Hi, all,

There is a simple programming with generalized constraints (min(…)), but the julia tells me errors as follows:

ERROR: LoadError: MethodError: no method matching isless(::AffExpr, ::AffExpr)

Closest candidates are:

isless(::Any, ::Missing) at missing.jl:88

isless(::Missing, ::Any) at missing.jl:87

The problem is can I use generalized constraints in JuMP naturally, and the codes are:

```
model = Model(Gurobi.Optimizer)
@variables(model, begin
s[1:8], Bin
z[1:8], Bin
d[1:8], Bin
o[1:8], Bin
end)
for t in 1:8
@constraint(model, o[t] == min((1 - s[t]) + z[t], 1 - d[t]))
end
@constraints(model, begin
s[1:3] .== 0
s[4:end] .== 1
z[1:5] .== 0
z[6:end] .== 1
d[1:2] .== 1
end)
optimize!(model)
value.(o)
```

And the expected output may be:

[0, 0, 1, 0, 0, 1, 1, 1]

I already know if I use the Gurobi solver, the codes above can use the following form (may with nonconvex optimizer in gurobi?)

```
model = Model(Gurobi.Optimizer)
@variables(model, begin
s[1:8], Bin
z[1:8], Bin
d[1:8], Bin
o[1:8], Bin
end)
for t in rg(8)
@constraint(model, o[t] == ((1 - s[t]) + z[t]) * (1 - d[t]))
end
@constraints(model, begin
s[1:3] .== 0
s[4:end] .== 1
z[1:5] .== 0
z[6:end] .== 1
d[1:2] .== 1
end)
optimize!(model)
value.(o)
```