This is an actual experiment I’m doing currently. And JuMP give me Warning message without mentioning the name of the model I’ve established. Therefore I am not able to locate the source of this Warning. @odow
julia> while true
opt_ass_opt(trh, "trh")
x = jvr6.(trh_x); oLh_check = JuMP.value(oLh); lb = JuMP.objective_bound(trh);
JuMP.fix.(tr2_x, x) # 🖌️
proper_Q = get_proper_Q(x) # ✅
updVec .= false
for j in 1:proper_Q[1]
Y = proper_Q[3][:, :, j]
JuMP.fix.(tr2_Y, Y) # 🖌️
opt_ass_opt(tr2, "tr2")
b2 = jvr6.(tr2_b2); oΛ2_check = JuMP.value(oΛ2) # 🥑✂️
set_argZ_objective(x, Y, b2); opt_ass_optortime(argZ, "argZ")
Z_new = jvr6.(argZ_Z)
cn, px, pY, pb = JuMP.value(argZ_cn), jvr6.(argZ_c_x), jvr6.(argZ_c_Y), jvr6.(argZ_c_b2)
if oΛ2_check < cn + ip(px, x) + ip(pY, Y) + ip(pb, b2) - UPDTOL
add_cut_for_oΛ2(cn, px, pY, pb)
updVec[2] = true
end
recruit(Z_list, Z_new)
proper_P = get_proper_P(x, Y, Z_list) # ✅
add_cut_for_oLh(x, proper_Q, proper_P, oLh_check) && (updVec[1] = true)
end
@info "$updVec, lb = $lb"
all(updVec .== false) && break
end
[ Info: Bool[1, 1], lb = -208.90723332481141
┌ Warning: The addition operator has been used on JuMP expressions a large number of times. This warning is safe to ignore but may indicate that model generation is slower than necessary. For performance reasons, you should not add expressions in a loop. Instead of x += y, use add_to_expression!(x,y) to modify x in place. If y is a single variable, you may also use add_to_expression!(x, coef, y) for x += coef*y.
└ @ JuMP K:\judepot1112\packages\JuMP\i68GU\src\operators.jl:282
[ Info: Bool[1, 1], lb = 0.6
[ Info: Bool[1, 1], lb = 1.23
[ Info: Bool[1, 1], lb = 1.23
As you can see (restricted to the code I’ve posted here), there are 3 models trh, tr2 and argZ.
And for some information about this Warning, you can check this post if you have time, thanks.