Remove docs DiffEqDevTools dependency (#4729)

Co-authored-by: ChrisRackauckas-Claude <accounts@chrisrackauckas.com>

Author: ChrisRackauckas-Claude

docs/Project.toml

@@ -7,7 +7,6 @@ CommonSolve = "38540f10-b2f7-11e9-35d8-d573e4eb0ff2"
 ControlSystemsBase = "aaaaaaaa-a6ca-5380-bf3e-84a91bcd477e"
 ControlSystemsMTK = "687d7614-c7e5-45fc-bfc3-9ee385575c88"
 DataInterpolations = "82cc6244-b520-54b8-b5a6-8a565e85f1d0"
-DiffEqDevTools = "f3b72e0c-5b89-59e1-b016-84e28bfd966d"
 Distributions = "31c24e10-a181-5473-b8eb-7969acd0382f"
 Documenter = "e30172f5-a6a5-5a46-863b-614d45cd2de4"
 DynamicQuantities = "06fc5a27-2a28-4c7c-a15d-362465fb6821"
@@ -44,7 +43,6 @@ CommonSolve = "0.2"
 ControlSystemsBase = "1.20"
 ControlSystemsMTK = "2.6"
 DataInterpolations = "8"
-DiffEqDevTools = "2, 3"
 Distributions = "0.25"
 Documenter = "1"
 DynamicQuantities = "1"

docs/src/tutorials/dynamic_optimization.md

@@ -41,12 +41,12 @@ What we would like to optimize here is the final height of the rocket. We do thi
 
 Now we can construct a problem and solve it. Let us use JuMP as our backend here. Note that the package trigger is actually [InfiniteOpt](https://infiniteopt.github.io/InfiniteOpt.jl/stable/), and not JuMP - this package includes JuMP but is designed for optimization on function spaces. Additionally we need to load the solver package - we will use [Ipopt](https://github.com/jump-dev/Ipopt.jl) here (a good choice in general).
 
-Here we have also loaded DiffEqDevTools because we will need to construct the ODE tableau. This is only needed if one desires a custom ODE tableau for the collocation - by default the solver will use RadauIIA5.
+The collocation solver uses a default ODE tableau. A custom tableau can still be supplied when needed.
 
 ```@example dynamic_opt
-using InfiniteOpt, Ipopt, DiffEqDevTools
+using InfiniteOpt, Ipopt
 jprob = JuMPDynamicOptProblem(rocket, [u0map; pmap], (ts, te); dt = 0.001)
-jsol = solve(jprob, JuMPCollocation(Ipopt.Optimizer, constructRadauIIA5()));
+jsol = solve(jprob, JuMPCollocation(Ipopt.Optimizer));
 ```
 
 The solution has three fields: `jsol.sol` is the ODE solution for the states, `jsol.input_sol` is the ODE solution for the inputs, and `jsol.model` is the wrapped model that we can use to query things like objective and constraint residuals.