iR2N: R2N for inexact evaluations

Author: nathanemac

Project.toml

@@ -13,6 +13,7 @@ NLPModels = "a4795742-8479-5a88-8948-cc11e1c8c1a6"
 NLPModelsModifiers = "e01155f1-5c6f-4375-a9d8-616dd036575f"
 Percival = "01435c0c-c90d-11e9-3788-63660f8fbccc"
 Printf = "de0858da-6303-5e67-8744-51eddeeeb8d7"
+ProxTV = "925ea013-038b-5ab6-a1ab-e0849925e528"
 ProximalOperators = "a725b495-10eb-56fe-b38b-717eba820537"
 RegularizedProblems = "ea076b23-609f-44d2-bb12-a4ae45328278"
 ShiftedProximalOperators = "d4fd37fa-580c-4e43-9b30-361c21aae263"

src/RegularizedOptimization.jl

@@ -13,7 +13,8 @@ using LinearOperators,
   NLPModelsModifiers,
   RegularizedProblems,
   ShiftedProximalOperators,
-  SolverCore
+  SolverCore,
+  ProxTV
 using Percival: AugLagModel, update_y!, update_μ!
 
 const callback_docstring = "
@@ -44,7 +45,9 @@ include("LM_alg.jl")
 include("LMTR_alg.jl")
 include("R2DH.jl")
 include("R2NModel.jl")
+include("iR2_alg.jl")
 include("R2N.jl")
 include("AL_alg.jl")
+include("iR2N.jl")
 
 end  # module RegularizedOptimization