# IloIfThen constraints for CPLEX in Julia
**URL:** https://discourse.julialang.org/t/iloifthen-constraints-for-cplex-in-julia/85619
**Category:** Optimization (Mathematical)
**Created:** [August 11, 2022, 1:44pm UTC](https://discourse.julialang.org/t/iloifthen-constraints-for-cplex-in-julia/85619 "2022-08-11T13:44:54Z")
**Posts on this page:** 4
**Page:** 1
### Author:  [@Deoch](https://discourse.julialang.org/u/Deoch)
#### Post date: [August 11, 2022, 1:44pm UTC](https://discourse.julialang.org/t/iloifthen-constraints-for-cplex-in-julia/85619/1 "2022-08-11T13:44:55Z")
Hello,
I need to use the conditional constraints for a JuMP model with CPLEX, that would be IloIfThen in C++. Like if I have three variables x, y and z, a constraint like:
If (x \>= 1) && (y \<= 2) then z == 1
I am using JuMP, and have tried to use the indicator constraints, with something like this:
@constraint(m, name, ((x \>= 1) && (y \<= 2)) =\> { z == 1})
However it does not work, it seems that I can’t use a JuMP decision variable in a logical expression, therefore I can only use a binary decision variable to trigger the constraint instead of a more complex logical expression.
Of course I could linearize the conditional constraints, but that is not possible in my case: I need to implement this model (found in the literature) to benchmark an algorithm I developped. The model uses a lot of conditional constraints, and I fear that the workaround of linearizing everything might affect the performances.
So, is there a way for JuMP to model this constraint ? Otherwise, is it possible to do that using the low-level CPLEX API ? What would be the syntax in this case ?
Thanks!
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### Author:  [@odow](https://discourse.julialang.org/u/odow)
#### Post date: [August 11, 2022, 11:39pm UTC](https://discourse.julialang.org/t/iloifthen-constraints-for-cplex-in-julia/85619/2 "2022-08-11T23:39:11Z")
> Of course I could linearize the conditional constraints
Yes, this is what you will need to do.
> is there a way for JuMP to model this constraint ?
No. JuMP supports only indicator constraints of the form `z => { ... }` where `z` is a binary variable.
> Otherwise, is it possible to do that using the low-level CPLEX API ? What would be the syntax in this case ?
you can call any functions in the CPLEX C API: [https://github.com/jump-dev/CPLEX.jl/blob/master/src/gen2210/libcpx\_api.jl](https://github.com/jump-dev/CPLEX.jl/blob/master/src/gen2210/libcpx_api.jl)
But this is usually non-trivial, and requires a detailed understanding of the C API.
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### Author:  [@jd-foster](https://discourse.julialang.org/u/jd-foster)
#### Post date: [August 11, 2022, 11:42pm UTC](https://discourse.julialang.org/t/iloifthen-constraints-for-cplex-in-julia/85619/3 "2022-08-11T23:42:30Z")
It sounds like you’re looking to do constraint programming. There is a nice blog post about the relationship between JuMP and this topic here:
> **[An update on constraint programming in JuMP](https://jump.dev/blog/constraint-programming-update/)**
>
> Author: Thibaut Cuvelier (@dourouc05) JuMP and MathOptInterface are oriented towards traditional mathematical optimization, encompassing problem classes such as mixed-integer linear programs and conic optimization. However, the MathOptInterface API...
In particular, the package [ConstraintProgrammingExtensions.jl](https://github.com/JuliaConstraints/ConstraintProgrammingExtensions.jl) can interact with **[CPLEXCP.jl](https://github.com/JuliaConstraints/CPLEXCP.jl)**, which wraps the Java interface rather than the C++ version; see, for example, this list of exported functions [here](https://github.com/JuliaConstraints/CPLEXCP.jl/blob/cadc6577e4a076f1a763c904a4bc022e88472986/src/CPLEXCP.jl#L259).
There is probably some more work to do with introductory tutorials and docs for getting started.
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### Author:  [@jd-foster](https://discourse.julialang.org/u/jd-foster)
#### Post date: [August 11, 2022, 11:58pm UTC](https://discourse.julialang.org/t/iloifthen-constraints-for-cplex-in-julia/85619/4 "2022-08-11T23:58:19Z")
Or have a look here on reformulation ideas:
[https://docs.mosek.com/modeling-cookbook/mio.html](https://docs.mosek.com/modeling-cookbook/mio.html)