Augmented Lagrangian method

Project.toml

@@ -9,6 +9,7 @@ LinearOperators = "5c8ed15e-5a4c-59e4-a42b-c7e8811fb125"
 Logging = "56ddb016-857b-54e1-b83d-db4d58db5568"
 NLPModels = "a4795742-8479-5a88-8948-cc11e1c8c1a6"
 NLPModelsModifiers = "e01155f1-5c6f-4375-a9d8-616dd036575f"
+Percival = "01435c0c-c90d-11e9-3788-63660f8fbccc"
 Printf = "de0858da-6303-5e67-8744-51eddeeeb8d7"
 ProximalOperators = "a725b495-10eb-56fe-b38b-717eba820537"
 RegularizedProblems = "ea076b23-609f-44d2-bb12-a4ae45328278"
@@ -20,6 +21,7 @@ TSVD = "9449cd9e-2762-5aa3-a617-5413e99d722e"
 LinearOperators = "2.7"
 NLPModels = "0.19, 0.20"
 NLPModelsModifiers = "0.7"
+Percival = "0.7.2"
 ProximalOperators = "0.15"
 RegularizedProblems = "0.1.1"
 ShiftedProximalOperators = "0.2"

examples/demo-cutest.jl

@@ -0,0 +1,46 @@
+using NLPModels, CUTEst
+using ProximalOperators
+using RegularizedOptimization
+
+problem_name = "HS8"
+nlp = CUTEstModel(problem_name)
+@assert !has_bounds(nlp)
+@assert equality_constrained(nlp)
+
+h = NormL1(1.0)
+
+stats = AL(nlp, h, atol = 1e-6, verbose = 1)
+print(stats)
+
+using RegularizedProblems
+
+regnlp = RegularizedNLPModel(nlp, h)
+stats = AL(regnlp, atol = 1e-6, verbose = 1)
+print(stats)
+
+solver = ALSolver(regnlp)
+stats = solve!(solver, regnlp, atol = 1e-6, verbose = 1)
+print(stats)
+
+using SolverCore
+
+stats = GenericExecutionStats(nlp)
+solver = ALSolver(regnlp)
+stats = solve!(solver, regnlp, stats, atol = 1e-6, verbose = 1)
+print(stats)
+
+callback =
+  (regnlp, solver, stats) -> begin
+    @info "iter $(stats.iter), obj $(stats.objective), status $(stats.status)"
+  end
+stats = AL(nlp, h, atol = 1e-6, verbose = 1, callback = callback)
+print(stats)
+
+callback =
+  (regnlp, solver, stats) -> begin
+    @info "iter $(stats.iter), f $(stats.solver_specific[:smooth_obj]), h $(stats.solver_specific[:nonsmooth_obj])"
+  end
+stats = AL(nlp, h, atol = 1e-6, verbose = 1, callback = callback)
+print(stats)
+
+finalize(nlp)