diff --git a/docs/Project.toml b/docs/Project.toml index 3339adbc97c..25f0657cef6 100644 --- a/docs/Project.toml +++ b/docs/Project.toml @@ -25,7 +25,7 @@ CSV = "0.10" DataFrames = "1" Documenter = "0.27.9" GLPK = "=1.1.0" -HTTP = "1" +HTTP = "=1.5.2" HiGHS = "=1.2.0" Interpolations = "0.14" Ipopt = "=1.1.0" diff --git a/docs/src/tutorials/linear/callbacks.jl b/docs/src/tutorials/linear/callbacks.jl index 451a7b52107..d431633ee3f 100644 --- a/docs/src/tutorials/linear/callbacks.jl +++ b/docs/src/tutorials/linear/callbacks.jl @@ -88,12 +88,12 @@ function example_user_cut_constraint() function my_callback_function(cb_data) callback_called = true x_vals = callback_value.(Ref(cb_data), x) - accumulated = sum(item_weights[i] for i = 1:N if x_vals[i] > 1e-4) + accumulated = sum(item_weights[i] for i in 1:N if x_vals[i] > 1e-4) println("Called with accumulated = $(accumulated)") - n_terms = sum(1 for i = 1:N if x_vals[i] > 1e-4) + n_terms = sum(1 for i in 1:N if x_vals[i] > 1e-4) if accumulated > 10 con = @build_constraint( - sum(x[i] for i = 1:N if x_vals[i] > 0.5) <= n_terms - 1 + sum(x[i] for i in 1:N if x_vals[i] > 0.5) <= n_terms - 1 ) println("Adding $(con)") MOI.submit(model, MOI.UserCut(cb_data), con) diff --git a/src/lp_sensitivity2.jl b/src/lp_sensitivity2.jl index 59b501ae1fd..7bed3adace0 100644 --- a/src/lp_sensitivity2.jl +++ b/src/lp_sensitivity2.jl @@ -95,7 +95,7 @@ function lp_sensitivity_report(model::Model; atol::Float64 = 1e-8) # We call `collect` here because some Julia versions are missing sparse # matrix \ sparse vector fallbacks. j => B_fact \ collect(std_form.A[:, j]) for - j = 1:length(basis.basic_cols) if basis.basic_cols[j] == false + j in 1:length(basis.basic_cols) if basis.basic_cols[j] == false ) report = SensitivityReport( diff --git a/test/constraint.jl b/test/constraint.jl index 40ca8f3bcaf..5c39fbfb444 100644 --- a/test/constraint.jl +++ b/test/constraint.jl @@ -649,15 +649,15 @@ function test_sum_constraint(ModelType, ::Any) C = [1 2 3; 4 5 6; 7 8 9] @test_expression sum(C[i, j] * x[i, j] for i in 1:2, j in 2:3) - @test_expression sum(C[i, j] * x[i, j] for i = 1:3, j in 1:3 if i != j) - y + @test_expression sum(C[i, j] * x[i, j] for i in 1:3, j in 1:3 if i != j) - y @test JuMP.isequal_canonical( @expression(model, sum(C[i, j] * x[i, j] for i in 1:3, j in 1:i)), sum(C[i, j] * x[i, j] for i in 1:3 for j in 1:i), ) @test_expression sum(C[i, j] * x[i, j] for i in 1:3 for j in 1:i) - @test_expression sum(C[i, j] * x[i, j] for i = 1:3 if true for j in 1:i) + @test_expression sum(C[i, j] * x[i, j] for i in 1:3 if true for j in 1:i) @test_expression sum( - C[i, j] * x[i, j] for i = 1:3 if true for j = 1:i if true + C[i, j] * x[i, j] for i in 1:3 if true for j in 1:i if true ) @test_expression sum(0 * x[i, 1] for i in 1:3) @test_expression sum(0 * x[i, 1] + y for i in 1:3) diff --git a/test/hygiene.jl b/test/hygiene.jl index 5a37d12de35..2946fcf1fe5 100644 --- a/test/hygiene.jl +++ b/test/hygiene.jl @@ -20,30 +20,30 @@ JuMP.@variable(model, z[i = 1:2, j = 1:2], Symmetric) @test z isa LinearAlgebra.Symmetric JuMP.@constraint(model, x + sum(j * y[j] for j in r) <= 1) -JuMP.@constraint(model, sum(y[j] for j = r if j == 4) <= 1) +JuMP.@constraint(model, sum(y[j] for j in r if j == 4) <= 1) JuMP.@constraint(model, -1 <= x + y[3] <= 1) JuMP.@constraints(model, begin x + sum(j * y[j] for j in r) <= 1 - sum(y[j] for j = r if j == 4) <= 1 + sum(y[j] for j in r if j == 4) <= 1 end) JuMP.@constraint(model, [x x; -x x] >= 0, JuMP.PSDCone()) JuMP.@objective(model, sense, y[4]) JuMP.@objective(model, Min, x + sum(j * y[j] for j in r)) -JuMP.@objective(model, Max, sum(y[j] for j = r if j == 4)) +JuMP.@objective(model, Max, sum(y[j] for j in r if j == 4)) JuMP.@NLconstraint(model, y[3] == 1) JuMP.@NLconstraint(model, x + sum(j * y[j] for j in r) <= 1) -JuMP.@NLconstraint(model, sum(y[j] for j = r if j == 4) <= 1) +JuMP.@NLconstraint(model, sum(y[j] for j in r if j == 4) <= 1) JuMP.@NLconstraints(model, begin x + sum(j * y[j] for j in r) <= 1 - sum(y[j] for j = r if j == 4) <= 1 + sum(y[j] for j in r if j == 4) <= 1 end) JuMP.@NLobjective(model, sense, y[4]) JuMP.@NLobjective(model, Min, x + sum(j * y[j] for j in r)) -JuMP.@NLobjective(model, Max, sum(y[j] for j = r if j == 4)) +JuMP.@NLobjective(model, Max, sum(y[j] for j in r if j == 4)) # TODO: Add tests for the content of the model. diff --git a/test/macros.jl b/test/macros.jl index 35bafd0593e..af23b48ec48 100644 --- a/test/macros.jl +++ b/test/macros.jl @@ -137,7 +137,7 @@ let id = 0 end function test_Check_Julia_generator_expression_parsing() - sumexpr = :(sum(x[i, j] * y[i, j] for i = 1:N, j in 1:M if i != j)) + sumexpr = :(sum(x[i, j] * y[i, j] for i in 1:N, j in 1:M if i != j)) @test sumexpr.head == :call @test sumexpr.args[1] == :sum @test sumexpr.args[2].head == :generator diff --git a/test/perf/JuMPBenchmarks.jl b/test/perf/JuMPBenchmarks.jl index c7c86ec2d70..4171b34d701 100644 --- a/test/perf/JuMPBenchmarks.jl +++ b/test/perf/JuMPBenchmarks.jl @@ -221,12 +221,12 @@ function _macro_linear(N::Int) sum( sum( N * i * j * k * y[i, j, k] + x[i, j] for - k = 1:N if i != j && j != k + k in 1:N if i != j && j != k ) for j in 1:N ) for i in 1:N ) + sum( - sum(x[i, j] for j = 1:5N if j % i == 3) for - i = 1:10N if i <= N * z + sum(x[i, j] for j in 1:5N if j % i == 3) for + i in 1:10N if i <= N * z ) ) end @@ -252,12 +252,12 @@ function _macro_quad(N::Int) sum( sum( N * i * j * k * y[i, j, k] * x[i, j] for - k = 1:N if i != j && j != k + k in 1:N if i != j && j != k ) for j in 1:N ) for i in 1:N ) + sum( - sum(x[i, j] for j = 1:5N if j % i == 3) for - i = 1:10N if i <= N * z + sum(x[i, j] for j in 1:5N if j % i == 3) for + i in 1:10N if i <= N * z ) ) end