🤖 Format .jl files (#138)

Co-authored-by: tmigot <tmigot@users.noreply.github.com>

.breakage/get_jso_users.jl

@@ -1,18 +1,18 @@
-import GitHub, PkgDeps  # both export users()
-
-length(ARGS) >= 1 || error("specify at least one JSO package as argument")
-
-jso_repos, _ = GitHub.repos("JuliaSmoothOptimizers")
-jso_names = [splitext(x.name)[1] for x ∈ jso_repos]
-
-name = splitext(ARGS[1])[1]
-name ∈ jso_names || error("argument should be one of ", jso_names)
-
-dependents = String[]
-try
-  global dependents = filter(x -> x ∈ jso_names, PkgDeps.users(name))
-catch e
-  # package not registered; don't insert into dependents
-end
-
-println(dependents)
+import GitHub, PkgDeps  # both export users()
+
+length(ARGS) >= 1 || error("specify at least one JSO package as argument")
+
+jso_repos, _ = GitHub.repos("JuliaSmoothOptimizers")
+jso_names = [splitext(x.name)[1] for x ∈ jso_repos]
+
+name = splitext(ARGS[1])[1]
+name ∈ jso_names || error("argument should be one of ", jso_names)
+
+dependents = String[]
+try
+  global dependents = filter(x -> x ∈ jso_names, PkgDeps.users(name))
+catch e
+  # package not registered; don't insert into dependents
+end
+
+println(dependents)

src/feasibility.jl

@@ -205,15 +205,7 @@ using `lsmr` method from `Krylov.jl`.
 - `infeasible`: `true` if the problem is infeasible.
 - `solved`: `true` if the problem has been successfully solved.
 """
-function TR_lsmr(
-  solver,
-  cz,
-  Jz,
-  ctol::AbstractFloat,
-  Δ::T,
-  normcz::AbstractFloat,
-  Jd,
-) where {T}
+function TR_lsmr(solver, cz, Jz, ctol::AbstractFloat, Δ::T, normcz::AbstractFloat, Jd) where {T}
   Krylov.solve!(
     solver,
     Jz,

src/model-Fletcherpenaltynlp.jl

@@ -284,14 +284,7 @@ end
 
 Redefine the NLPModel function `hprod` to account for Lagrange multiplier of size < ncon.
 """
-function hprod_nln!(
-  nlp::FletcherPenaltyNLP{S, T},
-  x,
-  y,
-  v,
-  Hv;
-  obj_weight = one(S),
-) where {S, T}
+function hprod_nln!(nlp::FletcherPenaltyNLP{S, T}, x, y, v, Hv; obj_weight = one(S)) where {S, T}
   return if nlp.explicit_linear_constraints & (nlp.meta.ncon > 0)
     nlp.lag_mul .= zero(S)
     nlp.lag_mul[nlp.meta.nln] .= y
@@ -327,7 +320,12 @@ end
 
 Redefine the NLPModel function `hprod` to account for Lagrange multiplier of size < ncon.
 """
-function hess_nln(nlp::FletcherPenaltyNLP{S, T}, x::AbstractVector, y; obj_weight = one(S)) where {S, T}
+function hess_nln(
+  nlp::FletcherPenaltyNLP{S, T},
+  x::AbstractVector,
+  y;
+  obj_weight = one(S),
+) where {S, T}
   return if nlp.explicit_linear_constraints & (nlp.meta.ncon > 0)
     nlp.lag_mul .= zero(S)
     nlp.lag_mul[nlp.meta.nln] .= y
@@ -371,11 +369,7 @@ function obj(nlp::FletcherPenaltyNLP{T, S}, x::AbstractVector) where {T, S}
   return fx
 end
 
-function grad!(
-  nlp::FletcherPenaltyNLP{T, S},
-  x::AbstractVector,
-  gx::AbstractVector,
-) where {T, S}
+function grad!(nlp::FletcherPenaltyNLP{T, S}, x::AbstractVector, gx::AbstractVector) where {T, S}
   nvar = get_nvar(nlp)
   @lencheck nvar x gx
   increment!(nlp, :neval_grad)
@@ -406,11 +400,7 @@ function grad!(
   return gx
 end
 
-function objgrad!(
-  nlp::FletcherPenaltyNLP{T, S},
-  x::AbstractVector,
-  gx::AbstractVector,
-) where {T, S}
+function objgrad!(nlp::FletcherPenaltyNLP{T, S}, x::AbstractVector, gx::AbstractVector) where {T, S}
   nvar = get_nvar(nlp)
   @lencheck nvar x gx
   increment!(nlp, :neval_obj)

src/parameters.jl

@@ -261,11 +261,7 @@ mutable struct FPSSSolver{
   }
 end
 
-function FPSSSolver(
-  nlp::AbstractNLPModel{T, S},
-  x0::S = nlp.meta.x0;
-  kwargs...,
-) where {T, S}
+function FPSSSolver(nlp::AbstractNLPModel{T, S}, x0::S = nlp.meta.x0; kwargs...) where {T, S}
   cx0, gx0 = cons(nlp, x0), grad(nlp, x0)
   #Tanj: how to handle stopping criteria where tol_check depends on the State?
   Fptc(atol, rtol, opt0) =

test/runtests.jl

@@ -71,14 +71,14 @@ end
     x0 = T[-1.2; 1.0]
     ℓ, u = zeros(T, 2), 2 * ones(T, 2)
     nlp = ADNLPModel(f, x0, ℓ, u, c, zeros(1), zeros(1))
-  
+
     ϵ = eps(T)^T(1 / 4)
-  
+
     ng0 = norm(grad(nlp, nlp.meta.x0))
-  
+
     stp = NLPStopping(nlp)
     stats = fps_solve(stp)
-  
+
     @test eltype(stats.solution) == T
     @test stats.objective isa T
     @test stats.dual_feas isa T