Change R2_alg so that it returns list of times

src/R2DH.jl

@@ -143,11 +143,11 @@ function R2DH(
   s = zero(xk)
   ψ = has_bnds ? shifted(h, xk, l_bound - xk, u_bound - xk, selected) : shifted(h, xk)
 
-  Fobj_hist = zeros(maxIter)
-  Hobj_hist = zeros(maxIter)
-  time_hist = zeros(maxIter)
+  Fobj_hist = zeros(maxIter+1)
+  Hobj_hist = zeros(maxIter+1)
+  time_hist = zeros(maxIter+1)
   FHobj_hist = fill!(Vector{R}(undef, Mmonotone), R(-Inf))
-  Complex_hist = zeros(Int, maxIter)
+  Complex_hist = zeros(Int, maxIter+1)
   if verbose > 0
     #! format: off
     @info @sprintf "%6s %8s %8s %7s %8s %7s %7s %7s %1s" "iter" "f(x)" "h(x)" "√(ξ/ν)" "ρ" "σ" "‖x‖" "‖s‖" ""
@@ -181,6 +181,8 @@ function R2DH(
     time_hist[k] = elapsed_time
     Mmonotone > 0 && (FHobj_hist[mod(k-1, Mmonotone) + 1] = fk + hk)
 
+    #Dkσk .= max.(Dkσk, eps(R)) 
+
     # model with diagonal hessian
     φ(d) = ∇fk' * d + (d' * (Dkσk .* d)) / 2
     mk(d) = φ(d) + ψ(d)
@@ -190,8 +192,9 @@ function R2DH(
     else
       iprox!(s, ψ, ∇fk, Dkσk)
     end
+    mks = mk(s)
 
-    if mk(s) > 1/eps(R)
+    if mks < -1e5
       σk = σk * γ
       Dkσk .= D.d .+ σk
       DNorm = norm(D.d, Inf)
@@ -208,8 +211,8 @@ function R2DH(
 
     fhmax = Mmonotone > 0 ? maximum(FHobj_hist) : fk + hk
     Δobj = fhmax - (fkn + hkn) + max(1, abs(fhmax)) * 10 * eps()
-    Δmod = fhmax - (fk + mk(s)) + max(1, abs(hk)) * 10 * eps()
-    ξ = hk - mk(s) + max(1, abs(hk)) * 10 * eps()
+    Δmod = fhmax - (fk + mks) + max(1, abs(hk)) * 10 * eps()
+    ξ = hk - mks + max(1, abs(hk)) * 10 * eps()
     sqrt_ξ_νInv = ξ ≥ 0 ? sqrt(ξ / ν) : sqrt(-ξ / ν)
 
     if ξ ≥ 0 && k == 1

src/R2_alg.jl

@@ -21,6 +21,7 @@ mutable struct R2Solver{
   Fobj_hist::Vector{R}
   Hobj_hist::Vector{R}
   Complex_hist::Vector{Int}
+  Time_hist::Vector{R}
 end
 
 function R2Solver(
@@ -46,6 +47,7 @@ function R2Solver(
   end
   Fobj_hist = zeros(R, maxIter + 2)
   Hobj_hist = zeros(R, maxIter + 2)
+  Time_hist = zeros(R, maxIter + 2)
   Complex_hist = zeros(Int, maxIter + 2)
   return R2Solver(
     xk,
@@ -62,6 +64,7 @@ function R2Solver(
     Fobj_hist,
     Hobj_hist,
     Complex_hist,
+    Time_hist,
   )
 end
 
@@ -87,6 +90,7 @@ function R2Solver(reg_nlp::AbstractRegularizedNLPModel{T, V}; max_iter::Int = 10
   end
   Fobj_hist = zeros(T, max_iter + 2)
   Hobj_hist = zeros(T, max_iter + 2)
+  Time_hist = zeros(T, max_iter + 2)
   Complex_hist = zeros(Int, max_iter + 2)
 
   ψ =
@@ -107,6 +111,7 @@ function R2Solver(reg_nlp::AbstractRegularizedNLPModel{T, V}; max_iter::Int = 10
     Fobj_hist,
     Hobj_hist,
     Complex_hist,
+    Time_hist,
   )
 end
 
@@ -201,6 +206,7 @@ function R2(
     ν = options.ν,
     γ = options.γ,
   )
+  return stats
 end
 
 function R2(
@@ -232,6 +238,7 @@ function R2(
   outdict = Dict(
     :Fhist => stats.solver_specific[:Fhist],
     :Hhist => stats.solver_specific[:Hhist],
+    :Time_hist => stats.solver_specific[:Time_hist],
     :Chist => stats.solver_specific[:SubsolverCounter],
     :NonSmooth => h,
     :status => stats.status,
@@ -274,6 +281,7 @@ function R2(
   outdict = Dict(
     :Fhist => stats.solver_specific[:Fhist],
     :Hhist => stats.solver_specific[:Hhist],
+    :Time_hist => stats.solver_specific[:Time_hist],
     :Chist => stats.solver_specific[:SubsolverCounter],
     :NonSmooth => h,
     :status => stats.status,
@@ -295,10 +303,12 @@ function R2(reg_nlp::AbstractRegularizedNLPModel; kwargs...)
       solver.Fobj_hist[stats.iter + 1] = stats.solver_specific[:smooth_obj]
       solver.Hobj_hist[stats.iter + 1] = stats.solver_specific[:nonsmooth_obj]
       solver.Complex_hist[stats.iter + 1] += 1
+      solver.Time_hist[stats.iter + 1] = stats.elapsed_time
     end
   solve!(solver, reg_nlp, stats; callback = cb, max_iter = max_iter, kwargs...)
   set_solver_specific!(stats, :Fhist, solver.Fobj_hist[1:(stats.iter + 1)])
   set_solver_specific!(stats, :Hhist, solver.Hobj_hist[1:(stats.iter + 1)])
+  set_solver_specific!(stats, :Time_hist, solver.Time_hist[1:(stats.iter + 1)])
   set_solver_specific!(stats, :SubsolverCounter, solver.Complex_hist[1:(stats.iter + 1)])
   return stats
 end