formatting

src/NonconvexPavito.jl

@@ -12,13 +12,16 @@ import NonconvexCore: optimize!, Workspace
 
 @params struct PavitoIpoptCbcOptions
     nt::NamedTuple
-    subsolver_options
+    subsolver_options::Any
     first_order::Bool
 end
 function PavitoIpoptCbcOptions(;
-    first_order = true, subsolver_options = IpoptOptions(first_order = first_order), kwargs...,
+    first_order = true,
+    subsolver_options = IpoptOptions(first_order = first_order),
+    kwargs...,
 )
-    first_order = !hasproperty(subsolver_options.nt, :hessian_approximation) ||
+    first_order =
+        !hasproperty(subsolver_options.nt, :hessian_approximation) ||
         subsolver_options.nt.hessian_approximation == "limited-memory"
     return PavitoIpoptCbcOptions((; kwargs...), subsolver_options, first_order)
 end
@@ -32,8 +35,10 @@ end
     counter::Base.RefValue{Int}
 end
 function PavitoIpoptCbcWorkspace(
-    model::VecModel, x0::AbstractVector = getinit(model);
-    options = PavitoIpoptCbcOptions(), kwargs...,
+    model::VecModel,
+    x0::AbstractVector = getinit(model);
+    options = PavitoIpoptCbcOptions(),
+    kwargs...,
 )
     integers = model.integer
     @assert length(integers) == length(x0)
@@ -49,20 +54,24 @@ function PavitoIpoptCbcWorkspace(
     end
     optimizer = JuMP.optimizer_with_attributes(
         Pavito.Optimizer,
-        "cont_solver" => nl_solver, Dict(solver_options)...,
+        "cont_solver" => nl_solver,
+        Dict(solver_options)...,
         "mip_solver" => JuMP.optimizer_with_attributes(Cbc.Optimizer),
     )
     problem, counter = get_jump_problem(
-        model, copy(x0); first_order = options.first_order,
-        optimizer = optimizer, integers = integers,
+        model,
+        copy(x0);
+        first_order = options.first_order,
+        optimizer = optimizer,
+        integers = integers,
     )
     return PavitoIpoptCbcWorkspace(model, problem, copy(x0), integers, options, counter)
 end
 @params struct PavitoIpoptCbcResult <: AbstractResult
-    minimizer
-    minimum
-    problem
-    status
+    minimizer::Any
+    minimum::Any
+    problem::Any
+    status::Any
     fcalls::Int
 end
 
@@ -79,13 +88,17 @@ function optimize!(workspace::PavitoIpoptCbcWorkspace)
     term_status = MOI.get(moi_model, MOI.TerminationStatus())
     primal_status = MOI.get(moi_model, MOI.PrimalStatus())
     return PavitoIpoptCbcResult(
-        minimizer, objval, problem, (term_status, primal_status), counter[],
+        minimizer,
+        objval,
+        problem,
+        (term_status, primal_status),
+        counter[],
     )
 end
 
 struct PavitoIpoptCbcAlg <: AbstractOptimizer end
 
-function Workspace(model::VecModel, optimizer::PavitoIpoptCbcAlg, args...; kwargs...,)
+function Workspace(model::VecModel, optimizer::PavitoIpoptCbcAlg, args...; kwargs...)
     return PavitoIpoptCbcWorkspace(model, args...; kwargs...)
 end
 

test/runtests.jl

@@ -1,7 +1,7 @@
 using NonconvexPavito, LinearAlgebra, Test
 
 f(x::AbstractVector) = sqrt(x[2])
-g(x::AbstractVector, a, b) = (a*x[1] + b)^3 - x[2]
+g(x::AbstractVector, a, b) = (a * x[1] + b)^3 - x[2]
 
 @testset "First order - $first_order" for first_order in [true, false]
     options = PavitoIpoptCbcOptions(first_order = first_order)