Prep for v1.31.0

[odow]

TODO

• [docs] update package versions #4187
• Restore uncached version of moi_function #4189
• Fix checking if problem is an LP in lp_sensitivity_report #4199
• Clean up check_belongs_to_model methods #4200

Pre-release

• Check that the pinned packages in docs/Project.toml are updated. We pin
  the versions so that changes in the solvers (changes in printing, small
  numeric changes) do not break the printing of the JuMP docs in arbitrary
  commits.
• Check that the rev fields in docs/packages.toml are updated. We pin
  the versions of solvers and extensions to ensure that changes to their
  READMEs do not break the JuMP docs in arbitrary commits, and to ensure
  that the versions are compatible with the latest JuMP and
  MathOptInterface releases.
• Check compat of DimensionalData in Project.toml
• Check compat of MacroTools in Project.toml
• Update docs/src/changelog.md
• https://github.com/jump-dev/JuMP.jl/actions/runs/28484006371
• https://github.com/jump-dev/JuMP.jl/actions/runs/28839389542
• https://github.com/jump-dev/JuMP.jl/actions/runs/29485043758
• https://github.com/jump-dev/JuMP.jl/actions/runs/29712517128
• Change the version number in Project.toml
• The commit messages in this PR do not contain [ci skip]

The release

• After merging this pull request, comment [at]JuliaRegistrator register in
  the GitHub commit. This should automatically publish a new version to the
  Julia registry, as well as create a tag, and rebuild the documentation
  for this tag.

  These steps can take quite a bit of time (1 hour or more), so don't be
  surprised if the new documentation takes a while to appear. In addition,
  the links in the README will be broken until JuliaHub fetches the new
  version on their servers.
  

Post-release

• Once the tag is created, update the relevant release- branch. The latest
  release branch at the time of writing is release-1.0 (we haven't
  back-ported any patches that needed to create a release-1.Y branch). To
  to update the release branch with the v1.10.0 tag, do:
  git checkout release-1.0 git pull git merge v1.10.0 git push

[codecov]

Codecov Report

✅ All modified and coverable lines are covered by tests.
✅ Project coverage is 99.96%. Comparing base (125964c) to head (7237bda).
⚠️ Report is 3 commits behind head on master.

Additional details and impacted files

@@            Coverage Diff             @@
##           master    #4188      +/-   ##
==========================================
+ Coverage   99.87%   99.96%   +0.09%     
==========================================
  Files          43       43              
  Lines        6316     6315       -1     
==========================================
+ Hits         6308     6313       +5     
+ Misses          8        2       -6     

☔ View full report in Codecov by Harness.
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• ❄️ Test Analytics: Detect flaky tests, report on failures, and find test suite problems.

[odow]

There are a couple of blocking failures here:

• PiecewiseLinearOpt: ProductOfSets is wrong when a constraint has zero rows MathOptInterface.jl#3019
• Plasmo: blocking because of the change to moi_function. We should ensure backwards compatibility.
• SumOfSquares: okay because it checks the text of an error message.

[odow]

@blegat the failure in PiecewiseLinearApprox is because of this change in COSMO: oxfordcontrol/COSMO.jl#198

[odow]

This is ready for release. The only decision is whether to do something about #4190 first.

[odow]

Okay, DisjunctiveProgramming is failing, but only because we have improved the functions.

Before

(tmp) pkg> st
Status `~/git/infiniteopt/DisjunctiveProgramming/tmp/Project.toml`
  [0d27d021] DisjunctiveProgramming v0.6.1 `https://github.com/infiniteopt/DisjunctiveProgramming.jl.git#master`
  [8dfed614] Test v1.11.0

julia> begin
           using DisjunctiveProgramming
           import DisjunctiveProgramming as DP
           include("test/utilities.jl")
           model = GDPModel()
           @variable(model, 10 <= x <= 100)
           @variable(model, z, Logical)
           @constraint(model, con, x^2 in MOI.LessThan{Float64}(5), Disjunct(z))
           zbin = variable_by_name(model, "z")
           method = DisjunctiveProgramming._Hull(Hull(1e-3), Set([x]))
           prep_bounds(x, model, Hull())
           DisjunctiveProgramming._disaggregate_variables(model, z, Set([x]), method)
           x_z = variable_by_name(model, "x_z")
           ref = reformulate_disjunct_constraint(model, constraint_object(con), zbin, method)
           ref[1].func
       end
Test Summary:     | Pass  Total  Time
Utility Functions |   21     21  0.1s
((0) + ((x_z²) / (0.999 z + 0.001))) - (5 z)

After

(DisjunctiveProgramming) pkg> st
Project DisjunctiveProgramming v0.6.1
Status `~/git/infiniteopt/DisjunctiveProgramming/Project.toml`
  [4076af6c] JuMP v1.31.0 `~/git/jump-dev/JuMP`
  [189a3867] Reexport v1.2.2

julia> begin
           using DisjunctiveProgramming
           import DisjunctiveProgramming as DP
           include("test/utilities.jl")
           model = GDPModel()
           @variable(model, 10 <= x <= 100)
           @variable(model, z, Logical)
           @constraint(model, con, x^2 in MOI.LessThan{Float64}(5), Disjunct(z))
           zbin = variable_by_name(model, "z")
           method = DisjunctiveProgramming._Hull(Hull(1e-3), Set([x]))
           prep_bounds(x, model, Hull())
           DisjunctiveProgramming._disaggregate_variables(model, z, Set([x]), method)
           x_z = variable_by_name(model, "x_z")
           ref = reformulate_disjunct_constraint(model, constraint_object(con), zbin, method)
           ref[1].func
       end
Test Summary:     | Pass  Total  Time
Utility Functions |   21     21  0.1s
((x_z²) / (0.999 z + 0.001)) - (5 z)

I'll make a PR to fix their tests.