v0.2.5 updates

CHANGELOG.md

@@ -1,5 +1,10 @@
 # Critical Updates for Alpine.jl
 
+## v0.2.5
+* Cleaning up of solver logs
+* Time correction for local solve 
+* Removed unused `Alpine.Optimizer` options in solver.jl
+
 ## v0.2.4
 * Added unit tests for utility functions
 * Removed un-used functions from utility functions which aren't part of Alpine's algorithm     

Project.toml

@@ -2,7 +2,7 @@ name = "Alpine"
 uuid = "07493b3f-dabb-5b16-a503-4139292d7dd4"
 authors = ["Harsha Nagarajan, Site Wang, Kaarthik Sundar and contributors"]
 repo = "https://github.com/lanl-ansi/Alpine.jl.git"
-version = "0.2.4"
+version = "0.2.5"
 
 [deps]
 Distributed = "8ba89e20-285c-5b6f-9357-94700520ee1b"

src/algorithm.jl

@@ -91,30 +91,43 @@ function presolve(m::Optimizer)
    # Solver status - returns error when see different
 
    if m.status[:local_solve] in STATUS_OPT || m.status[:local_solve] in STATUS_LIMIT
-      get_option(m, :log_level) > 0 && println("  Local solver returns a feasible point")
+
+      get_option(m, :log_level) > 0 && println("  Local solver returns a feasible point with value $(round(m.best_obj, digits=4))")
+
       bound_tightening(m, use_bound = true)    # performs bound-tightening with the local solve objective value
+
       get_option(m, :presolve_bt) && init_disc(m)            # Re-initialize discretization dictionary on tight bounds
+
       get_option(m, :disc_ratio_branch) && (set_option(m, :disc_ratio, update_disc_ratio(m, true)))
+
       add_partition(m, use_solution=m.best_sol)  # Setting up the initial discretization
-      # get_option(m, :log_level) > 0 && println("Ending the presolve")
+
    elseif m.status[:local_solve] in STATUS_INF
+
       (get_option(m, :log_level) > 0) && println("  Bound tightening without objective bounds (OBBT)")
+
       bound_tightening(m, use_bound = false)                      # do bound tightening without objective value
+
       (get_option(m, :disc_ratio_branch)) && (set_option(m, :disc_ratio, update_disc_ratio(m, true)))
+
       get_option(m, :presolve_bt) && init_disc(m)
-      # get_option(m, :log_level) > 0 && println("Ending the presolve")
+
    elseif m.status[:local_solve] == MOI.INVALID_MODEL
+
       @warn " Warning: Presolve ends with local solver yielding $(m.status[:local_solve]). \n This may come from Ipopt's `:Not_Enough_Degrees_Of_Freedom`. \n Consider more replace equality constraints with >= and <= to resolve this."
+
    else
+
       @warn " Warning: Presolve ends with local solver yielding $(m.status[:local_solve])."
+
    end
 
    cputime_presolve = time() - start_presolve
    m.logs[:presolve_time] += cputime_presolve
    m.logs[:total_time] = m.logs[:presolve_time]
    m.logs[:time_left] -= m.logs[:presolve_time]
    # (get_option(m, :log_level) > 0) && println("Presolve time = $(round.(m.logs[:total_time]; digits=2))s")
-   (get_option(m, :log_level) > 0) && println("  Completed presolve in $(round.(m.logs[:total_time]; digits=2))s ($(m.logs[:bt_iter]) iterations)")
+   (get_option(m, :log_level) > 0) && println("  Completed presolve in $(round.(m.logs[:total_time]; digits=2))s")
    return
 end
 
@@ -161,7 +174,7 @@ function check_exit(m::Optimizer)
    m.logs[:n_iter] >= get_option(m, :max_iter) && return true
    m.best_abs_gap <= get_option(m, :abs_gap) && return true
 
-   # Userlimits check
+   # User-limits check
    m.logs[:time_left] < get_option(m, :tol) && return true
 
    return false
@@ -234,7 +247,6 @@ function local_solve(m::Optimizer; presolve = false)
       l_var, u_var = m.l_var_tight[1:m.num_var_orig], m.u_var_tight[1:m.num_var_orig]
    end
 
-   start_local_solve = time()
    x = load_nonlinear_model(m, local_solve_model, l_var, u_var)
    (!m.d_orig.want_hess) && MOI.initialize(m.d_orig, [:Grad, :Jac, :Hess, :HessVec, :ExprGraph]) # Safety scheme for sub-solvers re-initializing the NLPEvaluator
 
@@ -257,6 +269,7 @@ function local_solve(m::Optimizer; presolve = false)
       end
    end
 
+   start_local_solve = time()
    MOI.optimize!(local_solve_model)
    local_nlp_status = MOI.get(local_solve_model, MOI.TerminationStatus())
 

src/log.jl

@@ -34,15 +34,15 @@ function logging_summary(m::Optimizer)
       printstyled("\nPROBLEM STATISTICS\n", color=:cyan)
       is_min_sense(m) && (println("  Objective sense = Min", ))
       is_max_sense(m) && (println("  Objective sense = Max", ))
-      println("  #Variables = ", length([i for i in 1:m.num_var_orig if m.var_type[i] == :Cont]) + length([i for i in 1:m.num_var_orig if m.var_type[i] == :Bin]) + length([i for i in 1:m.num_var_orig if m.var_type[i] == :Int]))
-      println("  #Bin-Int Variables = ", length([i for i in 1:m.num_var_orig if m.var_type[i] == :Bin]) + length([i for i in 1:m.num_var_orig if m.var_type[i] == :Int]))
-      println("  #Constraints = ", m.num_constr_orig)
-      println("  #NL Constraints = ", m.num_nlconstr_orig)
-      println("  #Linear Constraints = ", m.num_lconstr_orig)
-      get_option(m, :recognize_convex) && println("  #Detected convex constraints = $(length([i for i in m.constr_structure if i == :convex]))")
-      println("  #Detected nonlinear terms = ", length(m.nonconvex_terms))
-      println("  #Variables involved in nonlinear terms = ", length(m.candidate_disc_vars))
-      println("  #Potential variables for partitioning = ", length(m.disc_vars))
+      println("  # Variables = ", length([i for i in 1:m.num_var_orig if m.var_type[i] == :Cont]) + length([i for i in 1:m.num_var_orig if m.var_type[i] == :Bin]) + length([i for i in 1:m.num_var_orig if m.var_type[i] == :Int]))
+      println("  # Bin-Int Variables = ", length([i for i in 1:m.num_var_orig if m.var_type[i] == :Bin]) + length([i for i in 1:m.num_var_orig if m.var_type[i] == :Int]))
+      println("  # Constraints = ", m.num_constr_orig)
+      println("  # NL Constraints = ", m.num_nlconstr_orig)
+      println("  # Linear Constraints = ", m.num_lconstr_orig)
+      get_option(m, :recognize_convex) && println("  # Detected convex constraints = $(length([i for i in m.constr_structure if i == :convex]))")
+      println("  # Detected nonlinear terms = ", length(m.nonconvex_terms))
+      println("  # Variables involved in nonlinear terms = ", length(m.candidate_disc_vars))
+      println("  # Potential variables for partitioning = ", length(m.disc_vars))
 
       printstyled("SUB-SOLVERS USED BY ALPINE\n", color=:cyan)
       if get_option(m, :minlp_solver) === nothing
@@ -53,8 +53,15 @@ function logging_summary(m::Optimizer)
       println("  MIP solver = ", m.mip_solver_id)
 
       printstyled("ALPINE CONFIGURATION\n", color=:cyan)
-      println("  Maximum iterations =  ", get_option(m, :max_iter))
-      println("  Relative optimality gap criteria = ", get_option(m, :rel_gap)*100, "%")
+      if is_min_sense(m)
+         println("  Maximum iterations (lower-bounding MIPs) =  ", get_option(m, :max_iter))
+      elseif is_max_sense(m)
+         println("  Maximum iterations (upper-bounding MIPs) =  ", get_option(m, :max_iter))
+      else 
+         println("  Maximum iterations (bounding MIPs) =  ", get_option(m, :max_iter))
+      end
+
+      println("  Relative global optimality gap = ", get_option(m, :rel_gap)*100, "%")
 
       if get_option(m, :disc_var_pick) == 0
          println("  Potential variables chosen for partitioning = All")
@@ -72,7 +79,7 @@ function logging_summary(m::Optimizer)
        (get_option(m, :convhull_ebd)) && println("  Encoding method = $(get_option(m, :convhull_ebd_encode))")
        (get_option(m, :convhull_ebd)) && println("  Independent branching scheme = $(get_option(m, :convhull_ebd_ibs))")
       println("  Bound-tightening (OBBT) presolve = ", get_option(m, :presolve_bt))
-      get_option(m, :presolve_bt) && println("  OBBT maximum iterations = ", get_option(m, :presolve_bt_max_iter))
+      get_option(m, :presolve_bt) && println("  Maximum iterations (OBBT) = ", get_option(m, :presolve_bt_max_iter))
    end
 
 end

src/presolve.jl

@@ -63,7 +63,7 @@ function minmax_bound_tightening(m::Optimizer; use_bound = true, timelimit = Inf
         timelimit = get_option(m, :presolve_time_limit)
     end
 
-    # Regulating Special Input Conditions: default use best feasible solution objective value
+    # Regulating special input conditions: default use best feasible solution objective value
     (use_bound == true) ? bound = m.best_obj : bound = Inf
     l_var_orig = copy(m.l_var_tight)
     u_var_orig = copy(m.u_var_tight)