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Bump compat for Introduction to OptimizationProblems.jl tutorial #141

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merged 3 commits into from
Sep 25, 2024
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Bump compat for Introduction to OptimizationProblems.jl tutorial #141

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a5009c9
Update index.jmd
tmigot Sep 25, 2024
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Update Project.toml
tmigot Sep 25, 2024
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Update tutorials/introduction-to-optimizationproblems/index.jmd
tmigot Sep 25, 2024
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10 changes: 5 additions & 5 deletions tutorials/introduction-to-optimizationproblems/Project.toml
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Expand Up @@ -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"
4 changes: 2 additions & 2 deletions tutorials/introduction-to-optimizationproblems/index.jmd
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Expand Up @@ -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
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