🤖 Format .jl files (#113)

Co-authored-by: tmigot <[email protected]>

Author: github-actions[bot]

src/JSOSuite.jl

@@ -424,7 +424,6 @@ include("solve.jl")
     function minimize(::Val{:CaNNOLeS}, nlp; kwargs...)
       return CaNNOLeS.cannoles(nlp; linsolve = :ldlfactorizations, kwargs...)
     end
-
   end
 end
 
@@ -457,7 +456,8 @@ end
   @require NLPModelsKnitro = "bec4dd0d-7755-52d5-9a02-22f0ffc7efcb" begin
     @init begin
       @require NLPModelsKnitro = "bec4dd0d-7755-52d5-9a02-22f0ffc7efcb" begin
-        JSOSuite.optimizers[JSOSuite.optimizers.name .== "KNITRO", :is_available] .= KNITRO.has_knitro()
+        JSOSuite.optimizers[JSOSuite.optimizers.name .== "KNITRO", :is_available] .=
+          KNITRO.has_knitro()
       end
     end
     include("solvers/knitro_solve.jl")
@@ -561,7 +561,6 @@ function bmark_solvers end
 
 @init begin
   @require SolverBenchmark = "581a75fa-a23a-52d0-a590-d6201de2218a" begin
-
     function SolverBenchmark.bmark_solvers(
       problems,
       solver_names::Vector{String},
@@ -588,7 +587,6 @@ function bmark_solvers end
       end
       return SolverBenchmark.bmark_solvers(solvers, problems; kwargs...)
     end
-
   end
 end
 

src/selection.jl

@@ -160,5 +160,6 @@ end
 function generic end
 
 generic(::AbstractNLSModel, optimizers::DataFrame) = optimizers
-generic(::Union{QuadraticModel, LLSModel}, optimizers::DataFrame) = optimizers[optimizers.can_solve_nlp, :]
+generic(::Union{QuadraticModel, LLSModel}, optimizers::DataFrame) =
+  optimizers[optimizers.can_solve_nlp, :]
 generic(::AbstractNLPModel, optimizers::DataFrame) = optimizers[optimizers.can_solve_nlp, :]

src/solve_model.jl

@@ -35,7 +35,12 @@ function minimize(solver_name::String, model::JuMP.Model, args...; kwargs...)
   return minimize(solver_name, nlp, args...; kwargs...)
 end
 
-function minimize(solver::Val{solver_name}, model::JuMP.Model, args...; kwargs...) where {solver_name}
+function minimize(
+  solver::Val{solver_name},
+  model::JuMP.Model,
+  args...;
+  kwargs...,
+) where {solver_name}
   nlp = MathOptNLPModel(model)
   return minimize(solver, nlp, args...; kwargs...)
 end

test/runtests.jl

@@ -63,9 +63,12 @@ end
   @testset "Test $solver_name" for solver_name in JSOSuite.optimizers[!, :name_solver]
     solver_name == :DCIWorkspace && continue
     solver_name == :RipQPSolver && continue
-    is_available = JSOSuite.optimizers[JSOSuite.optimizers.name_solver .== solver_name, :is_available]
-    can_solve_nlp = JSOSuite.optimizers[JSOSuite.optimizers.name_solver .== solver_name, :can_solve_nlp]
-    spec_nls = JSOSuite.optimizers[JSOSuite.optimizers.name_solver .== solver_name, :specialized_nls]
+    is_available =
+      JSOSuite.optimizers[JSOSuite.optimizers.name_solver .== solver_name, :is_available]
+    can_solve_nlp =
+      JSOSuite.optimizers[JSOSuite.optimizers.name_solver .== solver_name, :can_solve_nlp]
+    spec_nls =
+      JSOSuite.optimizers[JSOSuite.optimizers.name_solver .== solver_name, :specialized_nls]
     if is_available[1] && can_solve_nlp[1]
       test_in_place_solve(nlp, solver_name)
       test_in_place_solve(model, solver_name)
@@ -111,7 +114,10 @@ end
     OptimizationProblems.ADNLPProblems.eval(Meta.parse(problem))() for
     problem ∈ meta[(5 .<= meta.nvar .<= 10) .& (meta.ncon .== 0) .& (.!meta.has_bounds), :name]
   ]
-  select = JSOSuite.optimizers[JSOSuite.optimizers.can_solve_nlp .& JSOSuite.optimizers.is_available, :name]
+  select = JSOSuite.optimizers[
+    JSOSuite.optimizers.can_solve_nlp .& JSOSuite.optimizers.is_available,
+    :name,
+  ]
   stats = bmark_solvers(ad_problems, select, atol = 1e-3, max_time = 10.0, verbose = 0)
   @test true # just test that it runs
 end