/// <summary>
/// Select coins in a way that user can pay without a change output (to increase privacy)
/// and try to find a solution that requires to pay as little extra amount as possible.
/// </summary>
/// <param name="strategy">The strategy determines what the algorithm is looking for.</param>
/// <param name="target">Target value we want to, ideally, sum up from the input values. </param>
/// <param name="inputEffectiveValues">Dictionary to map back the effective values to their original SmartCoin. </param>
/// <returns><c>true</c> if a solution was found, <c>false</c> otherwise.</returns>
internal static bool TryGetCoins(SelectionStrategy strategy, long target, Dictionary <SmartCoin, long> inputEffectiveValues, [NotNullWhen(true)] out IEnumerable <SmartCoin>?selectedCoins, CancellationToken cancellationToken = default)
{
selectedCoins = null;
BranchAndBound branchAndBound = new();
bool foundExactMatch = false;
List <long>?solution = null;
try
{
foundExactMatch = branchAndBound.TryGetMatch(strategy, out solution, cancellationToken);
}
catch (OperationCanceledException)
{
Logger.LogInfo("Computing privacy suggestions was cancelled or timed out.");
}
// If we've not found an optimal solution then we will use the best.
if (!foundExactMatch && strategy.GetBestSelectionFound() is long[] bestSolution)
{
solution = bestSolution.ToList();
}
if (solution is not null)
{
// Sanity check: do not return solution that is much higher or much lower than the target.
if (solution.Sum() > target * MaxExtraPayment || solution.Sum() < target * MinPaymentThreshold)
{
return(false);
}
List <SmartCoin> resultCoins = new();
int i = 0;
foreach ((SmartCoin smartCoin, long effectiveSatoshis) in inputEffectiveValues)
{
// Both arrays are in decreasing order so the first match will be the coin we are looking for.
if (effectiveSatoshis == solution[i])
{
i++;
resultCoins.Add(smartCoin);
if (i == solution.Count)
{
break;
}
}
}
selectedCoins = resultCoins;
return(true);
}
return(false);
}
