🤖 Format .jl files

Author: dpo

src/RegularizedOptimization.jl

@@ -8,7 +8,12 @@ using Arpack, ProximalOperators
 
 # dependencies from us
 using LinearOperators,
-  ManualNLPModels, NLPModels, NLPModelsModifiers, RegularizedProblems, ShiftedProximalOperators, SolverCore
+  ManualNLPModels,
+  NLPModels,
+  NLPModelsModifiers,
+  RegularizedProblems,
+  ShiftedProximalOperators,
+  SolverCore
 using Percival: AugLagModel, update_y!, update_μ!
 
 const callback_docstring = "

src/TRDH_alg.jl

@@ -190,7 +190,7 @@ function TRDH(
   selected::AbstractVector{<:Integer} = 1:length(x0),
   kwargs...,
 ) where {R <: Real, F, G, H, DQN <: AbstractDiagonalQuasiNewtonOperator, X}
-  nlp = NLPModel(x0, f, grad=∇f!)
+  nlp = NLPModel(x0, f, grad = ∇f!)
   reg_nlp = RegularizedNLPModel(nlp, h, selected)
   stats = TRDH(
     reg_nlp;

src/TR_alg.jl

@@ -59,7 +59,9 @@ function TRSolver(
     shifted(reg_nlp.h, xk, l_bound_m_x, u_bound_m_x, reg_nlp.selected) :
     shifted(reg_nlp.h, xk, T(1), χ)
 
-  Bk = isa(reg_nlp.model, QuasiNewtonModel) ? hess_op(reg_nlp.model, xk) : hess_op!(reg_nlp.model, xk, similar(xk))
+  Bk =
+    isa(reg_nlp.model, QuasiNewtonModel) ? hess_op(reg_nlp.model, xk) :
+    hess_op!(reg_nlp.model, xk, similar(xk))
   sub_nlp = R2NModel(Bk, ∇fk, zero(T), x0) #FIXME 
   subpb = RegularizedNLPModel(sub_nlp, ψ)
   substats = RegularizedExecutionStats(subpb)
@@ -341,7 +343,7 @@ function SolverCore.solve!(
       set_radius!(solver.subsolver.ψ, ∆_effective)
       set_radius!(ψ, ∆_effective)
     end
-    with_logger(subsolver_logger) do 
+    with_logger(subsolver_logger) do
       if isa(solver.subsolver, TRDHSolver) #FIXME
         solver.subsolver.D.d[1] = 1/ν₁
         solve!(

test/test_allocs.jl

@@ -42,21 +42,26 @@ end
 # Test non allocating solve!
 @testset "allocs" begin
   for (h, h_name) ∈ ((NormL0(λ), "l0"),)
-    for (solver, solver_name) ∈ ((:R2Solver, "R2"), (:R2DHSolver, "R2DH"), (:R2NSolver, "R2N"), (:TRDHSolver, "TRDH"), (:TRSolver, "TR"))
+    for (solver, solver_name) ∈ (
+      (:R2Solver, "R2"),
+      (:R2DHSolver, "R2DH"),
+      (:R2NSolver, "R2N"),
+      (:TRDHSolver, "TRDH"),
+      (:TRSolver, "TR"),
+    )
       @testset "$(solver_name)" begin
         (solver_name == "R2N" || solver_name == "TR") && continue #FIXME
         reg_nlp = RegularizedNLPModel(LBFGSModel(bpdn), h)
         solver = eval(solver)(reg_nlp)
         stats = RegularizedExecutionStats(reg_nlp)
-        solver_name == "R2" && @test @wrappedallocs(
-          solve!(solver, reg_nlp, stats, ν = 1.0, atol = 1e-6, rtol = 1e-6)
-        ) == 0
+        solver_name == "R2" &&
+          @test @wrappedallocs(solve!(solver, reg_nlp, stats, ν = 1.0, atol = 1e-6, rtol = 1e-6)) ==
+                0
         solver_name == "R2DH" && @test @wrappedallocs(
           solve!(solver, reg_nlp, stats, σk = 1.0, atol = 1e-6, rtol = 1e-6)
         ) == 0
-        solver_name == "TRDH" && @test @wrappedallocs(
-          solve!(solver, reg_nlp, stats, atol = 1e-6, rtol = 1e-6)
-        ) == 0
+        solver_name == "TRDH" &&
+          @test @wrappedallocs(solve!(solver, reg_nlp, stats, atol = 1e-6, rtol = 1e-6)) == 0
         @test stats.status == :first_order
       end
     end