Exact Algorithms and Complexity of the Smallest Grammar Problem
Xiangle Chen
The Smallest Grammar Problem (SGP) asks for a smallest straight-line grammar generating a given string, with size measured by the total right-hand-side length. For an input of length $n\ge2$ over at most $σ$ symbols, where $2\leσ\le n$, let $R=\log_σn$. We give a deterministic exact algorithm running in $n^{O(1)}2^{O(n\log\log n/(R\sqrt{\log n}))}$ time and polynomial space, with constants independent of $σ$. This gives time $2^{O(n\log\log n/(\log n)^{3/2})}$ for fixed alphabets and $2^{O(n\log\log n/\sqrt{\log n})}$ for arbitrary alphabets, up to polynomial factors. The algorithm combines pairwise disjoint representative occurrences of dictionary strings, a sparse interval certificate, and the Boolean tensor contraction theorem of OpenAI. It explicitly enforces dictionary membership, rule boundaries, and the grammar-size budget. Independently of that contraction theorem, disjoint representatives yield exact algorithms in $O^*(1.872258^n)$ time and $2^{O((n/R)\log(R+1))}$ time using $O(n)$ words of space. Anchor-based grammar projections establish binary NP-completeness and reductions with tunable alphabet size. We state their consequences as unconditional quantitative transfers from SGP algorithms to sparse SAT algorithms, and quantify the additional loss from sparsifying general formulas.
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