Part of cluster mempool: #30289
This replaces the cluster linearization algorithm introduced in #30126 and #30286 (a combination of LIMO with candidate-set search), with a completely different algorithm: spanning-forest linearization, which appears to have much better performance for hard clusters. See this post for a comparison between various linearization algorithms, and this post for benchmarks comparing them. Replaying historical mempool data on it shows that it can effectively linearize every observed cluster up to 64 transactions optimally within tens of microseconds, though pathological examples can be created which take longer.
The algorithm is effectively a very specialized version of the simplex algorithm to the problem of finding high-feerate topological subsets of clusters, but modified to find all consecutive such subsets concurrently rather than just the first one. See the post above for how it is related.
It represents the cluster as partitioned into a set of chunks, each with a spanning tree of its internal dependencies connecting the transactions. Randomized improvements are made by selecting dependencies to add and remove to these spanning trees, merging and splitting chunks, until no more improvements are possible. Like simplex, it does not necessarily make progress in every step, and thus has no upper bound on its runtime, but randomization makes long runtimes very unlikely, and additionally makes it hard to adversarially construct clusters in which the algorithm reliably makes bad choices.
