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Replaces Optimization dependency with OptimizationBase #88

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Oct 24, 2025
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Replaces Optimization dependency with OptimizationBase #88

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03f249f
Replaces Optimization dependency with OptimizationBase
nicholaskl97 Oct 22, 2025
f852d59
Use `[sources]` in Project.toml for NeuralLyapunovProblemLibrary
nicholaskl97 Oct 22, 2025
1289948
Update OptimizationBase compat to 2.1, 3 rather than 2, 3
nicholaskl97 Oct 22, 2025
233a9c8
Updated documentation CI for consistent versioning
nicholaskl97 Oct 22, 2025
2759f7b
Remove Pkg from Project.toml
nicholaskl97 Oct 24, 2025
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2 changes: 1 addition & 1 deletion .github/workflows/CI.yml
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Expand Up @@ -76,7 +76,7 @@ jobs:
- uses: actions/checkout@v5
- uses: julia-actions/setup-julia@v2
with:
version: '1'
version: '1.11'
- uses: julia-actions/cache@v2
- name: Configure doc environment
shell: julia --project=docs --color=yes {0}
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12 changes: 6 additions & 6 deletions Project.toml
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Expand Up @@ -15,14 +15,16 @@ LuxCore = "bb33d45b-7691-41d6-9220-0943567d0623"
MLDataDevices = "7e8f7934-dd98-4c1a-8fe8-92b47a384d40"
ModelingToolkit = "961ee093-0014-501f-94e3-6117800e7a78"
NeuralPDE = "315f7962-48a3-4962-8226-d0f33b1235f0"
Optimization = "7f7a1694-90dd-40f0-9382-eb1efda571ba"
OrdinaryDiffEq = "1dea7af3-3e70-54e6-95c3-0bf5283fa5ed"
QuasiMonteCarlo = "8a4e6c94-4038-4cdc-81c3-7e6ffdb2a71b"
SciMLBase = "0bca4576-84f4-4d90-8ffe-ffa030f20462"
StableRNGs = "860ef19b-820b-49d6-a774-d7a799459cd3"
SymbolicIndexingInterface = "2efcf032-c050-4f8e-a9bb-153293bab1f5"
Symbolics = "0c5d862f-8b57-4792-8d23-62f2024744c7"

[sources]
NeuralLyapunovProblemLibrary = {path = "lib/NeuralLyapunovProblemLibrary"}

[compat]
Aqua = "0.8.11"
Boltz = "1.2"
Expand All @@ -43,11 +45,10 @@ ModelingToolkit = "9.77"
NLopt = "1"
NeuralLyapunovProblemLibrary = "0.0.1"
NeuralPDE = "5.19.1"
Optimization = "4"
OptimizationBase = "2.1, 3"
OptimizationOptimJL = "0.4"
OptimizationOptimisers = "0.3"
OrdinaryDiffEq = "6.92"
Pkg = "1"
QuasiMonteCarlo = "0.3.3"
Random = "1"
SafeTestsets = "0.1"
Expand All @@ -67,13 +68,12 @@ ExplicitImports = "7d51a73a-1435-4ff3-83d9-f097790105c7"
LuxCUDA = "d0bbae9a-e099-4d5b-a835-1c6931763bda"
NLopt = "76087f3c-5699-56af-9a33-bf431cd00edd"
NeuralLyapunovProblemLibrary = "83723521-ca9c-4edc-8e64-505178d150fe"
Optimization = "7f7a1694-90dd-40f0-9382-eb1efda571ba"
OptimizationBase = "bca83a33-5cc9-4baa-983d-23429ab6bcbb"
OptimizationOptimJL = "36348300-93cb-4f02-beb5-3c3902f8871e"
OptimizationOptimisers = "42dfb2eb-d2b4-4451-abcd-913932933ac1"
Pkg = "44cfe95a-1eb2-52ea-b672-e2afdf69b78f"
Random = "9a3f8284-a2c9-5f02-9a11-845980a1fd5c"
SafeTestsets = "1bc83da4-3b8d-516f-aca4-4fe02f6d838f"
Test = "8dfed614-e22c-5e08-85e1-65c5234f0b40"

[targets]
test = ["SafeTestsets", "Test", "Optimization", "OptimizationOptimJL", "OptimizationOptimisers", "NeuralLyapunovProblemLibrary", "NLopt", "Random", "CSDP", "ComponentArrays", "LuxCUDA", "DiffEqGPU", "Aqua", "ExplicitImports", "Pkg"]
test = ["SafeTestsets", "Test", "OptimizationBase", "OptimizationOptimJL", "OptimizationOptimisers", "NeuralLyapunovProblemLibrary", "NLopt", "Random", "CSDP", "ComponentArrays", "LuxCUDA", "DiffEqGPU", "Aqua", "ExplicitImports"]
3 changes: 1 addition & 2 deletions src/NeuralLyapunov.jl
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Expand Up @@ -9,7 +9,7 @@ using ModelingToolkit: @named, @parameters, ODESystem, PDESystem, parameters, un
defaults, operation, unbound_inputs, defaults, structural_simplify
import SciMLBase
using SciMLBase: ODEFunction, ODEInputFunction, ODEProblem, solve, EnsembleProblem,
EnsembleThreads
EnsembleThreads, remake

using SymbolicIndexingInterface: SymbolCache, variable_symbols
using NeuralPDE: PhysicsInformedNN, discretize, LogOptions
Expand All @@ -22,7 +22,6 @@ using MLDataDevices: cpu_device
using Boltz.Layers: ShiftTo
using StableRNGs: StableRNG
using QuasiMonteCarlo: sample, LatinHypercubeSample
using Optimization: remake
using DataFrames: DataFrame

const cpud = cpu_device()
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2 changes: 1 addition & 1 deletion test/benchmark_CUDA.jl
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Expand Up @@ -173,7 +173,7 @@ end
function f(x, p, t)
pos = x[1]
vel = x[2]
return vcat(vel, -vel - pos)
return [vel, -vel - pos]
end
function f(dx, x, p, t)
pos = x[1]
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8 changes: 4 additions & 4 deletions test/damped_pendulum.jl
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@@ -1,6 +1,6 @@
using NeuralPDE, Lux, ComponentArrays, ModelingToolkit, NeuralLyapunov
import Boltz.Layers: PeriodicEmbedding, MLP
import Optimization
import OptimizationBase
using OptimizationOptimisers: Adam
using OptimizationOptimJL: BFGS
using StableRNGs, Random
Expand Down Expand Up @@ -86,9 +86,9 @@ prob = discretize(pde_system, discretization)

########################## Solve OptimizationProblem ##########################

res = Optimization.solve(prob, Adam(0.1); maxiters = 500)
prob = Optimization.remake(prob, u0 = res.u)
res = Optimization.solve(prob, BFGS(); maxiters = 500)
res = OptimizationBase.solve(prob, Adam(0.1); maxiters = 500)
prob = OptimizationBase.remake(prob, u0 = res.u)
res = OptimizationBase.solve(prob, BFGS(); maxiters = 500)

###################### Get numerical numerical functions ######################

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8 changes: 4 additions & 4 deletions test/damped_pendulum_lux.jl
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@@ -1,6 +1,6 @@
using NeuralPDE, Lux, ModelingToolkit, NeuralLyapunov, NeuralLyapunovProblemLibrary
import Boltz.Layers: PeriodicEmbedding, MLP
import Optimization
import OptimizationBase
using OptimizationOptimisers: Adam
using OptimizationOptimJL: BFGS
using StableRNGs, Random
Expand Down Expand Up @@ -71,9 +71,9 @@ prob = discretize(pde_system, discretization)

########################## Solve OptimizationProblem ##########################

res = Optimization.solve(prob, Adam(0.01f0); maxiters = 300)
prob = Optimization.remake(prob, u0 = res.u)
res = Optimization.solve(prob, BFGS(); maxiters = 300)
res = OptimizationBase.solve(prob, Adam(0.01f0); maxiters = 300)
prob = OptimizationBase.remake(prob, u0 = res.u)
res = OptimizationBase.solve(prob, BFGS(); maxiters = 300)

###################### Get numerical numerical functions ######################

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8 changes: 4 additions & 4 deletions test/damped_pendulum_lux_2.jl
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@@ -1,6 +1,6 @@
using NeuralPDE, Lux, ModelingToolkit, NeuralLyapunov, NeuralLyapunovProblemLibrary
import Boltz.Layers: PeriodicEmbedding, MLP
import Optimization
import OptimizationBase
using OptimizationOptimisers: Adam
using OptimizationOptimJL: BFGS
using StableRNGs, Random
Expand Down Expand Up @@ -70,9 +70,9 @@ prob = discretize(pde_system, discretization)

########################## Solve OptimizationProblem ##########################

res = Optimization.solve(prob, Adam(0.01f0); maxiters = 300)
prob = Optimization.remake(prob, u0 = res.u)
res = Optimization.solve(prob, BFGS(); maxiters = 300)
res = OptimizationBase.solve(prob, Adam(0.01f0); maxiters = 300)
prob = OptimizationBase.remake(prob, u0 = res.u)
res = OptimizationBase.solve(prob, BFGS(); maxiters = 300)

###################### Get numerical numerical functions ######################

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8 changes: 4 additions & 4 deletions test/damped_sho.jl
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@@ -1,6 +1,6 @@
using NeuralPDE, Lux, NeuralLyapunov, ComponentArrays
using Boltz.Layers: MLP
import Optimization
import OptimizationBase
using OptimizationOptimisers: Adam
using OptimizationOptimJL: BFGS
using StableRNGs, Random
Expand Down Expand Up @@ -63,9 +63,9 @@ sym_prob = symbolic_discretize(pde_system, discretization)

########################## Solve OptimizationProblem ##########################

res = Optimization.solve(prob, Adam(); maxiters = 450)
prob = Optimization.remake(prob, u0 = res.u)
res = Optimization.solve(prob, BFGS(); maxiters = 300)
res = OptimizationBase.solve(prob, Adam(); maxiters = 450)
prob = OptimizationBase.remake(prob, u0 = res.u)
res = OptimizationBase.solve(prob, BFGS(); maxiters = 300)

###################### Get numerical numerical functions ######################
(V, V̇) = get_numerical_lyapunov_function(
Expand Down
12 changes: 6 additions & 6 deletions test/damped_sho_CUDA.jl
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@@ -1,5 +1,5 @@
using NeuralPDE, NeuralLyapunov
import Optimization
import OptimizationBase
using OptimizationOptimisers: Adam
using OptimizationOptimJL: BFGS
using Random
Expand Down Expand Up @@ -62,11 +62,11 @@ sym_prob = symbolic_discretize(pde_system, discretization)

########################## Solve OptimizationProblem ##########################

res = Optimization.solve(prob, Adam(0.01f0); maxiters = 300)
prob = Optimization.remake(prob, u0 = res.u)
res = Optimization.solve(prob, Adam(); maxiters = 300)
prob = Optimization.remake(prob, u0 = res.u)
res = Optimization.solve(prob, BFGS(); maxiters = 300)
res = OptimizationBase.solve(prob, Adam(0.01f0); maxiters = 300)
prob = OptimizationBase.remake(prob, u0 = res.u)
res = OptimizationBase.solve(prob, Adam(); maxiters = 300)
prob = OptimizationBase.remake(prob, u0 = res.u)
res = OptimizationBase.solve(prob, BFGS(); maxiters = 300)

###################### Get numerical numerical functions ######################
(V, V̇) = get_numerical_lyapunov_function(
Expand Down
8 changes: 4 additions & 4 deletions test/inverted_pendulum.jl
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@@ -1,6 +1,6 @@
using NeuralPDE, Lux, NeuralLyapunov
import Boltz.Layers: PeriodicEmbedding, MLP
import Optimization
import OptimizationBase
using OptimizationOptimisers: Adam
using OptimizationOptimJL: BFGS
using StableRNGs, Random
Expand Down Expand Up @@ -88,9 +88,9 @@ prob = discretize(pde_system, discretization)

########################## Solve OptimizationProblem ##########################

res = Optimization.solve(prob, Adam(0.05f0); maxiters = 300)
prob = Optimization.remake(prob, u0 = res.u)
res = Optimization.solve(prob, BFGS(); maxiters = 300)
res = OptimizationBase.solve(prob, Adam(0.05f0); maxiters = 300)
prob = OptimizationBase.remake(prob, u0 = res.u)
res = OptimizationBase.solve(prob, BFGS(); maxiters = 300)

########################### Get numerical functions ###########################

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8 changes: 4 additions & 4 deletions test/inverted_pendulum_ODESystem.jl
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@@ -1,6 +1,6 @@
using NeuralPDE, Lux, ModelingToolkit, NeuralLyapunov, NeuralLyapunovProblemLibrary
import Boltz.Layers: PeriodicEmbedding, MLP
import Optimization
import OptimizationBase
using OptimizationOptimisers: Adam
using OptimizationOptimJL: BFGS
using StableRNGs, Random
Expand Down Expand Up @@ -82,9 +82,9 @@ prob = discretize(pde_system, discretization)

########################## Solve OptimizationProblem ##########################

res = Optimization.solve(prob, Adam(0.05f0); maxiters = 300)
prob = Optimization.remake(prob, u0 = res.u)
res = Optimization.solve(prob, BFGS(); maxiters = 300)
res = OptimizationBase.solve(prob, Adam(0.05f0); maxiters = 300)
prob = OptimizationBase.remake(prob, u0 = res.u)
res = OptimizationBase.solve(prob, BFGS(); maxiters = 300)

########################### Get numerical functions ###########################

Expand Down
8 changes: 4 additions & 4 deletions test/roa_estimation.jl
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@@ -1,5 +1,5 @@
using NeuralPDE, Lux, ComponentArrays, NeuralLyapunov
import Optimization
import OptimizationBase
using OptimizationOptimisers: Adam
using OptimizationOptimJL: BFGS
using Boltz.Layers: MLP
Expand Down Expand Up @@ -54,9 +54,9 @@ sym_prob = symbolic_discretize(pde_system, discretization)

########################## Solve OptimizationProblem ##########################

res = Optimization.solve(prob, Adam(); maxiters = 300)
prob = Optimization.remake(prob, u0 = res.u)
res = Optimization.solve(prob, BFGS(); maxiters = 300)
res = OptimizationBase.solve(prob, Adam(); maxiters = 300)
prob = OptimizationBase.remake(prob, u0 = res.u)
res = OptimizationBase.solve(prob, BFGS(); maxiters = 300)

###################### Get numerical numerical functions ######################
(V, V̇) = get_numerical_lyapunov_function(
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5 changes: 0 additions & 5 deletions test/runtests.jl
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@@ -1,10 +1,5 @@
using SafeTestsets

# Use the local version of NeuralLyapunovProblemLibrary
import Pkg
Pkg.develop(Pkg.PackageSpec(path = (pwd() * "/../lib/NeuralLyapunovProblemLibrary")))
Pkg.instantiate()

const GROUP = lowercase(get(ENV, "GROUP", "all"))
const DEVICE = lowercase(get(ENV, "DEVICE", "cpu"))

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