Fix printing test for JuMP v1.23.0

docs/Project.toml

@@ -19,7 +19,7 @@ InfiniteOpt = "0.5"
 Ipopt = "1.6"
 HiGHS = "1"
 julia = "1.6"
-JuMP = "1.22"
+JuMP = "1.23"
 Literate = "2.18"
 Plots = "1"
 SpecialFunctions = "2"

docs/src/guide/measure.md

@@ -342,12 +342,12 @@ tmodel = transformation_model(model);
 
 # output
 A JuMP Model
-Minimization problem with:
-Variables: 3
-Objective function type: QuadExpr
-Model mode: AUTOMATIC
-CachingOptimizer state: NO_OPTIMIZER
-Solver name: No optimizer attached.
+├ solver: none
+├ objective_sense: MIN_SENSE
+│ └ objective_function_type: QuadExpr
+├ num_variables: 3
+├ num_constraints: 0
+└ Names registered in the model: none
 
 ```
 
@@ -384,12 +384,12 @@ trans_m = transformation_model(model);
 
 # output
 A JuMP Model
-Minimization problem with:
-Variables: 5
-Objective function type: QuadExpr
-Model mode: AUTOMATIC
-CachingOptimizer state: NO_OPTIMIZER
-Solver name: No optimizer attached.
+├ solver: none
+├ objective_sense: MIN_SENSE
+│ └ objective_function_type: QuadExpr
+├ num_variables: 5
+├ num_constraints: 0
+└ Names registered in the model: none
 
 ```
 Now let's look again at the number of supports, the transcription of `u`, and the 
@@ -442,12 +442,12 @@ tmodel = transformation_model(model);
 
 # output
 A JuMP Model
-Minimization problem with:
-Variables: 2
-Objective function type: QuadExpr
-Model mode: AUTOMATIC
-CachingOptimizer state: NO_OPTIMIZER
-Solver name: No optimizer attached.
+├ solver: none
+├ objective_sense: MIN_SENSE
+│ └ objective_function_type: QuadExpr
+├ num_variables: 2
+├ num_constraints: 0
+└ Names registered in the model: none
 
 ```
 Then we get the supports are consistent for `u` and the integral:

docs/src/guide/model.md

@@ -32,7 +32,7 @@ Feasibility problem with:
   Measures: 0
 Transformation backend information:
   Backend type: TranscriptionBackend
-  Solver name: No optimizer attached.
+  Solver: none
   Transformation built and up-to-date: false
 ```
 Ultimately, `model` will be solved via a transformation backend. By default, 
@@ -52,7 +52,7 @@ Feasibility problem with:
   Measures: 0
 Transformation backend information:
   Backend type: TranscriptionBackend
-  Solver name: No optimizer attached.
+  Solver: none
   Transformation built and up-to-date: false
 ```
 
@@ -72,7 +72,7 @@ Feasibility problem with:
   Measures: 0
 Transformation backend information:
   Backend type: TranscriptionBackend
-  Solver name: Ipopt
+  Solver: Ipopt
   Transformation built and up-to-date: false
 ```
 For completeness, the table of currently supported JuMP compatible optimizers 
@@ -96,7 +96,7 @@ Feasibility problem with:
   Measures: 0
 Transformation backend information:
   Backend type: TranscriptionBackend
-  Solver name: Ipopt
+  Solver: Ipopt
   Transformation built and up-to-date: false
 ```
 
@@ -120,11 +120,11 @@ julia> using InfiniteOpt, Ipopt
 julia> backend = TranscriptionBackend(Ipopt.Optimizer)
 A TranscriptionBackend that uses a
 A JuMP Model
-Feasibility problem with:
-Variables: 0
-Model mode: AUTOMATIC
-CachingOptimizer state: EMPTY_OPTIMIZER
-Solver name: Ipopt
+├ solver: Ipopt
+├ objective_sense: FEASIBILITY_SENSE
+├ num_variables: 0
+├ num_constraints: 0
+└ Names registered in the model: none
 ```
 
 We query the underlying transformation backend, transformation model, and transformation
@@ -137,19 +137,19 @@ julia> using InfiniteOpt; model = InfiniteModel();
 julia> tbackend = transformation_backend(model)
 A TranscriptionBackend that uses a
 A JuMP Model
-Feasibility problem with:
-Variables: 0
-Model mode: AUTOMATIC
-CachingOptimizer state: NO_OPTIMIZER
-Solver name: No optimizer attached.
+├ solver: none
+├ objective_sense: FEASIBILITY_SENSE
+├ num_variables: 0
+├ num_constraints: 0
+└ Names registered in the model: none
 
 julia> tmodel = transformation_model(model)
 A JuMP Model
-Feasibility problem with:
-Variables: 0
-Model mode: AUTOMATIC
-CachingOptimizer state: NO_OPTIMIZER
-Solver name: No optimizer attached.
+├ solver: none
+├ objective_sense: FEASIBILITY_SENSE
+├ num_variables: 0
+├ num_constraints: 0
+└ Names registered in the model: none
 
 julia> data = transformation_data(model);
 ```
@@ -163,11 +163,11 @@ julia> set_transformation_backend(model, TranscriptionBackend(Ipopt.Optimizer))
 julia> tbackend = transformation_backend(model)
 A TranscriptionBackend that uses a
 A JuMP Model
-Feasibility problem with:
-Variables: 0
-Model mode: AUTOMATIC
-CachingOptimizer state: EMPTY_OPTIMIZER
-Solver name: Ipopt
+├ solver: Ipopt
+├ objective_sense: FEASIBILITY_SENSE
+├ num_variables: 0
+├ num_constraints: 0
+└ Names registered in the model: none
 ```
 Again, since `TranscriptionBackend` is the default, the following models are equivalent:
 ```jldoctest
@@ -187,7 +187,7 @@ Feasibility problem with:
   Measures: 0
 Transformation backend information:
   Backend type: TranscriptionBackend
-  Solver name: Ipopt
+  Solver: Ipopt
   Transformation built and up-to-date: false
 ```
 

docs/src/guide/optimize.md

@@ -97,27 +97,27 @@ let's extract the transformation backend from the example above in the basic usa
 julia> backend = transformation_backend(model)
 A TranscriptionBackend that uses a
 A JuMP Model
-Minimization problem with:
-Variables: 11
-Objective function type: AffExpr
-`AffExpr`-in-`MathOptInterface.EqualTo{Float64}`: 1 constraint
-`AffExpr`-in-`MathOptInterface.GreaterThan{Float64}`: 10 constraints
-`VariableRef`-in-`MathOptInterface.GreaterThan{Float64}`: 11 constraints
-Model mode: AUTOMATIC
-CachingOptimizer state: ATTACHED_OPTIMIZER
-Solver name: Ipopt
+├ solver: Ipopt
+├ objective_sense: MIN_SENSE
+│ └ objective_function_type: AffExpr
+├ num_variables: 11
+├ num_constraints: 22
+│ ├ AffExpr in MOI.EqualTo{Float64}: 1
+│ ├ AffExpr in MOI.GreaterThan{Float64}: 10
+│ └ VariableRef in MOI.GreaterThan{Float64}: 11
+└ Names registered in the model: none
 
 julia> tmodel = transformation_model(model)
 A JuMP Model
-Minimization problem with:
-Variables: 11
-Objective function type: AffExpr
-`AffExpr`-in-`MathOptInterface.EqualTo{Float64}`: 1 constraint
-`AffExpr`-in-`MathOptInterface.GreaterThan{Float64}`: 10 constraints
-`VariableRef`-in-`MathOptInterface.GreaterThan{Float64}`: 11 constraints
-Model mode: AUTOMATIC
-CachingOptimizer state: ATTACHED_OPTIMIZER
-Solver name: Ipopt
+├ solver: Ipopt
+├ objective_sense: MIN_SENSE
+│ └ objective_function_type: AffExpr
+├ num_variables: 11
+├ num_constraints: 22
+│ ├ AffExpr in MOI.EqualTo{Float64}: 1
+│ ├ AffExpr in MOI.GreaterThan{Float64}: 10
+│ └ VariableRef in MOI.GreaterThan{Float64}: 11
+└ Names registered in the model: none
 ```
 
 The `JuMP` variable(s) stored in the transformation backend that correspond to a 

docs/src/guide/transcribe.md

@@ -68,16 +68,16 @@ julia> build_transformation_backend!(inf_model)
 
 julia> trans_model = transformation_model(inf_model)
 A JuMP Model
-Minimization problem with:
-Variables: 4
-Objective function type: AffExpr
-`AffExpr`-in-`MathOptInterface.EqualTo{Float64}`: 1 constraint
-`QuadExpr`-in-`MathOptInterface.LessThan{Float64}`: 3 constraints
-`VariableRef`-in-`MathOptInterface.GreaterThan{Float64}`: 3 constraints
-`VariableRef`-in-`MathOptInterface.ZeroOne`: 1 constraint
-Model mode: AUTOMATIC
-CachingOptimizer state: NO_OPTIMIZER
-Solver name: No optimizer attached.
+├ solver: none
+├ objective_sense: MIN_SENSE
+│ └ objective_function_type: AffExpr
+├ num_variables: 4
+├ num_constraints: 8
+│ ├ AffExpr in MOI.EqualTo{Float64}: 1
+│ ├ QuadExpr in MOI.LessThan{Float64}: 3
+│ ├ VariableRef in MOI.GreaterThan{Float64}: 3
+│ └ VariableRef in MOI.ZeroOne: 1
+└ Names registered in the model: none
 
 julia> print(trans_model)
 Min 2 z + y(0.0) + y(5.0) + y(10.0)
@@ -357,27 +357,27 @@ which wraps [`build_transcription_backend!`](@ref InfiniteOpt.TranscriptionOpt.b
 julia> backend1 = TranscriptionBackend() # make an empty backend
 A TranscriptionBackend that uses a
 A JuMP Model
-Feasibility problem with:
-Variables: 0
-Model mode: AUTOMATIC
-CachingOptimizer state: NO_OPTIMIZER
-Solver name: No optimizer attached.
+├ solver: none
+├ objective_sense: FEASIBILITY_SENSE
+├ num_variables: 0
+├ num_constraints: 0
+└ Names registered in the model: none
 
 julia> build_transformation_backend!(inf_model); 
 
 julia> backend2 = transformation_backend(inf_model) # generate from an InfiniteModel
 A TranscriptionBackend that uses a
 A JuMP Model
-Minimization problem with:
-Variables: 4
-Objective function type: AffExpr
-`AffExpr`-in-`MathOptInterface.EqualTo{Float64}`: 1 constraint
-`QuadExpr`-in-`MathOptInterface.LessThan{Float64}`: 3 constraints
-`VariableRef`-in-`MathOptInterface.GreaterThan{Float64}`: 3 constraints
-`VariableRef`-in-`MathOptInterface.ZeroOne`: 1 constraint
-Model mode: AUTOMATIC
-CachingOptimizer state: NO_OPTIMIZER
-Solver name: No optimizer attached.
+├ solver: none
+├ objective_sense: MIN_SENSE
+│ └ objective_function_type: AffExpr
+├ num_variables: 4
+├ num_constraints: 8
+│ ├ AffExpr in MOI.EqualTo{Float64}: 1
+│ ├ QuadExpr in MOI.LessThan{Float64}: 3
+│ ├ VariableRef in MOI.GreaterThan{Float64}: 3
+│ └ VariableRef in MOI.ZeroOne: 1
+└ Names registered in the model: none
 ```
 The call to `build_transformation_backend!` is the backbone 
 behind infinite model transformation and is what encapsulates all the methods to 
@@ -391,11 +391,11 @@ via [`transformation_model`](@ref):
 ```jldoctest transcribe; setup = :(empty!(inf_model.backend))
 julia> transformation_model(inf_model)
 A JuMP Model
-Feasibility problem with:
-Variables: 0
-Model mode: AUTOMATIC
-CachingOptimizer state: NO_OPTIMIZER
-Solver name: No optimizer attached.
+├ solver: none
+├ objective_sense: FEASIBILITY_SENSE
+├ num_variables: 0
+├ num_constraints: 0
+└ Names registered in the model: none
 ```
 Here we observe that such a model is currently empty and hasn't been populated 
 yet.

docs/src/tutorials/quick_start.md

@@ -72,7 +72,7 @@ Feasibility problem with:
   Measures: 0
 Transformation backend information:
   Backend type: TranscriptionBackend
-  Solver name: Ipopt
+  Solver: Ipopt
   Transformation built and up-to-date: false
 ```
 Learn more about `InfiniteModel`s and optimizers on our 

src/TranscriptionOpt/model.jl

@@ -130,6 +130,20 @@ function TranscriptionBackend(optimizer_constructor; kwargs...)
     return InfiniteOpt.JuMPBackend{Transcription}(model, TranscriptionData())
 end
 
+# Get the solver name from MOI
+# Inspired by https://github.com/jump-dev/JuMP.jl/blob/ce946b7092c45bdac916c9b531a13a5b929d45f0/src/print.jl#L281-L291
+function _try_solver_name(model)
+    if mode(model) != JuMP.DIRECT &&
+       MOI.Utilities.state(backend(model)) == MOI.Utilities.NO_OPTIMIZER
+        return "none"
+    end
+    try
+        return MOI.get(backend(model), MOI.SolverName())
+    catch
+        return "unknown"
+    end
+end
+
 # Printing
 function JuMP.show_backend_summary(
     io::IO,
@@ -148,9 +162,7 @@ function JuMP.show_backend_summary(
         # TODO add approximation method info (requires InfiniteOpt refactoring)
     end
     # solver name
-    moi_summary = sprint(JuMP.show_backend_summary, backend.model)
-    solver_str = filter(startswith("Solver"), split(moi_summary, "\n"))[1]
-    println(io, "  ", solver_str)
+    println(io, "  Solver: ", _try_solver_name(backend.model))
     return
 end