Counting Feedback Vertex Sets of All Sizes in Subexponential Time, Conditionally
TL;DR
This algorithm combines a whole-edge forest identity, subset-index scalar networks, sparse parity filtering, and disjoint high-degree branching to compute the complete size distribution deterministically in time and polynomial working space.
Abstract
Assuming a Boolean tensor contraction hypothesis (Theorem D.1 of OpenAI's recent preprint refuting the Exponential Time Hypothesis), we study exact feedback vertex set counting in general graphs. For an $n$-vertex, $m$-edge simple graph, we compute the complete size distribution deterministically in $\mathrm{poly}(n+m)\,2^{O(n(\log\log n)^2/\sqrt{\log n})}$ time and polynomial working space. The algorithm combines a whole-edge forest identity, subset-index scalar networks, sparse parity filtering, and disjoint high-degree branching. A binomial inversion recovers the multiplicities introduced by exceptional-set guesses. The counting interface supports fixed vertices, minimum solutions, and unranking. Component markers yield induced-tree and induced-path counts within the same bound. Sparse Hamilton, forest, and Steiner edge-tree counting follow from the scalar encoding.
