[docs] update should_i_use.md to remove optimal control (#3623)

Co-authored-by: Morten Piibeleht <[email protected]>
jump-dev · Dec 13, 2023 · 6b0ef01 · 6b0ef01
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diff --git a/docs/src/should_i_use.md b/docs/src/should_i_use.md
@@ -95,21 +95,6 @@ Alternatives to consider are:
  * [Optimization.jl](https://github.com/SciML/Optimization.jl)
  * [NLopt.jl](https://github.com/JuliaOpt/NLopt.jl)
 
-### Optimal control problems
-
-JuMP supports formulating optimal control problems as large nonlinear programs
-(see, for example, [Optimal control for a Space Shuttle reentry trajectory](@ref)).
-However, the nonlinear interface has a number of limitations (for example, the
-need to write out the dynamics in algebraic form) that mean JuMP might not be
-the right tool for the job.
-
-Alternatives to consider are:
-
- * [CasADi [MATLAB/Python]](https://web.casadi.org),
-   [CasADi.jl](https://github.com/ichatzinikolaidis/CasADi.jl)
- * [InfiniteOpt.jl](https://github.com/infiniteopt/InfiniteOpt.jl)
- * [pyomo.DAE [Python]](https://pyomo.readthedocs.io/en/stable/modeling_extensions/dae.html)
-
 ### Disciplined convex programming
 
 JuMP does not support [disciplined convex programming (DCP)](https://dcp.stanford.edu).

diff --git a/docs/src/tutorials/applications/two_stage_stochastic.jl b/docs/src/tutorials/applications/two_stage_stochastic.jl
@@ -8,6 +8,12 @@
 # The purpose of this tutorial is to demonstrate how to model and solve a
 # two-stage stochastic program.
 
+# !!! info
+#     The JuMP extension [InfiniteOpt.jl](../../packages/InfiniteOpt.md) can also be
+#     used to model and solve two-stage stochastic programs.
+#     The JuMP extension [SDDP.jl](../../packages/SDDP.md) can be
+#     used to model and solve multi-stage stochastic programs.
+
 # This tutorial uses the following packages
 
 using JuMP

diff --git a/docs/src/tutorials/nonlinear/nested_problems.jl b/docs/src/tutorials/nonlinear/nested_problems.jl
@@ -32,6 +32,10 @@
 # For a simpler example of writing a user-defined operator,
 # see the [User-defined Hessians](@ref) tutorial.
 
+# !!! info
+#     The JuMP extension [BilevelJuMP.jl](../../packages/BilevelJuMP.md) can also be used to model and
+#     solve bilevel optimization problems.
+
 # This tutorial uses the following packages:
 
 using JuMP

diff --git a/docs/src/tutorials/nonlinear/rocket_control.jl b/docs/src/tutorials/nonlinear/rocket_control.jl
@@ -12,6 +12,10 @@
 
 # The example is an optimal control problem of a nonlinear rocket.
 
+# !!! info
+#     The JuMP extension [InfiniteOpt.jl](../../packages/InfiniteOpt.md) can also be
+#     used to model and solve optimal control problems.
+
 # This tutorial uses the following packages:
 
 using JuMP

diff --git a/docs/src/tutorials/nonlinear/space_shuttle_reentry_trajectory.jl b/docs/src/tutorials/nonlinear/space_shuttle_reentry_trajectory.jl
@@ -31,6 +31,10 @@
 # ["Practical Methods for Optimal Control and Estimation Using Nonlinear Programming"](https://epubs.siam.org/doi/book/10.1137/1.9780898718577),
 # by John T. Betts.
 
+# !!! info
+#     The JuMP extension [InfiniteOpt.jl](../../packages/InfiniteOpt.md) can also be
+#     used to model and solve optimal control problems.
+
 # !!! tip
 #     This tutorial is a more-complicated version of the [Rocket Control](@ref) example.
 #     If you are new to solving nonlinear programs in JuMP, you may want to start there instead.