Merge branch 'JuliaSmoothOptimizers:master' into switch_stopping_criterion

Author: MaxenceGollier

Project.toml

@@ -6,7 +6,7 @@ author = ["Robert Baraldi <rbaraldi@uw.edu>",
           "Mohamed Laghdaf Habiboullah <mohamed-laghdaf-2.habiboullah@polymtl.ca>",
           "Geoffroy Leconte <geoffroy.leconte@polymtl.ca>",
           "Dominique Orban <dominique.orban@gmail.com>"]
-version = "0.1.0"
+version = "0.2.0"
 
 [deps]
 Arpack = "7d9fca2a-8960-54d3-9f78-7d1dccf2cb97"
@@ -33,7 +33,7 @@ NLPModelsModifiers = "0.7"
 OptimizationProblems = "0.9.2"
 Percival = "0.7.2"
 ProximalOperators = "0.15"
-RegularizedProblems = "0.1.1"
+RegularizedProblems = "0.1.3"
 ShiftedProximalOperators = "0.2"
 SolverCore = "0.3.0"
 julia = "^1.6.0"

src/LMTR_alg.jl

@@ -315,7 +315,7 @@ function SolverCore.solve!(
     ),
   )
 
-  callback(nls, solver, stats)
+  callback(reg_nls, solver, stats)
 
   done = stats.status != :unknown
 
@@ -461,7 +461,7 @@ function SolverCore.solve!(
       ),
     )
 
-    callback(nls, solver, stats)
+    callback(reg_nls, solver, stats)
 
     done = stats.status != :unknown
   end

src/LM_alg.jl

@@ -318,7 +318,7 @@ function SolverCore.solve!(
     ),
   )
 
-  callback(nls, solver, stats)
+  callback(reg_nls, solver, stats)
 
   done = stats.status != :unknown
 
@@ -449,7 +449,7 @@ function SolverCore.solve!(
       ),
     )
 
-    callback(nls, solver, stats)
+    callback(reg_nls, solver, stats)
 
     done = stats.status != :unknown
   end

src/R2DH.jl

@@ -356,7 +356,7 @@ function SolverCore.solve!(
     ),
   )
 
-  callback(nlp, solver, stats)
+  callback(reg_nlp, solver, stats)
 
   done = stats.status != :unknown
 
@@ -451,7 +451,7 @@ function SolverCore.solve!(
       ),
     )
 
-    callback(nlp, solver, stats)
+    callback(reg_nlp, solver, stats)
 
     done = stats.status != :unknown
   end

src/R2N.jl

@@ -67,7 +67,7 @@ function R2NSolver(
     has_bnds ? shifted(reg_nlp.h, xk, l_bound_m_x, u_bound_m_x, reg_nlp.selected) :
     shifted(reg_nlp.h, xk)
 
-  Bk = hess_op(reg_nlp.model, xk)
+  Bk = hess_op(reg_nlp, xk)
   sub_nlp = R2NModel(Bk, ∇fk, T(1), x0)
   subpb = RegularizedNLPModel(sub_nlp, ψ)
   substats = RegularizedExecutionStats(subpb)
@@ -344,7 +344,7 @@ function SolverCore.solve!(
     ),
   )
 
-  callback(nlp, solver, stats)
+  callback(reg_nlp, solver, stats)
 
   done = stats.status != :unknown
 
@@ -485,7 +485,7 @@ function SolverCore.solve!(
       ),
     )
 
-    callback(nlp, solver, stats)
+    callback(reg_nlp, solver, stats)
 
     done = stats.status != :unknown
   end

src/R2_alg.jl

@@ -430,7 +430,7 @@ function SolverCore.solve!(
     ),
   )
 
-  callback(nlp, solver, stats)
+  callback(reg_nlp, solver, stats)
 
   done = stats.status != :unknown
 
@@ -513,7 +513,7 @@ function SolverCore.solve!(
       ),
     )
 
-    callback(nlp, solver, stats)
+    callback(reg_nlp, solver, stats)
 
     done = stats.status != :unknown
   end

src/RegularizedOptimization.jl

@@ -20,10 +20,10 @@ import SolverCore.reset!
 
 const callback_docstring = "
 The callback is called at each iteration.
-The expected signature of the callback is `callback(nlp, solver, stats)`, and its output is ignored.
+The expected signature of the callback is `callback(reg_nlp, solver, stats)`, and its output is ignored.
 Changing any of the input arguments will affect the subsequent iterations.
 In particular, setting `stats.status = :user` will stop the algorithm.
-All relevant information should be available in `nlp` and `solver`.
+All relevant information should be available in `reg_nlp` and `solver`.
 Notably, you can access, and modify, the following:
 - `solver.xk`: current iterate;
 - `solver.∇fk`: current gradient;

src/TRDH_alg.jl

@@ -408,7 +408,7 @@ function SolverCore.solve!(
     ),
   )
 
-  callback(nlp, solver, stats)
+  callback(reg_nlp, solver, stats)
 
   done = stats.status != :unknown
 
@@ -514,7 +514,7 @@ function SolverCore.solve!(
       ),
     )
 
-    callback(nlp, solver, stats)
+    callback(reg_nlp, solver, stats)
 
     done = stats.status != :unknown
   end

src/TR_alg.jl

@@ -67,7 +67,7 @@ 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 = hess_op(reg_nlp.model, xk)
+  Bk = hess_op(reg_nlp, xk)
   sub_nlp = R2NModel(Bk, ∇fk, zero(T), x0) #FIXME 
   subpb = RegularizedNLPModel(sub_nlp, ψ)
   substats = RegularizedExecutionStats(subpb)
@@ -345,7 +345,7 @@ function SolverCore.solve!(
     ),
   )
 
-  callback(nlp, solver, stats)
+  callback(reg_nlp, solver, stats)
 
   done = stats.status != :unknown
 
@@ -503,7 +503,7 @@ function SolverCore.solve!(
       ),
     )
 
-    callback(nlp, solver, stats)
+    callback(reg_nlp, solver, stats)
 
     done = stats.status != :unknown
   end