Merge branch 'master' into patch-1

Author: ChrisRackauckas

.github/workflows/CI.yml

@@ -41,6 +41,8 @@ jobs:
           - OptimizationNLPModels
         version:
           - '1'
+          - 'lts'
+          - 'pre'
     steps:
       - uses: actions/checkout@v4
       - uses: julia-actions/setup-julia@v2

.github/workflows/Downgrade.yml

@@ -0,0 +1,34 @@
+name: Downgrade
+on:
+  pull_request:
+    branches:
+      - master
+    paths-ignore:
+      - 'docs/**'
+  push:
+    branches:
+      - master
+    paths-ignore:
+      - 'docs/**'
+jobs:
+  test:
+    runs-on: ubuntu-latest
+    strategy:
+      matrix:
+        downgrade_mode: ['alldeps']
+        julia-version: ['1.10']
+        group: ['Core']
+    steps:
+      - uses: actions/checkout@v4
+      - uses: julia-actions/setup-julia@v2
+        with:
+          version: ${{ matrix.julia-version }}
+      - uses: julia-actions/julia-downgrade-compat@v2
+        with:
+          skip: Pkg,TOML
+      - uses: julia-actions/julia-buildpkg@v1
+      - uses: julia-actions/julia-runtest@v1
+        with:
+          ALLOW_RERESOLVE: false
+        env:
+          GROUP: ${{ matrix.group }}

.github/workflows/DowngradeSublibraries.yml

@@ -0,0 +1,57 @@
+name: Downgrade Sublibraries
+on:
+  pull_request:
+    branches:
+      - master
+    paths-ignore:
+      - 'docs/**'
+  push:
+    branches:
+      - master
+    paths-ignore:
+      - 'docs/**'
+jobs:
+  test:
+    runs-on: ubuntu-latest
+    strategy:
+      fail-fast: false
+      matrix:
+        downgrade_mode: ['alldeps']
+        julia-version: ['1.10']
+        project:
+          - 'lib/OptimizationBBO'
+          - 'lib/OptimizationCMAEvolutionStrategy'
+          - 'lib/OptimizationEvolutionary'
+          - 'lib/OptimizationGCMAES'
+          - 'lib/OptimizationMOI'
+          - 'lib/OptimizationManopt'
+          - 'lib/OptimizationMetaheuristics'
+          - 'lib/OptimizationMultistartOptimization'
+          - 'lib/OptimizationNLPModels'
+          - 'lib/OptimizationNLopt'
+          - 'lib/OptimizationNOMAD'
+          - 'lib/OptimizationODE'
+          - 'lib/OptimizationOptimJL'
+          - 'lib/OptimizationOptimisers'
+          - 'lib/OptimizationPRIMA'
+          - 'lib/OptimizationPolyalgorithms'
+          - 'lib/OptimizationPyCMA'
+          - 'lib/OptimizationQuadDIRECT'
+          - 'lib/OptimizationSciPy'
+          - 'lib/OptimizationSpeedMapping'
+    steps:
+      - uses: actions/checkout@v4
+      - uses: julia-actions/setup-julia@v2
+        with:
+          version: ${{ matrix.julia-version }}
+      - uses: julia-actions/julia-downgrade-compat@v2
+        with:
+          project: ${{ matrix.project }}
+          skip: Pkg,TOML
+      - uses: julia-actions/julia-buildpkg@v1
+        with:
+          project: ${{ matrix.project }}
+      - uses: julia-actions/julia-runtest@v1
+        with:
+          project: ${{ matrix.project }}
+          ALLOW_RERESOLVE: false

.github/workflows/Invalidations.yml

@@ -1,2 +1,79 @@
 [default.extend-words]
-AGS = "AGS"
+# Julia-specific functions  
+indexin = "indexin"
+findfirst = "findfirst"
+findlast = "findlast"
+eachindex = "eachindex"
+setp = "setp"
+getp = "getp"
+setu = "setu"
+getu = "getu"
+
+# Mathematical/scientific terms
+jacobian = "jacobian"
+hessian = "hessian" 
+eigenvalue = "eigenvalue"
+eigenvector = "eigenvector"
+discretization = "discretization"
+linearization = "linearization"
+parameterized = "parameterized"
+discretized = "discretized"
+vectorized = "vectorized"
+
+# Common variable patterns in Julia/SciML
+ists = "ists"
+ispcs = "ispcs"
+osys = "osys"
+rsys = "rsys"
+usys = "usys"
+fsys = "fsys"
+eqs = "eqs"
+rhs = "rhs"
+lhs = "lhs"
+ode = "ode"
+pde = "pde"
+sde = "sde"
+dde = "dde"
+bvp = "bvp"
+ivp = "ivp"
+
+# Common abbreviations
+tol = "tol"
+rtol = "rtol"
+atol = "atol"
+idx = "idx"
+jdx = "jdx"
+prev = "prev"
+curr = "curr"
+init = "init"
+tmp = "tmp"
+vec = "vec"
+arr = "arr"
+dt = "dt"
+du = "du"
+dx = "dx"
+dy = "dy"
+dz = "dz"
+
+# Algorithm/type suffixes
+alg = "alg"
+prob = "prob"
+sol = "sol"
+cb = "cb"
+opts = "opts"
+args = "args"
+kwargs = "kwargs"
+
+# Scientific abbreviations
+ND = "ND"
+nd = "nd"
+MTK = "MTK"
+ODE = "ODE"
+PDE = "PDE"
+SDE = "SDE"
+
+# Optimization specific terms
+TikTak = "TikTak"  # Legitimate algorithm name in MultistartOptimization
+Tak = "Tak"  # Part of TikTak algorithm name
+opf = "opf"  # Optimal Power Flow abbreviation
+AGS = "AGS"  # Legitimate NLopt algorithm name

.typos.toml

@@ -1,6 +1,6 @@
 name = "Optimization"
 uuid = "7f7a1694-90dd-40f0-9382-eb1efda571ba"
-version = "4.4.0"
+version = "4.5.0"
 
 [deps]
 ADTypes = "47edcb42-4c32-4615-8424-f2b9edc5f35b"
@@ -41,7 +41,7 @@ Logging = "1.10"
 LoggingExtras = "0.4, 1"
 Lux = "1.12.4"
 MLUtils = "0.4"
-ModelingToolkit = "9"
+ModelingToolkit = "10"
 Mooncake = "0.4.138"
 Optim = ">= 1.4.1"
 OptimizationBase = "2"
@@ -56,7 +56,7 @@ Random = "1.10"
 Reexport = "1.2"
 ReverseDiff = "1"
 SafeTestsets = "0.1"
-SciMLBase = "2.39.0"
+SciMLBase = "2.82"
 SciMLSensitivity = "7"
 SparseArrays = "1.10"
 SparseDiffTools = "2"

Project.toml

@@ -7,6 +7,7 @@ pages = ["index.md",
         "tutorials/linearandinteger.md",
         "tutorials/minibatch.md",
         "tutorials/remakecomposition.md",
+        "tutorials/reusage_interface.md",
         "tutorials/symbolic.md"
     ],
     "Examples" => [
@@ -23,7 +24,7 @@ pages = ["index.md",
         "API/modelingtoolkit.md",
         "API/FAQ.md"
     ],
-    "Optimizer Packages" => [   
+    "Optimizer Packages" => [
         "BlackBoxOptim.jl" => "optimization_packages/blackboxoptim.md",
         "CMAEvolutionStrategy.jl" => "optimization_packages/cmaevolutionstrategy.md",
         "Evolutionary.jl" => "optimization_packages/evolutionary.md",

docs/pages.jl

@@ -37,7 +37,7 @@ Tada! That's how you do it. Now let's dive in a little more into what each part
 
 ## Understanding the Solution Object
 
-The solution object is a `SciMLBase.AbstractNoTimeSolution`, and thus it follows the 
+The solution object is a `SciMLBase.AbstractNoTimeSolution`, and thus it follows the
 [SciMLBase Solution Interface for non-timeseries objects](https://docs.sciml.ai/SciMLBase/stable/interfaces/Solutions/) and is documented at the [solution type page](@ref solution).
 However, for simplicity let's show a bit of it in action.
 
@@ -61,13 +61,13 @@ rosenbrock(sol.u, p)
 sol.objective
 ```
 
-The `sol.retcode` gives us more information about the solution process. 
+The `sol.retcode` gives us more information about the solution process.
 
 ```@example intro
 sol.retcode
 ```
 
-Here it says `ReturnCode.Success` which means that the solutuion successfully solved. We can learn more about the different return codes at 
+Here it says `ReturnCode.Success` which means that the solutuion successfully solved. We can learn more about the different return codes at
 [the ReturnCode part of the SciMLBase documentation](https://docs.sciml.ai/SciMLBase/stable/interfaces/Solutions/#retcodes).
 
 If we are interested about some of the statistics of the solving process, for example to help choose a better solver, we can investigate the `sol.stats`

docs/src/getting_started.md

@@ -76,17 +76,18 @@ ps_ca = ComponentArray(ps)
 smodel = StatefulLuxLayer{true}(model, nothing, st)
 
 function callback(state, l)
-    state.iter % 25 == 1 && @show "Iteration: %5d, Loss: %.6e\n" state.iter l
+    state.iter % 25 == 1 && @show "Iteration: $(state.iter), Loss: $l"
     return l < 1e-1 ## Terminate if loss is small
 end
 
 function loss(ps, data)
-    ypred = [smodel([data[1][i]], ps)[1] for i in eachindex(data[1])]
-    return sum(abs2, ypred .- data[2])
+    x_batch, y_batch = data
+    ypred = [smodel([x_batch[i]], ps)[1] for i in eachindex(x_batch)]
+    return sum(abs2, ypred .- y_batch)
 end
 
 optf = OptimizationFunction(loss, AutoZygote())
 prob = OptimizationProblem(optf, ps_ca, data)
 
-res = Optimization.solve(prob, Optimization.Sophia(), callback = callback)
+res = Optimization.solve(prob, Optimization.Sophia(), callback = callback, epochs = 100)
 ```

docs/src/optimization_packages/optimization.md

@@ -31,9 +31,9 @@ sol = solve(prob, PyCMAOpt())
 
 ## Passing solver-specific options
 
-Any keyword that `Optimization.jl` does not interpret is forwarded directly to PyCMA. 
+Any keyword that `Optimization.jl` does not interpret is forwarded directly to PyCMA.
 
-In the event an `Optimization.jl` keyword overlaps with a `PyCMA` keyword, the `Optimization.jl` keyword takes precedence. 
+In the event an `Optimization.jl` keyword overlaps with a `PyCMA` keyword, the `Optimization.jl` keyword takes precedence.
 
 An exhaustive list of keyword arguments can be found by running the following python script:
 
@@ -44,6 +44,7 @@ print(options)
 ```
 
 An example passing the `PyCMA` keywords "verbose" and "seed":
+
 ```julia
 sol = solve(prob, PyCMA(), verbose = -9, seed = 42)
 ```
@@ -54,10 +55,9 @@ The original Python result object is attached to the solution in the `original`
 
 ```julia
 sol = solve(prob, PyCMAOpt())
-println(sol.original) 
+println(sol.original)
 ```
 
 ## Contributing
 
-Bug reports and feature requests are welcome in the [Optimization.jl](https://github.com/SciML/Optimization.jl) issue tracker.  Pull requests that improve either the Julia wrapper or the documentation are highly appreciated. 
-
+Bug reports and feature requests are welcome in the [Optimization.jl](https://github.com/SciML/Optimization.jl) issue tracker.  Pull requests that improve either the Julia wrapper or the documentation are highly appreciated.