The acceptance that any attempt to improve on the existing selection will be based on conjecture underscores the value in anchoring such efforts to practical use cases. As research progresses, the aim is to establish a more precise definition of optimality for these subsets, alluding to potential future challenges if initial solutions become too entrenched before a rigorous method is developed.

The proposed algorithm suggests enhancements to the mempool with O(nlogn) processing efficiency, omitting the need for resubmitting transactions, although it doesn't address incremental relay pin issues. A scenario is described where three transactions (A, B, and C) are already in the mempool and a new transaction D arrives, pushing the cluster size over the limit. According to the algorithm, transactions would be evicted in a certain order to make room for the new high-fee transaction D, after which space might also allow for the re-addition of one of the evicted transactions, namely C. This process implies that while the algorithm does not solve all problems associated with transaction relays, it does provide a mechanism for managing the mempool when a high-fee transaction is encountered.

In this context, there's a visualization provided using a mermaid flowchart syntax to illustrate how the transactions relate and the resulting actions of the algorithm when faced with cluster size limits. The example conveys a dynamic where the algorithm would prioritize certain transactions for eviction but might still accommodate previously evicted transactions if space permits, thereby optimizing the mempool through selective inclusion based on fee rates.