[docs] update to Julia v1.12

.github/workflows/documentation.yml

@@ -16,10 +16,10 @@ jobs:
         with:
           # Fix the Julia version, because Enzyme doesn't always like being updated
           # to `latest`.
-          version: '1.11'
+          version: '1.12'
       - uses: julia-actions/cache@v2
       - name: Install Gurobi license
-        env: 
+        env:
           SECRET_GUROBI_LICENSE: ${{ secrets.GUROBI_LICENSE }}
         shell: bash
         run: |

docs/.gitignore

@@ -9,5 +9,4 @@ src/release_notes.md
 src/packages/*.md
 !src/packages/solvers.md
 !src/packages/extensions.md
-!src/packages/NLopt.md
 src/JuMP.pdf

docs/Project.toml

@@ -54,33 +54,33 @@ Clarabel = "=0.11.0"
 Clustering = "0.15.8"
 DSP = "0.8.4"
 DataFrames = "1.8.1"
-DifferentiationInterface = "0.7.12"
+DifferentiationInterface = "0.7.13"
 DimensionalData = "=0.29.25"
-Distributions = "0.25.122"
+Distributions = "0.25.123"
 Documenter = "=1.15.0"
 DocumenterCitations = "1.4.1"
 Dualization = "0.7.1"
-Enzyme = "0.13.109"
-ForwardDiff = "1.3.0"
-Gurobi = "=1.9.0"
+Enzyme = "0.13.112"
+ForwardDiff = "1.3.1"
+Gurobi = "=1.9.1"
 HTTP = "1.10.19"
 HiGHS = "=1.20.1"
 Images = "0.26.2"
-Interpolations = "0.15"
+Interpolations = "0.16.2"
 Ipopt = "=1.13.0"
 JSON = "0.21.4"
 JSONSchema = "1.5.0"
-LinearOperatorCollection = "2.2.1"
+LinearOperatorCollection = "2.3.0"
 Literate = "2.21.0"
 MarkdownAST = "0.1.2"
 MathOptChordalDecomposition = "=0.2.0"
 MathOptInterface = "=1.48.0"
-MultiObjectiveAlgorithms = "=1.8.0"
+MultiObjectiveAlgorithms = "=1.8.1"
 PATHSolver = "=1.7.9"
 ParametricOptInterface = "0.14.1"
-Plots = "1.41.2"
+Plots = "1.41.3"
 RegularizedLeastSquares = "0.16.11"
-SCS = "=2.4.0"
+SCS = "=2.5.0"
 SQLite = "1.6.1"
 SpecialFunctions = "2.6.1"
 StatsPlots = "0.15.8"

docs/packages.toml

@@ -47,7 +47,7 @@
 [CSDP]
     rev = "v1.1.2"
 [cuOpt]
-    rev = "v0.1.1"
+    rev = "v0.1.2"
 [DiffOpt]
     rev = "v0.5.4"
     extension = true
@@ -61,7 +61,7 @@
 [GLPK]
     rev = "v1.2.1"
 [Gurobi]
-    rev = "v1.9.0"
+    rev = "v1.9.1"
 [HiGHS]
     rev = "v1.20.1"
 [Hypatia]
@@ -70,7 +70,7 @@
 [Ipopt]
     rev = "v1.13.0"
 [KNITRO]
-    rev = "v1.1.0"
+    rev = "v1.2.0"
 [MathOptAnalyzer]
     rev = "v0.1.1"
 [MathOptIIS]
@@ -80,7 +80,7 @@
 [MosekTools]
     rev = "v0.15.10"
 [MultiObjectiveAlgorithms]
-    rev = "3bf253221cfa943532b5030ab7d5bed6157b9786"
+    rev = "v1.8.1"
     has_html = true
 [NEOSServer]
     rev = "v1.2.0"
@@ -103,7 +103,7 @@
     rev = "v0.7.6"
     extension = true
 [SCS]
-    rev = "v2.4.0"
+    rev = "v2.5.0"
 [SDPA]
     rev = "v0.6.1"
 [SDPLR]
@@ -144,7 +144,7 @@
     has_html = true
 [COPT]
     user = "COPT-Public"
-    rev = "v1.1.30"
+    rev = "v1.1.31"
 [COSMO]
     user = "oxfordcontrol"
     rev = "v0.8.9"
@@ -159,7 +159,7 @@
     extension = true
 [EAGO]
     user = "PSORLab"
-    rev = "v0.8.3"
+    rev = "v0.8.4"
     filename = "docs/src/jump/README.md"
 [GAMS]
     user = "GAMS-dev"

docs/src/manual/containers.md

@@ -138,7 +138,7 @@ julia> DataFrames.DataFrame(table)
    6 │     2      3  (2, 3)
 ```
 
-## DenseAxisArray
+## ## [DenseAxisArray](@id manual_dense_axis_array)
 
 A [`Containers.DenseAxisArray`](@ref) is created when the index sets are
 rectangular, but not of the form `1:n`. The index sets can be of any type.
@@ -275,7 +275,7 @@ And data, a 2-element Vector{Tuple{Int64, Symbol}}:
  (2, :B)
 ```
 
-## SparseAxisArray
+## [SparseAxisArray](@id manual_sparse_axis_array)
 
 A [`Containers.SparseAxisArray`](@ref) is created when the index sets are
 non-rectangular. This occurs in two circumstances:

docs/src/manual/nlp.md

@@ -227,7 +227,7 @@ julia> @variable(model, y);
 julia> c = [1, 2];
 
 julia> @NLobjective(model, Min, c' * x + 3y)
-ERROR: Unexpected array [1 2] in nonlinear expression. Nonlinear expressions may contain only scalar expressions.
+ERROR: Unexpected array adjoint([1, 2]) in nonlinear expression. Nonlinear expressions may contain only scalar expressions.
 [...]
 ```
 

docs/src/manual/nonlinear.md

@@ -796,7 +796,7 @@ works.
 
 The operator takes `f(x::Vector)` as input, instead of the splatted `f(x...)`.
 
-```jldoctest
+```jldoctest; filter=r"\(::ForwardDiff.+\)"
 julia> import ForwardDiff
 
 julia> my_operator_bad(x::Vector) = sum(x[i]^2 for i in eachindex(x))
@@ -806,7 +806,7 @@ julia> my_operator_good(x...) = sum(x[i]^2 for i in eachindex(x))
 my_operator_good (generic function with 1 method)
 
 julia> ForwardDiff.gradient(x -> my_operator_bad(x...), [1.0, 2.0])
-ERROR: MethodError: no method matching my_operator_bad(::ForwardDiff.Dual{ForwardDiff.Tag{var"#5#6", Float64}, Float64, 2}, ::ForwardDiff.Dual{ForwardDiff.Tag{var"#5#6", Float64}, Float64, 2})
+ERROR: MethodError: no method matching my_operator_bad(::ForwardDiff.Dual, ::ForwardDiff.Dual)
 [...]
 
 julia> ForwardDiff.gradient(x -> my_operator_good(x...), [1.0, 2.0])
@@ -820,7 +820,7 @@ julia> ForwardDiff.gradient(x -> my_operator_good(x...), [1.0, 2.0])
 The operator assumes `Float64` will be passed as input, but it must work for any
 generic `Real` type.
 
-```jldoctest
+```jldoctest; filter=r"\(::ForwardDiff.+\)"
 julia> import ForwardDiff
 
 julia> my_operator_bad(x::Float64...) = sum(x[i]^2 for i in eachindex(x))
@@ -830,7 +830,7 @@ julia> my_operator_good(x::Real...) = sum(x[i]^2 for i in eachindex(x))
 my_operator_good (generic function with 1 method)
 
 julia> ForwardDiff.gradient(x -> my_operator_bad(x...), [1.0, 2.0])
-ERROR: MethodError: no method matching my_operator_bad(::ForwardDiff.Dual{ForwardDiff.Tag{var"#5#6", Float64}, Float64, 2}, ::ForwardDiff.Dual{ForwardDiff.Tag{var"#5#6", Float64}, Float64, 2})
+ERROR: MethodError: no method matching my_operator_bad(::ForwardDiff.Dual, ::ForwardDiff.Dual)
 [...]
 
 julia> ForwardDiff.gradient(x -> my_operator_good(x...), [1.0, 2.0])

docs/src/tutorials/algorithms/pdhg.jl

@@ -255,7 +255,7 @@ end
 
 MOI.get(::Optimizer, ::MOI.SolverName) = "PDHG"
 
-# ### GenericModel
+# ### [GenericModel](@id tutorial_pdhg_generic_model)
 
 # The simplest way to solve a problem with your optimizer is to implement the
 # method `MOI.optimize!(dest::Optimizer, src::MOI.ModelLike)`, where `src` is an