foo

ocots · ocots · commit 28bd50958812 · 2026-03-15T14:35:10.000+01:00
diff --git a/docs/Project.toml b/docs/Project.toml
@@ -20,7 +20,6 @@ MarkdownAST = "d0879d2d-cac2-40c8-9cee-1863dc0c7391"
 NLPModelsIpopt = "f4238b75-b362-5c4c-b852-0801c9a21d71"
 NLPModelsKnitro = "bec4dd0d-7755-52d5-9a02-22f0ffc7efcb"
 NonlinearSolve = "8913a72c-1f9b-4ce2-8d82-65094dcecaec"
-OptimalControl = "5f98b655-cc9a-415a-b60e-744165666948"
 OrdinaryDiffEq = "1dea7af3-3e70-54e6-95c3-0bf5283fa5ed"
 Plots = "91a5bcdd-55d7-5caf-9e0b-520d859cae80"
 
diff --git a/docs/make.jl b/docs/make.jl
@@ -1,3 +1,8 @@
+# to run the documentation generation:
+# julia --project=. docs/make.jl
+pushfirst!(LOAD_PATH, joinpath(@__DIR__, ".."))
+pushfirst!(LOAD_PATH, @__DIR__)
+
 # control-toolbox packages
 using OptimalControl
 using CTBase
diff --git a/docs/src/assets/Manifest.toml b/docs/src/assets/Manifest.toml
diff --git a/docs/src/assets/Project.toml b/docs/src/assets/Project.toml
@@ -20,7 +20,6 @@ MarkdownAST = "d0879d2d-cac2-40c8-9cee-1863dc0c7391"
 NLPModelsIpopt = "f4238b75-b362-5c4c-b852-0801c9a21d71"
 NLPModelsKnitro = "bec4dd0d-7755-52d5-9a02-22f0ffc7efcb"
 NonlinearSolve = "8913a72c-1f9b-4ce2-8d82-65094dcecaec"
-OptimalControl = "5f98b655-cc9a-415a-b60e-744165666948"
 OrdinaryDiffEq = "1dea7af3-3e70-54e6-95c3-0bf5283fa5ed"
 Plots = "91a5bcdd-55d7-5caf-9e0b-520d859cae80"
 
diff --git a/docs/src/example-double-integrator-energy.md b/docs/src/example-double-integrator-energy.md
@@ -135,7 +135,7 @@ We are now ready to solve the shooting equations.
 
 ```@example main
 # auxiliary in-place NLE function
-nle!(s, p0, λ) = s[:] = S(p0)
+nle!(s, p0, _) = s[:] = S(p0)
 
 # initial guess for the Newton solver
 p0_guess = [1, 1]
@@ -169,7 +169,11 @@ plot(indirect_sol)
 
 ## State constraint
 
-The following example illustrates both direct and indirect solution approaches for a constrained energy minimization problem. The workflow demonstrates a practical strategy: a direct method on a coarse grid first identifies the problem structure and provides an initial guess for the indirect method, which then computes a precise solution via shooting based on Pontryagin's Maximum Principle. The direct solution can also be refined using a finer discretization grid for higher accuracy.
+The following example illustrates both direct and indirect solution approaches for the energy minimization problem with a state constraint on the maximal velocity. The workflow demonstrates a practical strategy: a direct method on a coarse grid first identifies the problem structure and provides an initial guess for the indirect method, which then computes a precise solution via shooting based on Pontryagin's Maximum Principle.
+
+!!! note
+
+    The direct solution can be refined using a finer discretization grid for higher accuracy.
 
 ### Direct method: constrained case
 
@@ -280,7 +284,7 @@ We can now solve the shooting equations.
 
 ```@example main
 # auxiliary in-place NLE function
-nle!(s, ξ, λ) = shoot!(s, ξ[1:2], ξ[3], ξ[4])
+nle!(s, ξ, _) = shoot!(s, ξ[1:2], ξ[3], ξ[4])
 
 # initial guess for the Newton solver
 ξ_guess = [p0..., t1, t2]
diff --git a/docs/src/manual-solve.md b/docs/src/manual-solve.md
@@ -1,5 +1,9 @@
 # [Solve a problem](@id manual-solve)
 
+```@meta
+Draft = false
+```
+
 This manual explains how to use the [`solve`](@ref) function to solve optimal control problems with OptimalControl.jl. The `solve` function provides a **descriptive mode** where you specify strategies using symbolic tokens, with automatic option routing and validation.
 
 For advanced usage, see:
