WIP: only bridge if needed

src/JuMP.jl

@@ -236,15 +236,30 @@ mutable struct Model <: AbstractModel
 end
 
 """
-    Model(; caching_mode::MOIU.CachingOptimizerMode=MOIU.AUTOMATIC)
+    Model(
+        [optimizer_factory];
+        caching_mode::MOIU.CachingOptimizerMode = MOIU.AUTOMATIC,
+        bridge_constraints::Bool = true,
+        auto_bridge::Bool = false,
+    )
+
+Return a new JuMP model based on a `CachingOptimizer` in `caching_mode`.
+
+See [`set_optimizer`](@ref) for the description of the `optimizer_factory` and
+`bridge_constraints` arguments.
+
+If `optimizer_factory` is not provided, use [`set_optimizer`](@ref) to set the
+optimizer before calling [`optimize!`](@ref).
 
-Return a new JuMP model without any optimizer; the model is stored the model in
-a cache. The mode of the `CachingOptimizer` storing this cache is
-`caching_mode`. Use [`set_optimizer`](@ref) to set the optimizer before
-calling [`optimize!`](@ref).
+If `auto_bridge == true`, the `CachingOptimizer` will automatically add bridges
+to the underlying optimizer only if necessary, and ignore the
+`bridge_constraints` argument.
 """
-function Model(;
+function Model(
+    optimizer_factory = nothing;
     caching_mode::MOIU.CachingOptimizerMode = MOIU.AUTOMATIC,
+    bridge_constraints::Bool = true,
+    auto_bridge::Bool = false,
     solver = nothing,
 )
     if solver !== nothing
@@ -254,41 +269,20 @@ function Model(;
             "(https://jump.dev/JuMP.jl/latest/) for latest syntax.",
         )
     end
-    universal_fallback = MOIU.UniversalFallback(MOIU.Model{Float64}())
-    caching_opt = MOIU.CachingOptimizer(universal_fallback, caching_mode)
-    return direct_model(caching_opt)
-end
-
-"""
-    Model(optimizer_factory;
-          caching_mode::MOIU.CachingOptimizerMode=MOIU.AUTOMATIC,
-          bridge_constraints::Bool=true)
-
-Return a new JuMP model with the provided optimizer and bridge settings. This
-function is equivalent to:
-```julia
-    model = Model()
-    set_optimizer(model, optimizer_factory,
-                  bridge_constraints=bridge_constraints)
-    return model
-```
-See [`set_optimizer`](@ref) for the description of the `optimizer_factory` and
-`bridge_constraints` arguments.
-
-## Examples
-
-The following creates a model with the optimizer set to `Ipopt`:
-```julia
-model = Model(Ipopt.Optimizer)
-```
-"""
-function Model(optimizer_factory; bridge_constraints::Bool = true, kwargs...)
-    model = Model(; kwargs...)
-    set_optimizer(
-        model,
-        optimizer_factory,
-        bridge_constraints = bridge_constraints,
+    cache = MOIU.UniversalFallback(MOIU.Model{Float64}())
+    caching_opt = MOIU.CachingOptimizer(
+        cache;
+        mode = caching_mode,
+        auto_bridge = auto_bridge,
     )
+    model = direct_model(caching_opt)
+    if optimizer_factory !== nothing
+        set_optimizer(
+            model,
+            optimizer_factory,
+            bridge_constraints = !auto_bridge && bridge_constraints
+        )
+    end
     return model
 end
 
@@ -718,7 +712,7 @@ the attribute MathOptInterface.SolveTime()" if the attribute is
 not implemented.
 """
 function solve_time(model::Model)
-    return MOI.get(model, MOI.SolveTime())
+    return MOI.get(model, MOI.SolveTimeSec())
 end
 
 """

src/aff_expr.jl

@@ -555,7 +555,7 @@ end
 function AffExpr(m::Model, f::MOI.ScalarAffineFunction)
     aff = AffExpr()
     for t in f.terms
-        add_to_expression!(aff, t.coefficient, VariableRef(m, t.variable_index))
+        add_to_expression!(aff, t.coefficient, VariableRef(m, t.variable))
     end
     aff.constant = f.constant
     return aff

src/quad_expr.jl

@@ -531,8 +531,8 @@ function QuadExpr(m::Model, f::MOI.ScalarQuadraticFunction)
         AffExpr(m, MOI.ScalarAffineFunction(f.affine_terms, f.constant)),
     )
     for t in f.quadratic_terms
-        v1 = t.variable_index_1
-        v2 = t.variable_index_2
+        v1 = t.variable_1
+        v2 = t.variable_2
         coef = t.coefficient
         if v1 == v2
             coef /= 2

test/print.jl

@@ -928,7 +928,7 @@ end
     MOI.set(mockoptimizer, MOI.SimplexIterations(), 1)
     MOI.set(mockoptimizer, MOI.BarrierIterations(), 1)
     MOI.set(mockoptimizer, MOI.NodeCount(), 1)
-    MOI.set(mockoptimizer, MOI.SolveTime(), 5.0)
+    MOI.set(mockoptimizer, MOI.SolveTimeSec(), 5.0)
 
     @test sprint(show, solution_summary(model)) == """
 * Solver : Mock