Hi all,

I am working on a nonlinear type MIP optimization problem solved through JuMP.

My solution is based on Juniper and HiGHS:

```
using Juniper
using HiGHS
using JuMP
using Ipopt
R10, R20 = 0.18, 0.195
L11, L21 = 2.4, 2.4
Ld_dec = 2.4
w1 = R10 / sum([R10, R20])
w2 = R20 / sum([R10, R20])
lb = 0.42 / Ld_dec
ub = 3.87 / Ld_dec
dec_step = 300
nl_solver = optimizer_with_attributes(Ipopt.Optimizer, "print_level"=>0)
mip_solver = optimizer_with_attributes(HiGHS.Optimizer, "output_flag"=>false)
minlp_solver = optimizer_with_attributes(Juniper.Optimizer, "nl_solver"=>nl_solver, "mip_solver"=>mip_solver)
mfc = Model(minlp_solver)
# set_silent(mfc)
@variable(mfc, u, Bin)
@variable(mfc, v, Bin)
@variable(mfc, 0.0<=x<=ub)
@variable(mfc, 0.0<=y<=ub)
@NLconstraint(mfc, x<=ub*u)
@NLconstraint(mfc, x>=lb*u)
@NLconstraint(mfc, y<=ub*v)
@NLconstraint(mfc, y>=lb*v)
@constraint(mfc, x+y==1)
@NLobjective(mfc, Min, (u*w1 * (x*Ld_dec-L11)^2 + v*w2 * (y*Ld_dec-L21)^2) + (u*w1*(0.01245 + 0.0257 * (x*Ld_dec - 2.38)^2) + v*w2*(0.01245+ 0.0257 * (y*Ld_dec - 2.38)^2)) * dec_step)
optimize!(mfc)
println("x: $(value(x))")
println("y: $(value(y))")
```

I obtained:

```
#branches: 2
Obj: 1.7942803188090848
x: 0.9999999966479224
y: 3.3520777017322965e-9
```

The expected results should be x=1.0, y=0.0.

This is due to an arithmetic calculation accuracy problem? How can I obtain an ‘accurate’ desired results?

Thank you in advance!