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Add optimization parameters to OptimizationState #945

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Merged
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Jul 23, 2025
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Add optimization parameters to OptimizationState #945

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1 change: 1 addition & 0 deletions lib/OptimizationBBO/src/OptimizationBBO.jl
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Expand Up @@ -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)
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1 change: 1 addition & 0 deletions lib/OptimizationCMAEvolutionStrategy/src/OptimizationCMAEvolutionStrategy.jl
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Expand Up @@ -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)

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1 change: 1 addition & 0 deletions lib/OptimizationEvolutionary/src/OptimizationEvolutionary.jl
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Expand Up @@ -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...)
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1 change: 1 addition & 0 deletions lib/OptimizationMOI/src/nlp.jl
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Expand Up @@ -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
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1 change: 1 addition & 0 deletions lib/OptimizationManopt/src/OptimizationManopt.jl
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Expand Up @@ -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)
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2 changes: 1 addition & 1 deletion lib/OptimizationNLopt/src/OptimizationNLopt.jl
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Expand Up @@ -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
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2 changes: 1 addition & 1 deletion lib/OptimizationODE/src/OptimizationODE.jl
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Expand Up @@ -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!)
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3 changes: 3 additions & 0 deletions lib/OptimizationOptimJL/src/OptimizationOptimJL.jl
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Expand Up @@ -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),
Expand Down Expand Up @@ -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),
Expand Down Expand Up @@ -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,
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2 changes: 2 additions & 0 deletions lib/OptimizationOptimisers/src/OptimizationOptimisers.jl
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Expand Up @@ -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)
Expand All @@ -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)
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2 changes: 1 addition & 1 deletion lib/OptimizationPRIMA/src/OptimizationPRIMA.jl
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Expand Up @@ -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
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1 change: 1 addition & 0 deletions lib/OptimizationPyCMA/src/OptimizationPyCMA.jl
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Expand Up @@ -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)

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8 changes: 4 additions & 4 deletions lib/OptimizationSciPy/src/OptimizationSciPy.jl
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Expand Up @@ -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
Expand Down Expand Up @@ -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
Expand Down Expand Up @@ -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
Expand All @@ -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
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2 changes: 1 addition & 1 deletion src/auglag.jl
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Expand Up @@ -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
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4 changes: 2 additions & 2 deletions src/lbfgsb.jl
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Expand Up @@ -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
Expand Down Expand Up @@ -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
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3 changes: 2 additions & 1 deletion src/sophia.jl
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Expand Up @@ -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.")
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8 changes: 5 additions & 3 deletions src/state.jl
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Expand Up @@ -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
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