# Workaround for modifying coefficients in constraints in JuMP?
**URL:** https://discourse.julialang.org/t/workaround-for-modifying-coefficients-in-constraints-in-jump/27123
**Category:** Optimization (Mathematical)
**Created:** [August 3, 2019, 12:21am UTC](https://discourse.julialang.org/t/workaround-for-modifying-coefficients-in-constraints-in-jump/27123 "2019-08-03T00:21:40Z")
**Posts on this page:** 10
**Page:** 1
### Author:  [@torgo](https://discourse.julialang.org/u/torgo)
#### Post date: [August 3, 2019, 12:21am UTC](https://discourse.julialang.org/t/workaround-for-modifying-coefficients-in-constraints-in-jump/27123/1 "2019-08-03T00:21:40Z")
In the 0.19 documentation on [this page](http://www.juliaopt.org/JuMP.jl/v0.19.0/constraints/) it says:
> ### [Modifying a constant term](http://www.juliaopt.org/JuMP.jl/v0.19.0/constraints/#Modifying-a-constant-term-1)
>
> Most often, modifications involve changing the “right-hand side” of a linear constraint. This presents a challenge for JuMP because it leads to ambiguities. For example, what is the right-hand side term of `@constraint(model, 2x + 1 <= x - 3)` ? This applies more generally to any constant term in a function appearing in the objective or a constraint.
>
> To avoid these ambiguities, JuMP includes the ability to _fix_ variables to a value using the [`fix`](http://www.juliaopt.org/JuMP.jl/v0.19.0/variables/#JuMP.fix) function. Fixing a variable sets its lower and upper bound to the same value. Thus, changes in a constant term can be simulated by adding a dummy variable and fixing it to different values. Here is an example:
>
> ```julia
> julia> @variable(model, const_term)
> const_term
>
> julia> @constraint(model, con, 2x <= const_term)
> con : 2 x - const_term <= 0.0
>
> julia> fix(const_term, 1.0)
>
> ```
>
> Note
>
> Even though `const_term` is fixed, it is still a decision variable. Thus, `const_term * x` is bilinear. Fixed variables are not replaced with constants when communicating the problem to a solver.
In my problem I have vector constraints like `const_term * x ` and I would like to repeatedly solve for different values of `const_term`.
The program is linear for any fixed value of `const_term` and (as stated above), if I try to use `fix` for `const_term` the program is recognized as quadratic/bilinear by Gurobi, which leads to problems.
What is a good workaround for solving a repeatedly parameterized problem in JuMP where the coefficients in the constraints change?
---
### Author:  [@ericphanson](https://discourse.julialang.org/u/ericphanson)
#### Post date: [August 3, 2019, 12:43am UTC](https://discourse.julialang.org/t/workaround-for-modifying-coefficients-in-constraints-in-jump/27123/2 "2019-08-03T00:43:06Z")
I haven’t used it before, but I think that’s the goal of [ParameterJuMP](https://github.com/JuliaStochOpt/ParameterJuMP.jl). And with [Convex.jl](https://github.com/JuliaOpt/Convex.jl) instead of JuMP— which may or may not be an viable alternative, depending on the problem— you could use `fix!` and `free!` (I recently helped fix some bugs regarding `fix!` and `free!` there, so I figured I’d advertise the package).
---
### Author:  [@tkoolen](https://discourse.julialang.org/u/tkoolen)
#### Post date: [August 3, 2019, 5:03am UTC](https://discourse.julialang.org/t/workaround-for-modifying-coefficients-in-constraints-in-jump/27123/3 "2019-08-03T05:03:38Z")
There’s [Constraints · JuMP](http://www.juliaopt.org/JuMP.jl/v0.19.2/constraints/#Modifying-a-variable-coefficient-1), the paragraph below the part of the documentation you posted. You could also think about using [GitHub - tkoolen/Parametron.jl: Efficiently solving instances of a parameterized family of (possibly mixed-integer) linear/quadratic optimization problems in Julia](https://github.com/tkoolen/Parametron.jl). This has been discussed before a number of times. See e.g. [Deleting containerized constraints - #13 by tkoolen](https://discourse.julialang.org/t/deleting-containerized-constraints/21971/13) for a Parametron demo.
---
### Author:  [@torgo](https://discourse.julialang.org/u/torgo)
#### Post date: [August 6, 2019, 3:57pm UTC](https://discourse.julialang.org/t/workaround-for-modifying-coefficients-in-constraints-in-jump/27123/4 "2019-08-06T15:57:13Z")
Thank you for the suggestions.
A solution for my purposes was just to continually `deepcopy` the model and then add the changed constraint.
I assume this is not very efficient, but for the time being it seems to work ok.
---
### Author:  [@odow](https://discourse.julialang.org/u/odow)
#### Post date: [August 6, 2019, 4:55pm UTC](https://discourse.julialang.org/t/workaround-for-modifying-coefficients-in-constraints-in-jump/27123/5 "2019-08-06T16:55:15Z")
Is there any reason why `set_coefficient` doesn’t work?
We try to discourage the use of `deepcopy`. Instead, you could use a function to rebuild the base model:
```nohighlight
function base_model()
model = Model()
@variable(model, x)
@variable(model, y)
@constraint(model, x >= 0)
return model
end
for c in 1:3
m = base_model()
@constraint(m, m[:x] + c * m[:y] <= 1)
optimize!(m)
end
```
---
### Author:  [@torgo](https://discourse.julialang.org/u/torgo)
#### Post date: [August 6, 2019, 7:00pm UTC](https://discourse.julialang.org/t/workaround-for-modifying-coefficients-in-constraints-in-jump/27123/6 "2019-08-06T19:00:35Z")
You’re right: `set_coefficient` works.
I had interpreted the “scalar” qualification in the manual [http://www.juliaopt.org/JuMP.jl/v0.19.2/constraints/#Modifying-a-variable-coefficient-1](http://www.juliaopt.org/JuMP.jl/v0.19.2/constraints/#Modifying-a-variable-coefficient-1) to mean that it wouldn’t work for a sum of multiple variables.
However, this works:
```julia
m = Model()
@variable(m, x[1:2])
@constraint(m, con, 5*x[1] + 6*x[2] == 0)
set_coefficient(con, x[1], 4)
println(m)
```
```julia
Feasibility
Subject to
4 x[1] + 6 x[2] = 0.0
```
So, that solves my immediate problem.
But if you have time, could you elaborate on why `deepcopy` is discouraged?
Also, for the base model workaround, my model is quite large and the part I want to adjust is only a small component of it. Would that make this solution less attractive?
---
### Author:  [@odow](https://discourse.julialang.org/u/odow)
#### Post date: [August 6, 2019, 7:17pm UTC](https://discourse.julialang.org/t/workaround-for-modifying-coefficients-in-constraints-in-jump/27123/7 "2019-08-06T19:17:05Z")
The underlying solver object is accessed via a C API. `deepcopy`’ing a model will copy the Julia part, but only copy the pointer to the underlying solver. So if you make modifications to one of the models, the second one is now out of date. See, e.g.,
```nohighlight
using JuMP, Gurobi
model = Model(with_optimizer(Gurobi.Optimizer))
@variable(model, x >= 0)
@objective(model, Min, x)
optimize!(model)
@assert value(x) == 0
model_2 = deepcopy(model)
@constraint(model_2, model_2[:x] >= 1)
optimize!(model_2)
@assert value(model_2[:x]) == 1
optimize!(model)
@show value(x) # <-- actually 1 not 0!
```
---
### Author:  [@torgo](https://discourse.julialang.org/u/torgo)
#### Post date: [August 6, 2019, 7:27pm UTC](https://discourse.julialang.org/t/workaround-for-modifying-coefficients-in-constraints-in-jump/27123/8 "2019-08-06T19:27:19Z")
Makes sense! Thanks for the explanation.
---
### Author:  [@odow](https://discourse.julialang.org/u/odow)
#### Post date: [August 6, 2019, 7:45pm UTC](https://discourse.julialang.org/t/workaround-for-modifying-coefficients-in-constraints-in-jump/27123/9 "2019-08-06T19:45:08Z")
If the docs were confusing, please help us make them better!
First, scroll to the top of [Constraints · JuMP](http://www.juliaopt.org/JuMP.jl/v0.19.2/constraints/#Modifying-a-variable-coefficient-1) and click “Edit on Github”
Then click the pencil “Edit this file”
Scroll down, make changes to the file, add a commit message, then click “Propose file change”
Let us know if you need any help!
---
### Author:  [@torgo](https://discourse.julialang.org/u/torgo)
#### Post date: [August 6, 2019, 8:09pm UTC](https://discourse.julialang.org/t/workaround-for-modifying-coefficients-in-constraints-in-jump/27123/10 "2019-08-06T20:09:34Z")
Ok, I modified the example and added a pull request.