This directory contains data structures and algorithms for various problems.
An instance of set covering is composed of two entities: elements and sets; sets cover a series of elements. The problem of set covering is about finding the cheapest combination of sets that cover all the elements.
More information on Wikipedia.
- Solver:
set_cover_heuristics.h. - Instance representation:
set_cover_model.h. - Instance parser:
set_cover_reader.h.
Create an instance:
// If the elements are integers, a subset can be a std::vector<int> (in a pair
// along its cost).
std::vector<std::pair<std::vector<int>, int>> subsets = ...;
SetCoverModel model;
for (const auto [subset, subset_cost] : subsets) {
model.AddEmptySubset(subset_cost)
for (const int element : subset) {
model.AddElementToLastSubset(element);
}
}
SetCoverLedger ledger(&model);Solve it using a MIP solver (guarantees to yield the optimum solution if it has enough time to run):
SetCoverMip mip(&ledger);
mip.SetTimeLimitInSeconds(10);
mip.NextSolution();
SubsetBoolVector best_choices = ledger.GetSolution();
LOG(INFO) << "Cost: " << ledger.cost();A custom combination of heuristics (10,000 iterations of: clearing 10% of the variables, running a Chvatal greedy descent, using steepest local search):
Cost best_cost = std::numeric_limits<Cost>::max();
SubsetBoolVector best_choices = ledger.GetSolution();
for (int i = 0; i < 10000; ++i) {
ledger.LoadSolution(best_choices);
ClearRandomSubsets(0.1 * model.num_subsets().value(), &ledger);
GreedySolutionGenerator greedy(&ledger);
CHECK(greedy.NextSolution());
SteepestSearch steepest(&ledger);
CHECK(steepest.NextSolution(10000));
EXPECT_TRUE(ledger.CheckSolution());
if (ledger.cost() < best_cost) {
best_cost = ledger.cost();
best_choices = ledger.GetSolution();
LOG(INFO) << "Better cost: " << best_cost << " at iteration = " << i;
}
}
ledger.LoadSolution(best_choices);
LOG(INFO) << "Best cost: " << ledger.cost();