300 lines
13 KiB
C++
300 lines
13 KiB
C++
// Copyright (c) 2017 The Bitcoin Core developers
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// Distributed under the MIT software license, see the accompanying
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// file COPYING or http://www.opensource.org/licenses/mit-license.php.
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#include <wallet/coinselection.h>
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#include <util.h>
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#include <utilmoneystr.h>
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// Descending order comparator
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struct {
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bool operator()(const CInputCoin& a, const CInputCoin& b) const
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{
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return a.effective_value > b.effective_value;
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}
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} descending;
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/*
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* This is the Branch and Bound Coin Selection algorithm designed by Murch. It searches for an input
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* set that can pay for the spending target and does not exceed the spending target by more than the
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* cost of creating and spending a change output. The algorithm uses a depth-first search on a binary
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* tree. In the binary tree, each node corresponds to the inclusion or the omission of a UTXO. UTXOs
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* are sorted by their effective values and the trees is explored deterministically per the inclusion
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* branch first. At each node, the algorithm checks whether the selection is within the target range.
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* While the selection has not reached the target range, more UTXOs are included. When a selection's
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* value exceeds the target range, the complete subtree deriving from this selection can be omitted.
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* At that point, the last included UTXO is deselected and the corresponding omission branch explored
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* instead. The search ends after the complete tree has been searched or after a limited number of tries.
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*
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* The search continues to search for better solutions after one solution has been found. The best
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* solution is chosen by minimizing the waste metric. The waste metric is defined as the cost to
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* spend the current inputs at the given fee rate minus the long term expected cost to spend the
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* inputs, plus the amount the selection exceeds the spending target:
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*
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* waste = selectionTotal - target + inputs × (currentFeeRate - longTermFeeRate)
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*
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* The algorithm uses two additional optimizations. A lookahead keeps track of the total value of
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* the unexplored UTXOs. A subtree is not explored if the lookahead indicates that the target range
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* cannot be reached. Further, it is unnecessary to test equivalent combinations. This allows us
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* to skip testing the inclusion of UTXOs that match the effective value and waste of an omitted
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* predecessor.
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*
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* The Branch and Bound algorithm is described in detail in Murch's Master Thesis:
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* https://murch.one/wp-content/uploads/2016/11/erhardt2016coinselection.pdf
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*
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* @param const std::vector<CInputCoin>& utxo_pool The set of UTXOs that we are choosing from.
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* These UTXOs will be sorted in descending order by effective value and the CInputCoins'
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* values are their effective values.
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* @param const CAmount& target_value This is the value that we want to select. It is the lower
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* bound of the range.
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* @param const CAmount& cost_of_change This is the cost of creating and spending a change output.
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* This plus target_value is the upper bound of the range.
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* @param std::set<CInputCoin>& out_set -> This is an output parameter for the set of CInputCoins
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* that have been selected.
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* @param CAmount& value_ret -> This is an output parameter for the total value of the CInputCoins
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* that were selected.
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* @param CAmount not_input_fees -> The fees that need to be paid for the outputs and fixed size
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* overhead (version, locktime, marker and flag)
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*/
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static const size_t TOTAL_TRIES = 100000;
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bool SelectCoinsBnB(std::vector<CInputCoin>& utxo_pool, const CAmount& target_value, const CAmount& cost_of_change, std::set<CInputCoin>& out_set, CAmount& value_ret, CAmount not_input_fees)
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{
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out_set.clear();
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CAmount curr_value = 0;
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std::vector<bool> curr_selection; // select the utxo at this index
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curr_selection.reserve(utxo_pool.size());
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CAmount actual_target = not_input_fees + target_value;
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// Calculate curr_available_value
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CAmount curr_available_value = 0;
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for (const CInputCoin& utxo : utxo_pool) {
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// Assert that this utxo is not negative. It should never be negative, effective value calculation should have removed it
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assert(utxo.effective_value > 0);
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curr_available_value += utxo.effective_value;
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}
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if (curr_available_value < actual_target) {
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return false;
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}
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// Sort the utxo_pool
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std::sort(utxo_pool.begin(), utxo_pool.end(), descending);
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CAmount curr_waste = 0;
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std::vector<bool> best_selection;
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CAmount best_waste = MAX_MONEY;
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// Depth First search loop for choosing the UTXOs
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for (size_t i = 0; i < TOTAL_TRIES; ++i) {
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// Conditions for starting a backtrack
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bool backtrack = false;
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if (curr_value + curr_available_value < actual_target || // Cannot possibly reach target with the amount remaining in the curr_available_value.
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curr_value > actual_target + cost_of_change || // Selected value is out of range, go back and try other branch
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(curr_waste > best_waste && (utxo_pool.at(0).fee - utxo_pool.at(0).long_term_fee) > 0)) { // Don't select things which we know will be more wasteful if the waste is increasing
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backtrack = true;
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} else if (curr_value >= actual_target) { // Selected value is within range
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curr_waste += (curr_value - actual_target); // This is the excess value which is added to the waste for the below comparison
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// Adding another UTXO after this check could bring the waste down if the long term fee is higher than the current fee.
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// However we are not going to explore that because this optimization for the waste is only done when we have hit our target
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// value. Adding any more UTXOs will be just burning the UTXO; it will go entirely to fees. Thus we aren't going to
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// explore any more UTXOs to avoid burning money like that.
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if (curr_waste <= best_waste) {
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best_selection = curr_selection;
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best_selection.resize(utxo_pool.size());
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best_waste = curr_waste;
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}
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curr_waste -= (curr_value - actual_target); // Remove the excess value as we will be selecting different coins now
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backtrack = true;
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}
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// Backtracking, moving backwards
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if (backtrack) {
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// Walk backwards to find the last included UTXO that still needs to have its omission branch traversed.
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while (!curr_selection.empty() && !curr_selection.back()) {
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curr_selection.pop_back();
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curr_available_value += utxo_pool.at(curr_selection.size()).effective_value;
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};
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if (curr_selection.empty()) { // We have walked back to the first utxo and no branch is untraversed. All solutions searched
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break;
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}
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// Output was included on previous iterations, try excluding now.
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curr_selection.back() = false;
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CInputCoin& utxo = utxo_pool.at(curr_selection.size() - 1);
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curr_value -= utxo.effective_value;
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curr_waste -= utxo.fee - utxo.long_term_fee;
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} else { // Moving forwards, continuing down this branch
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CInputCoin& utxo = utxo_pool.at(curr_selection.size());
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// Remove this utxo from the curr_available_value utxo amount
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curr_available_value -= utxo.effective_value;
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// Avoid searching a branch if the previous UTXO has the same value and same waste and was excluded. Since the ratio of fee to
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// long term fee is the same, we only need to check if one of those values match in order to know that the waste is the same.
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if (!curr_selection.empty() && !curr_selection.back() &&
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utxo.effective_value == utxo_pool.at(curr_selection.size() - 1).effective_value &&
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utxo.fee == utxo_pool.at(curr_selection.size() - 1).fee) {
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curr_selection.push_back(false);
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} else {
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// Inclusion branch first (Largest First Exploration)
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curr_selection.push_back(true);
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curr_value += utxo.effective_value;
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curr_waste += utxo.fee - utxo.long_term_fee;
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}
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}
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}
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// Check for solution
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if (best_selection.empty()) {
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return false;
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}
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// Set output set
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value_ret = 0;
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for (size_t i = 0; i < best_selection.size(); ++i) {
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if (best_selection.at(i)) {
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out_set.insert(utxo_pool.at(i));
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value_ret += utxo_pool.at(i).txout.nValue;
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}
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}
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return true;
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}
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static void ApproximateBestSubset(const std::vector<CInputCoin>& vValue, const CAmount& nTotalLower, const CAmount& nTargetValue,
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std::vector<char>& vfBest, CAmount& nBest, int iterations = 1000)
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{
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std::vector<char> vfIncluded;
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vfBest.assign(vValue.size(), true);
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nBest = nTotalLower;
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FastRandomContext insecure_rand;
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for (int nRep = 0; nRep < iterations && nBest != nTargetValue; nRep++)
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{
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vfIncluded.assign(vValue.size(), false);
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CAmount nTotal = 0;
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bool fReachedTarget = false;
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for (int nPass = 0; nPass < 2 && !fReachedTarget; nPass++)
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{
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for (unsigned int i = 0; i < vValue.size(); i++)
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{
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//The solver here uses a randomized algorithm,
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//the randomness serves no real security purpose but is just
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//needed to prevent degenerate behavior and it is important
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//that the rng is fast. We do not use a constant random sequence,
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//because there may be some privacy improvement by making
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//the selection random.
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if (nPass == 0 ? insecure_rand.randbool() : !vfIncluded[i])
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{
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nTotal += vValue[i].txout.nValue;
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vfIncluded[i] = true;
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if (nTotal >= nTargetValue)
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{
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fReachedTarget = true;
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if (nTotal < nBest)
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{
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nBest = nTotal;
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vfBest = vfIncluded;
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}
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nTotal -= vValue[i].txout.nValue;
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vfIncluded[i] = false;
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}
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}
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}
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}
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}
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}
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bool KnapsackSolver(const CAmount& nTargetValue, std::vector<CInputCoin>& vCoins, std::set<CInputCoin>& setCoinsRet, CAmount& nValueRet)
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{
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setCoinsRet.clear();
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nValueRet = 0;
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// List of values less than target
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boost::optional<CInputCoin> coinLowestLarger;
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std::vector<CInputCoin> vValue;
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CAmount nTotalLower = 0;
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random_shuffle(vCoins.begin(), vCoins.end(), GetRandInt);
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for (const CInputCoin &coin : vCoins)
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{
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if (coin.txout.nValue == nTargetValue)
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{
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setCoinsRet.insert(coin);
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nValueRet += coin.txout.nValue;
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return true;
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}
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else if (coin.txout.nValue < nTargetValue + MIN_CHANGE)
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{
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vValue.push_back(coin);
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nTotalLower += coin.txout.nValue;
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}
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else if (!coinLowestLarger || coin.txout.nValue < coinLowestLarger->txout.nValue)
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{
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coinLowestLarger = coin;
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}
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}
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if (nTotalLower == nTargetValue)
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{
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for (const auto& input : vValue)
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{
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setCoinsRet.insert(input);
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nValueRet += input.txout.nValue;
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}
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return true;
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}
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if (nTotalLower < nTargetValue)
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{
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if (!coinLowestLarger)
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return false;
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setCoinsRet.insert(coinLowestLarger.get());
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nValueRet += coinLowestLarger->txout.nValue;
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return true;
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}
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// Solve subset sum by stochastic approximation
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std::sort(vValue.begin(), vValue.end(), descending);
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std::vector<char> vfBest;
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CAmount nBest;
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ApproximateBestSubset(vValue, nTotalLower, nTargetValue, vfBest, nBest);
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if (nBest != nTargetValue && nTotalLower >= nTargetValue + MIN_CHANGE)
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ApproximateBestSubset(vValue, nTotalLower, nTargetValue + MIN_CHANGE, vfBest, nBest);
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// If we have a bigger coin and (either the stochastic approximation didn't find a good solution,
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// or the next bigger coin is closer), return the bigger coin
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if (coinLowestLarger &&
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((nBest != nTargetValue && nBest < nTargetValue + MIN_CHANGE) || coinLowestLarger->txout.nValue <= nBest))
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{
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setCoinsRet.insert(coinLowestLarger.get());
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nValueRet += coinLowestLarger->txout.nValue;
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}
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else {
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for (unsigned int i = 0; i < vValue.size(); i++)
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if (vfBest[i])
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{
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setCoinsRet.insert(vValue[i]);
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nValueRet += vValue[i].txout.nValue;
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}
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if (LogAcceptCategory(BCLog::SELECTCOINS)) {
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LogPrint(BCLog::SELECTCOINS, "SelectCoins() best subset: "); /* Continued */
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for (unsigned int i = 0; i < vValue.size(); i++) {
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if (vfBest[i]) {
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LogPrint(BCLog::SELECTCOINS, "%s ", FormatMoney(vValue[i].txout.nValue)); /* Continued */
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}
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}
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LogPrint(BCLog::SELECTCOINS, "total %s\n", FormatMoney(nBest));
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}
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}
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return true;
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}
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