Refactor imports and improve setup functions in RegularizedProblems module

Author: MohamedLaghdafHABIBOULLAH

src/RegularizedProblems.jl

@@ -1,9 +1,30 @@
 module RegularizedProblems
 
-using LinearAlgebra, SparseArrays
-using Random
-using ManualNLPModels, NLPModels, ShiftedProximalOperators
-using Distributions, Noise, ProximalOperators
+import Base: /, convert
+
+import Distributions
+using Distributions: MixtureModel, MvNormal, Uniform
+
+import LinearAlgebra
+using LinearAlgebra: I, dot, mul!, norm, qr
+
+import NLPModels
+using NLPModels:
+  AbstractNLPModel, AbstractNLSModel, Counters, NLSCounters, get_nvar, grad!, obj
+
+import ManualNLPModels
+using ManualNLPModels: NLPModel, NLSModel
+
+import Noise
+using Noise: add_gauss
+
+import ProximalOperators
+import ShiftedProximalOperators
+
+import Random
+using Random: randperm
+
+import SparseArrays
 
 include("utils.jl")
 include("types.jl")

src/testset_bpdn.jl

@@ -3,14 +3,18 @@ export setup_bpdn_l0, setup_bpdn_l1, setup_bpdn_B0
 
 function setup_bpdn_l0(args...; kwargs...)
   model, nls_model, _ = bpdn_model(args...; kwargs...)
-  λ = norm(grad(model, zeros(model.meta.nvar)), Inf) / 10
+  y = similar(model.meta.x0)
+  grad!(model, y, zeros(model.meta.nvar))
+  λ = norm(y) / 10
   h = ProximalOperators.NormL0(λ)
   return RegularizedNLPModel(model, h), RegularizedNLSModel(nls_model, h)
 end
 
 function setup_bpdn_l1(args...; kwargs...)
   model, nls_model, _ = bpdn_model(args...; kwargs...)
-  λ = norm(grad(model, zeros(model.meta.nvar)), Inf) / 10
+  y = similar(model.meta.x0)
+  grad!(model, y, zeros(model.meta.nvar))
+  λ = norm(y) / 10
   h = ProximalOperators.NormL1(λ)
   return RegularizedNLPModel(model, h), RegularizedNLSModel(nls_model, h)
 end