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🤖 Tutorials deployment from 92124205294a28562a5dd3ca86f474cc12bf071a (#…
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Co-authored-by: JSOTutorials bot <[email protected]>
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@abelsiqueira
abelsiqueira and JSOTutorials bot authored Sep 25, 2024
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82 changes: 41 additions & 41 deletions tutorials/introduction-to-optimizationproblems/index.md
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Expand Up @@ -5,11 +5,11 @@
\preamble{Tangi Migot}


[![NLPModels 0.20.0](https://img.shields.io/badge/NLPModels-0.20.0-8b0000?style=flat-square&labelColor=cb3c33)](https://juliasmoothoptimizers.github.io/NLPModels.jl/stable/)
[![NLPModelsJuMP 0.12.1](https://img.shields.io/badge/NLPModelsJuMP-0.12.1-8b0000?style=flat-square&labelColor=cb3c33)](https://juliasmoothoptimizers.github.io/NLPModelsJuMP.jl/stable/)
[![ADNLPModels 0.7.0](https://img.shields.io/badge/ADNLPModels-0.7.0-8b0000?style=flat-square&labelColor=cb3c33)](https://juliasmoothoptimizers.github.io/ADNLPModels.jl/stable/)
![JuMP 1.12.0](https://img.shields.io/badge/JuMP-1.12.0-000?style=flat-square&labelColor=999)
[![OptimizationProblems 0.7.1](https://img.shields.io/badge/OptimizationProblems-0.7.1-8b0000?style=flat-square&labelColor=cb3c33)](https://juliasmoothoptimizers.github.io/OptimizationProblems.jl/stable/)
[![NLPModels 0.21.3](https://img.shields.io/badge/NLPModels-0.21.3-8b0000?style=flat-square&labelColor=cb3c33)](https://jso.dev/NLPModels.jl/stable/)
[![NLPModelsJuMP 0.13.2](https://img.shields.io/badge/NLPModelsJuMP-0.13.2-8b0000?style=flat-square&labelColor=cb3c33)](https://jso.dev/NLPModelsJuMP.jl/stable/)
[![ADNLPModels 0.8.7](https://img.shields.io/badge/ADNLPModels-0.8.7-8b0000?style=flat-square&labelColor=cb3c33)](https://jso.dev/ADNLPModels.jl/stable/)
![JuMP 1.23.2](https://img.shields.io/badge/JuMP-1.23.2-000?style=flat-square&labelColor=999)
[![OptimizationProblems 0.9.0](https://img.shields.io/badge/OptimizationProblems-0.9.0-8b0000?style=flat-square&labelColor=cb3c33)](https://jso.dev/OptimizationProblems.jl/stable/)



Expand All @@ -26,7 +26,7 @@ length(problems)
```

```plaintext
288
372
```


Expand All @@ -39,14 +39,14 @@ jump_model = OptimizationProblems.PureJuMP.zangwil3()

```plaintext
A JuMP Model
Minimization problem with:
Variables: 3
Objective function type: Nonlinear
`JuMP.AffExpr`-in-`MathOptInterface.EqualTo{Float64}`: 3 constraints
Model mode: AUTOMATIC
CachingOptimizer state: NO_OPTIMIZER
Solver name: No optimizer attached.
Names registered in the model: constr1, constr2, constr3, x
├ solver: none
├ objective_sense: MIN_SENSE
│ └ objective_function_type: JuMP.AffExpr
├ num_variables: 3
├ num_constraints: 3
│ └ JuMP.AffExpr in MOI.EqualTo{Float64}: 3
└ Names registered in the model
└ :constr1, :constr2, :constr3, :x
```


Expand All @@ -59,7 +59,7 @@ length(var_problems)
```

```plaintext
94
95
```


Expand All @@ -72,13 +72,13 @@ jump_model_12 = OptimizationProblems.PureJuMP.woods(n=12)

```plaintext
A JuMP Model
Minimization problem with:
Variables: 12
Objective function type: Nonlinear
Model mode: AUTOMATIC
CachingOptimizer state: NO_OPTIMIZER
Solver name: No optimizer attached.
Names registered in the model: x
├ solver: none
├ objective_sense: MIN_SENSE
│ └ objective_function_type: JuMP.NonlinearExpr
├ num_variables: 12
├ num_constraints: 0
└ Names registered in the model
└ :x
```


Expand All @@ -89,13 +89,13 @@ jump_model_120 = OptimizationProblems.PureJuMP.woods(n=120)

```plaintext
A JuMP Model
Minimization problem with:
Variables: 120
Objective function type: Nonlinear
Model mode: AUTOMATIC
CachingOptimizer state: NO_OPTIMIZER
Solver name: No optimizer attached.
Names registered in the model: x
├ solver: none
├ objective_sense: MIN_SENSE
│ └ objective_function_type: JuMP.NonlinearExpr
├ num_variables: 120
├ num_constraints: 0
└ Names registered in the model
└ :x
```


Expand Down Expand Up @@ -134,7 +134,7 @@ length(problems)
```

```plaintext
288
372
```


Expand All @@ -151,8 +151,8 @@ ADNLPModel - Model with automatic differentiation backend ADModelBackend{
ForwardDiffADHvprod,
ForwardDiffADJprod,
ForwardDiffADJtprod,
ForwardDiffADJacobian,
ForwardDiffADHessian,
SparseADJacobian,
SparseADHessian,
ForwardDiffADGHjvprod,
}
Problem name: zangwil3
Expand All @@ -163,7 +163,7 @@ ADNLPModel - Model with automatic differentiation backend ADModelBackend{
low/upp: ⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅ 0 low/upp: ⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅ 0
fixed: ⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅ 0 fixed: ████████████████████ 3
infeas: ⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅ 0 infeas: ⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅ 0
nnzh: ( 0.00% sparsity) 6 linear: ████████████████████ 3
nnzh: (100.00% sparsity) 0 linear: ████████████████████ 3
nonlinear: ⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅ 0
nnzj: ( 0.00% sparsity) 9
Expand Down Expand Up @@ -192,7 +192,7 @@ ADNLPModel - Model with automatic differentiation backend ADModelBackend{
EmptyADbackend,
EmptyADbackend,
EmptyADbackend,
ForwardDiffADHessian,
SparseADHessian,
EmptyADbackend,
}
Problem name: woods
Expand All @@ -203,7 +203,7 @@ ADNLPModel - Model with automatic differentiation backend ADModelBackend{
low/upp: ⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅ 0 low/upp: ⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅ 0
fixed: ⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅ 0 fixed: ⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅ 0
infeas: ⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅ 0 infeas: ⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅ 0
nnzh: ( 0.00% sparsity) 78 linear: ⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅ 0
nnzh: ( 73.08% sparsity) 21 linear: ⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅ 0
nonlinear: ⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅ 0
nnzj: (------% sparsity)
Expand All @@ -230,7 +230,7 @@ ADNLPModel - Model with automatic differentiation backend ADModelBackend{
EmptyADbackend,
EmptyADbackend,
EmptyADbackend,
ForwardDiffADHessian,
SparseADHessian,
EmptyADbackend,
}
Problem name: woods
Expand All @@ -241,7 +241,7 @@ ADNLPModel - Model with automatic differentiation backend ADModelBackend{
low/upp: ⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅ 0 low/upp: ⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅ 0
fixed: ⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅ 0 fixed: ⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅ 0
infeas: ⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅ 0 infeas: ⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅ 0
nnzh: ( 0.00% sparsity) 7260 linear: ⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅ 0
nnzh: ( 97.11% sparsity) 210 linear: ⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅ 0
nonlinear: ⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅ 0
nnzj: (------% sparsity)
Expand All @@ -258,9 +258,9 @@ ADNLPModel - Model with automatic differentiation backend ADModelBackend{



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)
```

```plaintext
Expand All @@ -270,7 +270,7 @@ ADNLPModel - Model with automatic differentiation backend ADModelBackend{
EmptyADbackend,
EmptyADbackend,
EmptyADbackend,
ForwardDiffADHessian,
SparseADHessian,
EmptyADbackend,
}
Problem name: woods
Expand All @@ -281,7 +281,7 @@ ADNLPModel - Model with automatic differentiation backend ADModelBackend{
low/upp: ⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅ 0 low/upp: ⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅ 0
fixed: ⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅ 0 fixed: ⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅ 0
infeas: ⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅ 0 infeas: ⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅ 0
nnzh: ( 0.00% sparsity) 78 linear: ⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅ 0
nnzh: ( 73.08% sparsity) 21 linear: ⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅ 0
nonlinear: ⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅ 0
nnzj: (------% sparsity)
Expand Down

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