add RegularizedExecutionStats

JuliaSmoothOptimizers · Dec 31, 2024 · b5cbdc9 · b5cbdc9
1 parent 1c94830
commit b5cbdc9
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Showing 3 changed files with 5 additions and 6 deletions.
diff --git a/src/R2_alg.jl b/src/R2_alg.jl
@@ -132,7 +132,7 @@ For advanced usage, first define a solver "R2Solver" to preallocate the memory u
     solver = R2Solver(reg_nlp)
     solve!(solver, reg_nlp)
 
-    stats = GenericExecutionStats(reg_nlp)
+    stats = RegularizedExecutionStats(reg_nlp)
     solver = R2Solver(reg_nlp)
     solve!(solver, reg_nlp, stats)
 
@@ -292,7 +292,7 @@ function R2(reg_nlp::AbstractRegularizedNLPModel; kwargs...)
   kwargs_dict = Dict(kwargs...)
   max_iter = pop!(kwargs_dict, :max_iter, 10000)
   solver = R2Solver(reg_nlp, max_iter = max_iter)
-  stats = GenericExecutionStats(reg_nlp.model) # TODO: change this to `stats = GenericExecutionStats(reg_nlp)` when FHist etc. is ruled out.
+  stats = GenericExecutionStats(reg_nlp.model) # TODO: change this to `stats = RegularizedExecutionStats(reg_nlp)` when FHist etc. is ruled out.
   cb = pop!(
     kwargs_dict,
     :callback,

diff --git a/src/utils.jl b/src/utils.jl
@@ -1,4 +1,4 @@
-export GenericExecutionStats
+export RegularizedExecutionStats
 
 import SolverCore.GenericExecutionStats
 
@@ -32,9 +32,8 @@ Construct a GenericExecutionStats object from an AbstractRegularizedNLPModel.
 More specifically, construct a GenericExecutionStats on the NLPModel of reg_nlp and add three solver_specific entries namely :smooth_obj, :nonsmooth_obj and :xi.
 This is useful for reducing the number of allocations when calling solve!(..., reg_nlp, stats) and should be used by default.
 Warning: This should *not* be used when adding other solver_specific entries that do not have the current scalar type. 
-For instance, when one adds the history of the objective value as a solver_specific entry (which has Vector{T} type), this will cause an error and `GenericExecutionStats(reg_nlp.model)` should be used instead.
 """
-function GenericExecutionStats(reg_nlp :: AbstractRegularizedNLPModel{T, V}) where{T, V}
+function RegularizedExecutionStats(reg_nlp :: AbstractRegularizedNLPModel{T, V}) where{T, V}
   stats = GenericExecutionStats(reg_nlp.model, solver_specific = Dict{Symbol, T}())
   set_solver_specific!(stats, :smooth_obj, T(Inf))
   set_solver_specific!(stats, :nonsmooth_obj, T(Inf))

diff --git a/test/test_allocs.jl b/test/test_allocs.jl
@@ -41,7 +41,7 @@ end
     for solver ∈ (:R2Solver,)
       reg_nlp = RegularizedNLPModel(bpdn, h)
       solver = eval(solver)(reg_nlp)
-      stats = GenericExecutionStats(reg_nlp)
+      stats = RegularizedExecutionStats(reg_nlp)
       @test @wrappedallocs(solve!(solver, reg_nlp, stats)) == 0
     end
   end