Bump compat for Introduction to OptimizationProblems.jl tutorial (#141)

tmigot · web-flow · commit 92124205294a · 2024-09-25T15:05:10.000-04:00
* Update index.jmd

* Update Project.toml

* Update tutorials/introduction-to-optimizationproblems/index.jmd
diff --git a/tutorials/introduction-to-optimizationproblems/Project.toml b/tutorials/introduction-to-optimizationproblems/Project.toml
@@ -6,8 +6,8 @@ NLPModelsJuMP = "792afdf1-32c1-5681-94e0-d7bf7a5df49e"
 OptimizationProblems = "5049e819-d29b-5fba-b941-0eee7e64c1c6"
 
 [compat]
-ADNLPModels = "0.7"
-JuMP = "1.1"
-NLPModels = "0.20"
-NLPModelsJuMP = "0.12"
-OptimizationProblems = "0.7"
+ADNLPModels = "0.8"
+JuMP = "1.23"
+NLPModels = "0.21"
+NLPModelsJuMP = "0.13"
+OptimizationProblems = "0.9"
diff --git a/tutorials/introduction-to-optimizationproblems/index.jmd b/tutorials/introduction-to-optimizationproblems/index.jmd
@@ -62,9 +62,9 @@ nlp_12 = OptimizationProblems.ADNLPProblems.woods(n=12)
 ```julia
 nlp_120 = OptimizationProblems.ADNLPProblems.woods(n=120)
 ```
-One of the advantages of these problems is that they are type-stable. Indeed, one can specify the output type with the keyword `type` as follows.
+One of the advantages of these problems is that they are type-stable. Indeed, one can specify the output type with the keyword `type` as follows. Note that in version < 0.8 the argument was `type=Val(DataType)`.
 ```julia
-nlp16_12 = OptimizationProblems.ADNLPProblems.woods(n=12, type=Val(Float16))
+nlp16_12 = OptimizationProblems.ADNLPProblems.woods(n=12, type=Float16)
 ```
 Then, all the API will be compatible with the precised type.
 ```julia
