[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: |

.vale.ini

@@ -63,6 +63,9 @@ Google.Quotes = NO
 Google.FirstPerson = NO
 Vale.Spelling = NO
 
+[docs/src/packages/MultiObjectiveAlgorithms.md]
+Vale.Spelling = NO
+
 [docs/src/packages/Optim.md]
 Google.EmDash = NO
 

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"
-Documenter = "=1.15.0"
+Distributions = "0.25.123"
+Documenter = "=1.16.1"
 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/make.jl

@@ -560,17 +560,24 @@ function _add_moi_pages()
             end
         end
     end
-    # Fix `# Infeasibility certificates` in moi/background/infeasibility_certificates.md
-    filename = joinpath(moi_dir, "background", "infeasibility_certificates.md")
-    contents = read(filename, String)
-    id = "# [Infeasibility certificates](@id moi_infeasibility_certificates)"
-    contents = replace(contents, r"^# Infeasibility certificates$"m => id)
-    write(filename, contents)
-    # Fix `JSON.Object` in moi/submodules/FileFormats/overview.md
-    # This can be removed once we support JSON@1 in the documentation
-    filename = joinpath(moi_dir, "submodules", "FileFormats", "overview.md")
-    contents = read(filename, String)
-    write(filename, replace(contents, "JSON.Object" => "Dict"))
+    for (filename, replacements) in [
+        "background/infeasibility_certificates.md" => [
+            r"^# Infeasibility certificates$"m => "# [Infeasibility certificates](@id moi_infeasibility_certificates)",
+        ],
+        "reference/models.md" => ["# ResultStatusCode" => "# Result Status"],
+        # This can be removed once we support JSON@1 in the documentation
+        "submodules/FileFormats/overview.md" => ["JSON.Object" => "Dict"],
+        # These can be removed once we support [email protected] or later
+        "reference/models.md" => ["# ResultStatusCode" => "# Result Status"],
+        "submodules/Nonlinear/SymbolicAD.md" => [
+            "# `simplify`" => "# [`simplify`](@id symbolic_ad_manual_simplify)",
+            "# `variables`" => "# [`variables`](@id symbolic_ad_manual_variables)",
+            "# `derivative`" => "# [`derivative`](@id symbolic_ad_manual_derivative)",
+        ],
+    ]
+        filename = joinpath(moi_dir, filename)
+        write(filename, replace(read(filename, String), replacements...))
+    end
     return
 end
 

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

docs/src/tutorials/applications/web_app.jl

@@ -71,47 +71,30 @@ endpoint_solve(Dict{String,Any}("lower_bound" => 1.2))
 
 endpoint_solve(Dict{String,Any}())
 
-# For a second function, we need a function that accepts an `HTTP.Request`
-# object and returns an `HTTP.Response` object.
-
-function serve_solve(request::HTTP.Request)
-    data = JSON.parse(String(request.body))
-    solution = endpoint_solve(data)
-    return HTTP.Response(200, JSON.json(solution))
-end
-
-# Finally, we need an HTTP server. There are a variety of ways you can do this
-# in HTTP.jl. We use an explicit `Sockets.listen` so we have manual control of
-# when we shutdown the server.
-
-function setup_server(host, port)
-    server = HTTP.Sockets.listen(host, port)
-    HTTP.serve!(host, port; server = server) do request
-        try
-            ## Extend the server by adding other endpoints here.
-            if request.target == "/api/solve"
-                return serve_solve(request)
-            else
-                return HTTP.Response(404, "target $(request.target) not found")
-            end
+# We now need to turn each endpoint into a function that accepts an
+# `HTTP.Request`, parses the JSON input, runs the endpoint, converts the result
+# to JSON, and returns an `HTTP.Response`. In addition, the computation is
+# handled in a separate thread, and we catch any unhandled exceptions.
+
+function wrap_endpoint(endpoint::Function)
+    function serve_request(request::HTTP.Request)::HTTP.Response
+        task = Threads.@spawn try
+            ret = request.body |> String |> JSON.parse |> endpoint |> JSON.json
+            HTTP.Response(200, ret)
         catch err
-            ## Log details about the exception server-side
-            @info "Unhandled exception: $err"
-            ## Return a response to the client
-            return HTTP.Response(500, "internal error")
+            HTTP.Response(500, "internal error: $err")
         end
+        return fetch(task)
     end
-    return server
 end
 
-# !!! warning
-#     HTTP.jl does not serve requests on a separate thread. Therefore, a
-#     long-running job will block the main thread, preventing concurrent users from
-#     submitting requests. To work-around this, read [HTTP.jl issue 798](https://github.com/JuliaWeb/HTTP.jl/issues/798)
-#     or watch [Building Microservices and Applications in Julia](https://www.youtube.com/watch?v=uLhXgt_gKJc&t=9543s)
-#     from JuliaCon 2020.
+# Finally, we need an HTTP server. There are a variety of ways you can do this
+# in HTTP.jl. Here's one way:
 
-server = setup_server(HTTP.ip"127.0.0.1", 8080)
+router = HTTP.Router()
+## Register other routes as needed
+HTTP.register!(router, "/api/solve", wrap_endpoint(endpoint_solve))
+server = HTTP.serve!(router, HTTP.ip"127.0.0.1", 8080)
 
 # ## The client side
 

src/JuMP.jl

@@ -1131,7 +1131,7 @@ julia> set_optimize_hook(model, my_hook)
 my_hook (generic function with 1 method)
 
 julia> optimize!(model; test_arg = true)
-Base.Pairs{Symbol, Bool, Tuple{Symbol}, @NamedTuple{test_arg::Bool}}(:test_arg => 1)
+Base.Pairs{Symbol, Bool, Nothing, @NamedTuple{test_arg::Bool}}(:test_arg => 1)
 Calling with `ignore_optimize_hook = true`
 ERROR: NoOptimizer()
 [...]

src/macros/@force_nonlinear.jl

@@ -84,10 +84,10 @@ julia> @expression(model, @force_nonlinear(x * 2.0 * (1 + x) * x))
 x * 2 * (1 + x) * x
 
 julia> @allocated @expression(model, x * 2.0 * (1 + x) * x)
-3680
+3264
 
 julia> @allocated @expression(model, @force_nonlinear(x * 2.0 * (1 + x) * x))
-768
+944
 ```
 """
 macro force_nonlinear(expr)