🤖 Format .jl files

Author: dpo

examples/demo-cutest.jl

@@ -43,4 +43,4 @@ callback =
 stats = AL(nlp, h, atol = 1e-6, verbose = 1, callback = callback)
 print(stats)
 
-finalize(nlp)
+finalize(nlp)

src/AL_alg.jl

@@ -383,4 +383,4 @@ project_y!(nlp::AugLagModel) = project_y!(nlp::AugLagModel, -1e20, 1e20)
 function project_y!(nlp::AugLagModel, ymin::V, ymax::V) where {V}
   nlp.y .= max.(ymin, min.(nlp.y, ymax))
   nlp.μc_y .= nlp.μ .* nlp.cx .- nlp.y
-end
+end

src/utils.jl

@@ -33,9 +33,9 @@ More specifically, construct a GenericExecutionStats on the NLPModel of reg_nlp
 This is useful for reducing the number of allocations when calling solve!(..., reg_nlp, stats) and should be used by default.
 Warning: This should *not* be used when adding other solver_specific entries that do not have the current scalar type. 
 """
-function RegularizedExecutionStats(reg_nlp :: AbstractRegularizedNLPModel{T, V}) where{T, V}
+function RegularizedExecutionStats(reg_nlp::AbstractRegularizedNLPModel{T, V}) where {T, V}
   stats = GenericExecutionStats(reg_nlp.model, solver_specific = Dict{Symbol, T}())
   set_solver_specific!(stats, :smooth_obj, T(Inf))
   set_solver_specific!(stats, :nonsmooth_obj, T(Inf))
   return stats
-end
+end