,
             u = metadata["x"],
+            p = cache.p,
             grad = get(metadata, "g(x)", nothing),
             hess = get(metadata, "h(x)", nothing),
             objective = trace.value,
diff --git a/lib/OptimizationOptimisers/src/OptimizationOptimisers.jl b/lib/OptimizationOptimisers/src/OptimizationOptimisers.jl
@@ -121,6 +121,7 @@ function SciMLBase.__solve(cache::OptimizationCache{
                 opt_state = Optimization.OptimizationState(
                     iter = i + (epoch - 1) * length(data),
                     u = θ,
+                    p = d,
                     objective = x[1],
                     grad = G,
                     original = state)
@@ -146,6 +147,7 @@ function SciMLBase.__solve(cache::OptimizationCache{
                         cache.f.grad(G, θ, d)
                         opt_state = Optimization.OptimizationState(iter = iterations,
                             u = θ,
+                            p = d,
                             objective = x[1],
                             grad = G,
                             original = state)
diff --git a/lib/OptimizationPRIMA/src/OptimizationPRIMA.jl b/lib/OptimizationPRIMA/src/OptimizationPRIMA.jl
@@ -133,7 +133,7 @@ function SciMLBase.__solve(cache::Optimization.OptimizationCache{
     _loss = function (θ)
         x = cache.f(θ, cache.p)
         iter += 1
-        opt_state = Optimization.OptimizationState(u = θ, objective = x[1], iter = iter)
+        opt_state = Optimization.OptimizationState(u = θ, p = cache.p, objective = x[1], iter = iter)
         if cache.callback(opt_state, x...)
             error("Optimization halted by callback.")
         end
diff --git a/lib/OptimizationPyCMA/src/OptimizationPyCMA.jl b/lib/OptimizationPyCMA/src/OptimizationPyCMA.jl
@@ -125,6 +125,7 @@ function SciMLBase.__solve(cache::OptimizationCache{
     _cb = function(es)
         opt_state = Optimization.OptimizationState(; iter = pyconvert(Int, es.countiter),
             u = pyconvert(Vector{Float64}, es.best.x),
+            p = cache.p,
             objective = pyconvert(Float64, es.best.f),
             original = es)
 
diff --git a/lib/OptimizationSciPy/src/OptimizationSciPy.jl b/lib/OptimizationSciPy/src/OptimizationSciPy.jl
@@ -503,7 +503,7 @@ function SciMLBase.__solve(cache::OptimizationCache{F,RC,LB,UB,LC,UC,S,O,D,P,C})
         θ_vec = [θ]
         x = cache.f(θ_vec, cache.p)
         x = isa(x, Tuple) ? x : (x,)
-        opt_state = Optimization.OptimizationState(u = θ_vec, objective = x[1])
+        opt_state = Optimization.OptimizationState(u = θ_vec, p = cache.p, objective = x[1])
         if cache.callback(opt_state, x...)
             error("Optimization halted by callback")
         end
@@ -656,7 +656,7 @@ function SciMLBase.__solve(cache::OptimizationCache{F,RC,LB,UB,LC,UC,S,O,D,P,C})
         θ_vec = [θ]
         x = cache.f(θ_vec, cache.p)
         x = isa(x, Tuple) ? x : (x,)
-        opt_state = Optimization.OptimizationState(u = θ_vec, objective = x[1])
+        opt_state = Optimization.OptimizationState(u = θ_vec, p = cache.p, objective = x[1])
         if cache.callback(opt_state, x...)
             error("Optimization halted by callback")
         end
@@ -1423,7 +1423,7 @@ function _create_loss(cache; vector_output::Bool = false)
             elseif isa(x, Number)
                 x = (x,)
             end
-            opt_state = Optimization.OptimizationState(u = θ_julia, objective = sum(abs2, x))
+            opt_state = Optimization.OptimizationState(u = θ_julia, p = cache.p, objective = sum(abs2, x))
             if cache.callback(opt_state, x...)
                 error("Optimization halted by callback")
             end
@@ -1443,7 +1443,7 @@ function _create_loss(cache; vector_output::Bool = false)
             elseif isa(x, Number)
                 x = (x,)
             end
-            opt_state = Optimization.OptimizationState(u = θ_julia, objective = x[1])
+            opt_state = Optimization.OptimizationState(u = θ_julia, p = cache.p, objective = x[1])
             if cache.callback(opt_state, x...)
                 error("Optimization halted by callback")
             end
diff --git a/src/auglag.jl b/src/auglag.jl
@@ -105,7 +105,7 @@ function SciMLBase.__solve(cache::OptimizationCache{
             cache.f.cons(cons_tmp, θ)
             cons_tmp[eq_inds] .= cons_tmp[eq_inds] - cache.lcons[eq_inds]
             cons_tmp[ineq_inds] .= cons_tmp[ineq_inds] .- cache.ucons[ineq_inds]
-            opt_state = Optimization.OptimizationState(u = θ, objective = x[1])
+            opt_state = Optimization.OptimizationState(u = θ, objective = x[1], p = p)
             if cache.callback(opt_state, x...)
                 error("Optimization halted by callback.")
             end
diff --git a/src/lbfgsb.jl b/src/lbfgsb.jl
@@ -122,7 +122,7 @@ function SciMLBase.__solve(cache::OptimizationCache{
             cache.f.cons(cons_tmp, θ)
             cons_tmp[eq_inds] .= cons_tmp[eq_inds] - cache.lcons[eq_inds]
             cons_tmp[ineq_inds] .= cons_tmp[ineq_inds] .- cache.ucons[ineq_inds]
-            opt_state = Optimization.OptimizationState(u = θ, objective = x[1])
+            opt_state = Optimization.OptimizationState(u = θ, objective = x[1], p = cache.p)
             if cache.callback(opt_state, x...)
                 error("Optimization halted by callback.")
             end
@@ -209,7 +209,7 @@ function SciMLBase.__solve(cache::OptimizationCache{
         _loss = function (θ)
             x = cache.f(θ, cache.p)
 
-            opt_state = Optimization.OptimizationState(u = θ, objective = x[1])
+            opt_state = Optimization.OptimizationState(u = θ, objective = x[1], p = cache.p)
             if cache.callback(opt_state, x...)
                 error("Optimization halted by callback.")
             end
diff --git a/src/sophia.jl b/src/sophia.jl
@@ -93,7 +93,8 @@ function SciMLBase.__solve(cache::OptimizationCache{
                 u = θ,
                 objective = first(x),
                 grad = gₜ,
-                original = nothing)
+                original = nothing,
+                p = d)
             cb_call = cache.callback(opt_state, x...)
             if !(cb_call isa Bool)
                 error("The callback should return a boolean `halt` for whether to stop the optimization process. Please see the sciml_train documentation for information.")
diff --git a/src/state.jl b/src/state.jl
@@ -11,17 +11,19 @@ and is passed to the callback function as the first argument.
 - `gradient`: current gradient
 - `hessian`: current hessian
 - `original`: if the solver has its own state object then it is stored here
+- `p`: optimization parameters
 """
-struct OptimizationState{X, O, G, H, S}
+struct OptimizationState{X, O, G, H, S, P}
     iter::Int
     u::X
     objective::O
     grad::G
     hess::H
     original::S
+    p::P
 end
 
 function OptimizationState(; iter = 0, u = nothing, objective = nothing,
-        grad = nothing, hess = nothing, original = nothing)
-    OptimizationState(iter, u, objective, grad, hess, original)
+        grad = nothing, hess = nothing, original = nothing, p = nothing)
+    OptimizationState(iter, u, objective, grad, hess, original, p)
 end
