some clean up for eigen cuts

CHANGELOG.md

@@ -4,7 +4,8 @@ LaplacianOpt.jl Change Log
 ### v0.6.0
 - Added mutiple heuristics to handle both spanning trees and graphs with loops 
 - Refactored `log.jl` to handle solutions from heuristics
-- Included more used options for heuristic in `model_options`
+- Included more user options for heuristic in `model_options`
+- Cleaned up populating and logging of eigen cuts
 - Updated docs and unit tests to reflect above changes
 
 ### v0.5.0

src/constraints.jl

@@ -133,9 +133,8 @@ function constraint_lazycallback_wrapper(lom::LaplacianOptModel; optimizer = not
 end
 
 function constraint_eigen_cuts(W_val::Matrix{<:Number}, cb_cuts, lom::LaplacianOptModel)
-    if (size(lom.options.eigen_cuts_sizes)[1] > 0) &&
-       (minimum(lom.options.eigen_cuts_sizes) >= 2)
-        for cut_size in sort(unique(lom.options.eigen_cuts_sizes), rev = true)
+    if (length(keys(lom.minor_idx_dict)) > 0)
+        for cut_size in sort(collect(keys(lom.minor_idx_dict)), rev = true)
             for i in lom.minor_idx_dict[cut_size]
                 LOpt._add_eigen_cut_lazy(W_val, cb_cuts, lom, collect(i))
             end

src/lopt_model.jl

@@ -11,16 +11,16 @@ function build_LOModel(data::Dict{String,Any}; optimizer = nothing, options = no
         end
     end
 
-    LOpt._logging_info(lom)
-
     if lom.options.formulation_type == "max_λ2"
         if lom.options.solution_type == "heuristic"
+            LOpt._logging_info(lom)
             return lom
 
         elseif lom.options.solution_type == "optimal"
             # Populate PMinor indices
             lom.minor_idx_dict =
                 LOpt._PMinorIdx(lom.data["num_nodes"], lom.options.eigen_cuts_sizes)
+            LOpt._logging_info(lom)
 
             LOpt.variable_LOModel(lom)
             LOpt.constraint_LOModel(lom; optimizer = optimizer)
@@ -192,12 +192,8 @@ end
 
 function _logging_info(lom::LaplacianOptModel)
     if lom.options.solution_type == "optimal"
-        if (
-            size(lom.options.eigen_cuts_sizes)[1] > 0 &&
-            minimum(lom.options.eigen_cuts_sizes) >= 2 &&
-            maximum(lom.options.eigen_cuts_sizes) <= lom.data["num_nodes"]
-        )
-            for k in lom.options.eigen_cuts_sizes
+        if length(keys(lom.minor_idx_dict)) > 0
+            for k in keys(lom.minor_idx_dict)
                 Memento.info(_LOGGER, "Applying eigen cuts ($(k)x$(k) matrix)")
             end
         end
@@ -217,6 +213,6 @@ function _logging_info(lom::LaplacianOptModel)
             Memento.info(_LOGGER, "Applying topology flow cuts")
         end
     elseif lom.options.solution_type == "heuristic"
-        Memento.info(_LOGGER, "Applying heuristics to obtain a lower bound")
+        Memento.info(_LOGGER, "Applying heuristics to obtain a feasible solution")
     end
 end

src/solution.jl

@@ -46,7 +46,7 @@ function build_LOModel_result(lom::LaplacianOptModel, solve_time::Number)
         if status in [true, false]
             result["optimality_certificate_MISDP"] = status
             (status == false) &&
-                (Memento.warn(_LOGGER, "Optimality certificate for MISDP failed!!!"))
+                (Memento.warn(_LOGGER, "Optimality certificate for MISDP failed!"))
         end
     end
 

src/utility.jl

@@ -325,8 +325,19 @@ end
 
 function _PMinorIdx(N::Int64, sizes::Vector{Int64})
     minor_idx_dict = Dict{Int64,Vector{Tuple{Int64,Vararg{Int64}}}}()
-    for k in sizes
-        minor_idx_dict[k] = LOpt.get_minor_idx(N, k)
+
+    if length(sizes) > 0
+        if maximum(sizes) > N
+            Memento.warn(
+                _LOGGER,
+                "Detected maximum eigen-cut size ($(maximum(sizes))) > num_nodes",
+            )
+        end
+        for k in unique(sizes)
+            if (k >= 2) && (k <= N)
+                minor_idx_dict[k] = LOpt.get_minor_idx(N, k)
+            end
+        end
     end
 
     return minor_idx_dict