Skip to content
New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

[docs] debug failure on v1.11 #3843

Merged
merged 5 commits into from
Oct 9, 2024
Merged
Changes from all commits
Commits
File filter

Filter by extension

Filter by extension


Conversations
Failed to load comments.
Loading
Jump to
Jump to file
Failed to load files.
Loading
Diff view
Diff view
2 changes: 1 addition & 1 deletion .github/workflows/documentation.yml
Original file line number Diff line number Diff line change
@@ -14,7 +14,7 @@ jobs:
- uses: actions/checkout@v4
- uses: julia-actions/setup-julia@latest
with:
version: '1.10'
version: '1'
- name: Install dependencies
shell: julia --color=yes --project=docs/ {0}
run: |
3 changes: 1 addition & 2 deletions docs/src/tutorials/algorithms/tsp_lazy_constraints.jl
Original file line number Diff line number Diff line change
@@ -124,7 +124,7 @@ function generate_distance_matrix(n; random_seed = 1)
return X, Y, d
end

n = 40
n = 20
X, Y, d = generate_distance_matrix(n)

# For the JuMP model, we first initialize the model object. Then, we create the
@@ -252,7 +252,6 @@ function subtour_elimination_callback(cb_data)
if !(1 < length(cycle) < n)
return # Only add a constraint if there is a cycle
end
println("Found cycle of length $(length(cycle))")
S = [(i, j) for (i, j) in Iterators.product(cycle, cycle) if i < j]
con = @build_constraint(
sum(lazy_model[:x][i, j] for (i, j) in S) <= length(cycle) - 1,
14 changes: 8 additions & 6 deletions docs/src/tutorials/conic/ellipse_approx.jl
Original file line number Diff line number Diff line change
@@ -94,7 +94,7 @@ plot = Plots.scatter(
model = Model(SCS.Optimizer)
## We need to use a tighter tolerance for this example, otherwise the bounding
## ellipse won't actually be bounding...
set_attribute(model, "eps_rel", 1e-6)
set_attribute(model, "eps_rel", 1e-7)
set_silent(model)
m, n = size(S)
@variable(model, z[1:n])
@@ -124,11 +124,13 @@ D = value.(Z)

c = D \ value.(z)

# Finally, overlaying the solution in the plot we see the minimal volume approximating
# ellipsoid:
# We can check that each point lies inside the ellipsoid, by checking if the
# largest normalized radius is less than 1:

largest_radius = maximum(map(x -> (x - c)' * D * (x - c), eachrow(S)))

Test.@test isapprox(D, [0.00707 -0.0102; -0.0102173 0.0175624]; atol = 1e-2) #src
Test.@test isapprox(c, [-3.24802, -1.842825]; atol = 1e-2) #src
# Finally, overlaying the solution in the plot we see the minimal volume
# approximating ellipsoid:

P = sqrt(D)
q = -P * c
@@ -211,7 +213,7 @@ f = [1 - S[i, :]' * Z * S[i, :] + 2 * S[i, :]' * z - s for i in 1:m]
@objective(model, Max, 1 * t + 0)
optimize!(model)
Test.@test is_solved_and_feasible(model)
Test.@test isapprox(D, value.(Z); atol = 1e-6) #src
Test.@test isapprox(D, value.(Z); atol = 1e-3) #src
solve_time_1 = solve_time(model)

# This formulation gives the much smaller graph:
4 changes: 2 additions & 2 deletions src/macros/@force_nonlinear.jl
Original file line number Diff line number Diff line change
@@ -84,10 +84,10 @@ julia> @expression(model, @force_nonlinear(x * 2.0 * (1 + x) * x))
x * 2.0 * (1 + x) * x

julia> @allocated @expression(model, x * 2.0 * (1 + x) * x)
3200
2640

julia> @allocated @expression(model, @force_nonlinear(x * 2.0 * (1 + x) * x))
640
672
```
"""
macro force_nonlinear(expr)

Unchanged files with check annotations Beta

Copyright (c) 2017: Iain Dunning, Joey Huchette, Miles Lubin, and contributors

Check warning on line 1 in LICENSE.md

GitHub Actions / build

[vale] reported by reviewdog 🐶 [Google.Colons] ': I' should be in lowercase. Raw Output: {"message": "[Google.Colons] ': I' should be in lowercase.", "location": {"path": "LICENSE.md", "range": {"start": {"line": 1, "column": 19}}}, "severity": "WARNING"}
The JuMP Julia module is licensed under the **[MPL]** version 2.0: