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[docs] remove references to multiobjective optimization being unsupported #3223

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Feb 20, 2023
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[docs] remove references to multiobjective optimization being unsupported #3223

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[docs] remove references to multiobjective optimization being unsuppo…
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2 changes: 1 addition & 1 deletion docs/src/developers/roadmap.md
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Expand Up @@ -25,7 +25,7 @@ represent broad themes that we see as areas in which JuMP could be improved.
[https://github.com/jump-dev/JuMP.jl/issues/2227](https://github.com/jump-dev/JuMP.jl/issues/2227)
JuMP has a strong focus on linear, conic and nonlinear optimization problems.
We want to add better support for constraint programming.
- Add support for multiobjective problems:
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- **Done [#3176](https://github.com/jump-dev/JuMP.jl/pull/3176)** Add support for multiobjective problems:
[https://github.com/jump-dev/JuMP.jl/issues/2099](https://github.com/jump-dev/JuMP.jl/issues/2099)
JuMP is restricted to problems with scalar-valued objectives. We want to
extend this to vector-valued problems.
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11 changes: 1 addition & 10 deletions docs/src/should_i_use.md
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Expand Up @@ -12,7 +12,7 @@ You should use JuMP if you have a constrained optimization problem for which you
can formulate using the language of mathematical programming, that is:

* a set of decision variables
* a scalar objective function
* a scalar- or vector-valued objective function
* a set of constraints.

Key reasons to use JuMP include:
Expand Down Expand Up @@ -110,15 +110,6 @@ Alternatives to consider are:
* [InfiniteOpt.jl](https://github.com/infiniteopt/InfiniteOpt.jl)
* [pyomo.DAE [Python]](https://pyomo.readthedocs.io/en/stable/modeling_extensions/dae.html)

### Multiobjective programs

If your problem has more than one objective, JuMP is not the right tool for the
job. However, [we're working on fixing this!](https://github.com/jump-dev/JuMP.jl/issues/2099).

Alternatives to consider are:

* [vOptGeneric.jl](https://github.com/vOptSolver/vOptGeneric.jl)

### Disciplined convex programming

JuMP does not support [disciplined convex programming (DCP)](https://dcp.stanford.edu).
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