# A semicolon (;) fails to suppress showing in Julia REPL
**URL:** https://discourse.julialang.org/t/a-semicolon-fails-to-suppress-showing-in-julia-repl/127583
**Category:** General Usage
**Tags:** question
**Created:** [April 1, 2025, 9:18am UTC](https://discourse.julialang.org/t/a-semicolon-fails-to-suppress-showing-in-julia-repl/127583 "2025-04-01T09:18:23Z")
**Posts on this page:** 6
**Page:** 1
### Author:  [@WalterMadelim](https://discourse.julialang.org/u/WalterMadelim)
#### Post date: [April 1, 2025, 9:18am UTC](https://discourse.julialang.org/t/a-semicolon-fails-to-suppress-showing-in-julia-repl/127583/1 "2025-04-01T09:18:23Z")
The title is observed by the behavior like this
```julia
julia> JuMP.@constraint(lpr, c_rlt[r in 1:R1, c in 1:R2], sm(A1[r, :] * A2[c, :]', cross_multrix) >= 0); #
8×18 Matrix{JuMP.ConstraintRef{JuMP.Model, MathOptInterface.ConstraintIndex{MathOptInterface.ScalarAffineFunction{Float64}, MathOptInterface.GreaterThan{Float64}}, JuMP.ScalarShape}}:
c_rlt[1,1] : -22 lpr_g[1] - 22 lpr_g[2] + 1.2 lpr_Dv[1] - 1.2 lpr_Dv[4] - g_x_Dv[1,1] - g_x_Dv[2,1] + g_x_Dv[1,4] + g_x_Dv[2,4] >= -26.4 … c_rlt[1,18] : 1.2 lpr_Dv[6] - g_x_Dv[1,6] - g_x_Dv[2,6] >= 0
c_rlt[2,1] : -22 lpr_g[1] - 22 lpr_g[2] - 22 lpr_g[3] + 1.8 lpr_Dv[1] - 1.8 lpr_Dv[4] - g_x_Dv[1,1] - g_x_Dv[2,1] - g_x_Dv[3,1] + g_x_Dv[1,4] + g_x_Dv[2,4] + g_x_Dv[3,4] >= -39.6 c_rlt[2,18] : 1.8 lpr_Dv[6] - g_x_Dv[1,6] - g_x_Dv[2,6] - g_x_Dv[3,6] >= 0
c_rlt[3,1] : 22 lpr_g[1] + g_x_Dv[1,1] - g_x_Dv[1,4] >= 0
c_rlt[3,18] : g_x_Dv[1,6] >= 0
c_rlt[4,1] : 22 lpr_g[2] + g_x_Dv[2,1] - g_x_Dv[2,4] >= 0
c_rlt[4,18] : g_x_Dv[2,6] >= 0
c_rlt[5,1] : 22 lpr_g[3] + g_x_Dv[3,1] - g_x_Dv[3,4] >= 0
c_rlt[5,18] : g_x_Dv[3,6] >= 0
c_rlt[6,1] : -22 lpr_g[1] + lpr_Dv[1] - lpr_Dv[4] - g_x_Dv[1,1] + g_x_Dv[1,4] >= -22
… c_rlt[6,18] : lpr_Dv[6] - g_x_Dv[1,6] >= 0
c_rlt[7,1] : -22 lpr_g[2] + lpr_Dv[1] - lpr_Dv[4] - g_x_Dv[2,1] + g_x_Dv[2,4] >= -22
c_rlt[7,18] : lpr_Dv[6] - g_x_Dv[2,6] >= 0
c_rlt[8,1] : -22 lpr_g[3] + lpr_Dv[1] - lpr_Dv[4] - g_x_Dv[3,1] + g_x_Dv[3,4] >= -22
c_rlt[8,18] : lpr_Dv[6] - g_x_Dv[3,6] >= 0
```
I use the latest JuMP and Julia. Is this a bug (although not a serious one)?
The code to reproduce this is in the next post.
---
### Author:  [@WalterMadelim](https://discourse.julialang.org/u/WalterMadelim)
#### Post date: [April 1, 2025, 9:19am UTC](https://discourse.julialang.org/t/a-semicolon-fails-to-suppress-showing-in-julia-repl/127583/2 "2025-04-01T09:19:06Z")
(The following code is an example on using LP relaxation to solve a 2-stage ARO problem.
The case is an RCR variant of the small case in [CCG2012](https://discourse.julialang.org/t/columns-and-constraints-generation/103813/13).)
The bug of the title occurs at **line 104**.
Since no Complex number are involved here.
After rewriting the `adjoint` with `transpose`, the bug is resolved.
```julia
import JuMP, Gurobi; GRB_ENV = Gurobi.Env();
import SparseArrays; import LinearAlgebra.tr as tr
function sm(p, x) return sum(p .* x) end
function optimise(m)
JuMP.optimize!(m)
return (
JuMP.termination_status(m),
JuMP.primal_status(m),
JuMP.dual_status(m)
)
end
macro set_objective_function(m, f) return esc(:(JuMP.set_objective_function($m, JuMP.@expression($m, $f)))) end
function Model(name) # e.g. "st2_lms" or "st1_0M0" or "supp_l0s"
m = JuMP.Model(() -> Gurobi.Optimizer(GRB_ENV))
JuMP.MOI.set(m, JuMP.MOI.Name(), name)
s = name[end-1]
s == 'm' && JuMP.set_objective_sense(m, JuMP.MIN_SENSE)
s == 'M' && JuMP.set_objective_sense(m, JuMP.MAX_SENSE)
last(name) == 's' && JuMP.set_silent(m)
m
end
function solve_to_normality(m)
t, p, d = optimise(m)
name = JuMP.name(m)
t == JuMP.OPTIMAL || error("$name: $(string(t))")
p == JuMP.FEASIBLE_POINT || error("$name: (primal) $(string(p))")
(name[end-2] == 'l' && d != p) && error("$name: (dual) $(string(d))")
end
function safe_upper_bound(ObjBnd) return max(0., ObjBnd + 9.99, 1.01 * ObjBnd) end
function g2d(g) return B_d .+ 40 * g end
function stage2_problem_primal(z, d)
st2 = Model("st2_primal_lms")
JuMP.@variable(st2, x[i = 1:3, j = 1:3] >= 0)
JuMP.@variable(st2, ζ[i = 1:3] >= 0) # an expensive substitute for `z`
JuMP.@constraint(st2, DI[i = 1:3], z[i] + ζ[i] >= sum(x[i, :]))
JuMP.@constraint(st2, DJ[j = 1:3], sum(x[:, j]) >= d[j] )
@set_objective_function(st2, sm(R_x, x) + sm(R_ζ, ζ))
solve_to_normality(st2);
return JuMP.objective_value(st2)
end
function st2_dual_obj_f(z, d, DI, DJ) return sm(DJ, d) - sm(DI, z) end # 1st-stage decision; scene; 2nd-stage dual decision
function stage2_problem_dual(z, d) # ✅
st2 = Model("st2_dual_lMs")
JuMP.@variable(st2, 0 <= DI[i = 1:3] <= R_ζ[i]) # this dual variable is bounded above, because its associated primal constraint is "price-relaxed", the value of `ζ` can be recovered through `-dual(UpperBoundRef(DI))`
JuMP.@variable(st2, 0 <= DJ[j = 1:3]) # implicitly upper bounded
JuMP.@constraint(st2, x[i = 1:3, j = 1:3], R_x[i, j] + DI[i] - DJ[j] >= 0)
@set_objective_function(st2, st2_dual_obj_f(z, d, DI, DJ))
solve_to_normality(st2)
return JuMP.objective_value(st2), JuMP.value.(DI), JuMP.value.(DJ)
end
function get_st1_oz_cut(z, d)
_, DI, DJ = stage2_problem_dual(z, d)
return sm(DJ, d), -DI # cn, pz
end
function g_2_DI_DJ(z, g) return stage2_problem_dual(z, g2d(g)) end
function DI_DJ_2_g(z, DI, DJ) # This function embodies the uncertainty set, if OBJ were vacant
adversarial = Model("adversarial_lMs")
JuMP.@variable(adversarial, 0 <= g[1:3] <= 1)
JuMP.@constraint(adversarial, sum(first(g, 2)) <= 1.2); JuMP.@constraint(adversarial, sum(g) <= 1.8)
@set_objective_function(adversarial, st2_dual_obj_f(z, g2d(g), DI, DJ))
solve_to_normality(adversarial)
return JuMP.objective_value(adversarial), JuMP.value.(g)
end
function get_A1_A2() # A1 is the uncertainty's; A2 is the st2_dual's
A1 = SparseArrays.spzeros(R1, 1 + C1); A2 = SparseArrays.spzeros(R2, 1 + C2)
A1[1, 1] = 1.2; A1[1, 2] = -1; A1[1, 3] = -1; # r1
A1[2, 1] = 1.8; A1[2, 2] = -1; A1[2, 3] = -1; A1[2, 4] = -1; # r2
A1[3, 2] = 1; A1[4, 3] = 1; A1[5, 4] = 1; # r3
A1[6:8, 1] .= 1; A1[6, 2] = -1; A1[7, 3] = -1; A1[8, 4] = -1; # r4
for i in 1:3, j in 1:3 # constraint c1
r = (i-1) * 3 + j
A2[r, 0+ 1] = R_x[i, j]
A2[r, 1+ i] = 1
A2[r, 1+3+ j] = -1
end
[A2[i, j] = 1 for (i, j) in zip(10:12, 2:4)] # constraint c2
[A2[i, j] = -1 for (i, j) in zip(13:15, 2:4)]; A2[13:15, 1] .= R_ζ # constraint c3
[A2[i, j] = 1 for (i, j) in zip(16:18, 5:7)] # constraint c4
return A1, A2
end
function improve_g_by_BCA(z, g) # Block Coordinate Ascent
_, DI, DJ = g_2_DI_DJ(z, g)
return lb, g = DI_DJ_2_g(z, DI, DJ) # enhanced ObjVal=lb & scene=g
end
R_x, C_y, C_z, B_d = let # Data
[22 33 24; 33 23 30; 20 25 27], [400, 414, 326], [18, 25, 20], [206, 274, 220]
end; R_ζ = 20 * C_z; # recourse cost for z, me designed
# 🥎 🥎 🥎 🥎 🥎 🥎 🥎 🥎 🥎 🥎 🥎 🥎 🥎
st1 = Model("st1_bms"); JuMP.@variable(st1, st1_o);
JuMP.@variables(st1, begin st1_y[1:3], Bin; st1_z[1:3] >= 0 end); JuMP.@constraint(st1, st1_z .<= 800 * st1_y);
JuMP.set_objective_coefficient(st1, st1_y, C_y); JuMP.set_objective_coefficient(st1, st1_z, C_z);
solve_to_normality(st1); z = JuMP.value.(st1_z); # 🍏
# 🥎 🥎 🥎 🥎 🥎 🥎 🥎 🥎 🥎 🥎 🥎 🥎 🥎
best_lpr_ul = zeros(2); best_g = zeros(3); # global book
old_feas_val = zeros(1); # if the heuristic solution of BLP reaches a different value, then we deem it new
R1=1+1+3+3; C1=3; # 1 ⇒ uncertainty_side, 2 ⇒ st2_dual side
R2=3*3+3+3+3; C2=3+3; A1, A2 = get_A1_A2();
# 🥎 🥎 🥎 🥎 🥎 🥎 🥎 🥎 🥎 🥎 🥎 🥎 🥎
lpr = Model("lpr_lMs");
JuMP.@variables(lpr, begin lpr_g[1:C1]; lpr_Dv[1:C2]; g_x_Dv[1:C1, 1:C2] end);
JuMP.@expression(lpr, cross_multrix, [1 lpr_Dv'; lpr_g g_x_Dv]); JuMP.@expression(lpr, lpr_DI, lpr_Dv[1:3]);
JuMP.@expression(lpr, lpr_DJ, lpr_Dv[4:6]); JuMP.@expression(lpr, g_x_DJ, g_x_Dv[:, 4:6]);
JuMP.@constraint(lpr, [A1 * [1; lpr_g]; A2 * [1; lpr_Dv]] .>= 0); # [uncertainty_side; st2_dual_side] constraints
JuMP.@constraint(lpr, c_rlt[r in 1:R1, c in 1:R2], sm(A1[r, :] * A2[c, :]', cross_multrix) >= 0); # First, add all RLT constrs
@set_objective_function(lpr, sm(B_d, lpr_DJ) + 40 * tr(g_x_DJ)) # common static part
JuMP.set_objective_coefficient(lpr, lpr_DI, -z); # 🟡 1st-decision dependent
solve_to_normality(lpr);
c_safe_ub = JuMP.@constraint(lpr, safe_upper_bound(JuMP.objective_bound(lpr)) >= JuMP.objective_function(lpr));
let # initialize global book
solve_to_normality(lpr)
best_lpr_ul[1] = JuMP.objective_bound(lpr)
g = JuMP.value.(lpr_g) # get a heuristic solution from `lpr`
old_feas_val[1] = st2_dual_obj_f(z, g2d(g), JuMP.value.(lpr_DI), JuMP.value.(lpr_DJ)) # record the old value to help discovering new heuristic solution
lb, g = improve_g_by_BCA(z, g)
best_lpr_ul[2] = lb; best_g .= g
end;
JuMP.set_objective_coefficient(st1, st1_o, 1); # 1️⃣ a one-shot turn on
cn, pz = get_st1_oz_cut(z, g2d(best_g)); JuMP.@constraint(st1, st1_o >= cn + sm(pz, st1_z));
solve_to_normality(st1); z = JuMP.value.(st1_z); # 🍏✅ At this line, the 1st-stage is auto bounded
for i in 1:13 # After this iteration, We can prove that it converges to global optimality
JuMP.delete(lpr, c_safe_ub)
JuMP.set_objective_coefficient(lpr, lpr_DI, -z); # 🟡 1st-decision variant
solve_to_normality(lpr);
c_safe_ub = JuMP.@constraint(lpr, safe_upper_bound(JuMP.objective_bound(lpr)) >= JuMP.objective_function(lpr))
let # initialize global book
solve_to_normality(lpr)
best_lpr_ul[1] = JuMP.objective_bound(lpr)
g = JuMP.value.(lpr_g) # get a heuristic solution from `lpr`
old_feas_val[1] = st2_dual_obj_f(z, g2d(g), JuMP.value.(lpr_DI), JuMP.value.(lpr_DJ)) # record the old value to help discovering new heuristic solution
lb, g = improve_g_by_BCA(z, g)
best_lpr_ul[2] = lb; best_g .= g
end
cn, pz = get_st1_oz_cut(z, g2d(best_g)); JuMP.@constraint(st1, st1_o >= cn + sm(pz, st1_z))
solve_to_normality(st1); z = JuMP.value.(st1_z) # 🍏
@info "lb = $(JuMP.objective_bound(st1))"
end
```
---
### Author:  [@mbauman](https://discourse.julialang.org/u/mbauman)
#### Post date: [April 1, 2025, 1:28pm UTC](https://discourse.julialang.org/t/a-semicolon-fails-to-suppress-showing-in-julia-repl/127583/4 "2025-04-01T13:28:10Z")
This is indeed a bug, but has almost nothing to do with JuMP or your code — it’s just the single postfix `'` that’s confusing things:
>
>
> This string is parsed incorrectly by ends\_with\_semicolon:
>
> julia\> REPL.end…s\_with\_semicolon("W = W' \* W; # comment")
> false
>
> My guess is the single apostrophe (adjoint operator) is interpreted as a quote.
---
### Author:  [@WalterMadelim](https://discourse.julialang.org/u/WalterMadelim)
#### Post date: [April 1, 2025, 1:33pm UTC](https://discourse.julialang.org/t/a-semicolon-fails-to-suppress-showing-in-julia-repl/127583/5 "2025-04-01T13:33:37Z")
Oh, yes. I replace the adjoint with `transpose` then it doesn’t show anymore.
Does the developers have a plan to fix this issue?
---
### Author:  [@mbauman](https://discourse.julialang.org/u/mbauman)
#### Post date: [April 1, 2025, 2:04pm UTC](https://discourse.julialang.org/t/a-semicolon-fails-to-suppress-showing-in-julia-repl/127583/6 "2025-04-01T14:04:50Z")
You could! The `;` suppression is a heuristic based upon an overly simplistic parsing of the input. It lives here; one pageful of Julia code: [julia/stdlib/REPL/src/REPL.jl at 13311f324e850fefddfcdf43d6c93b9365e2cf46 · JuliaLang/julia · GitHub](https://github.com/JuliaLang/julia/blob/13311f324e850fefddfcdf43d6c93b9365e2cf46/stdlib/REPL/src/REPL.jl#L1686-L1728)
In the meantime, you can work around it by deleting the trailing comment.
---
### Author:  [@Palli](https://discourse.julialang.org/u/Palli)
#### Post date: [April 1, 2025, 5:27pm UTC](https://discourse.julialang.org/t/a-semicolon-fails-to-suppress-showing-in-julia-repl/127583/7 "2025-04-01T17:27:15Z")
I believe I’ve fixed this. It was a sort of drive-by fix (I’m leaving in a few minutes, and no longer follow up much on my PRs), didn’t mean to add/have a test, not in the habit… but added it also after seeing “test needed” label (believe one failure is just the label needs to go away and the other must be a false alarm “build i686-w64-mingw32”):
>
>
> Fixes #46189