Applicative theories offer a proof-theoretic framework for studying complexity. In this paper, we introduce a family of applicative theories to characterize classes ranging from constant-depth circuits to polynomial space. Our approach relies on a simple axiom scheme, the small variations of which allows to capture various forms of counting and alternation. This leads to highly uniform results. When applied on notation, the scheme captures bit counting, yielding a characterization of the functions computable by constant-depth, polynomial-size threshold circuits ($\textbf{FTC}^0$); when applied lexicographically, it allows for the counting of accepting computations, yielding a characterization of the functions in the counting hierarchy ($\textbf{FCH}$). Increasing the logical complexity of the induction formulas allowed in the scheme, we obtain characterizations of the functions computable by bounded Boolean circuits of logarithmic-depth and polynomial-size ($\textbf{FNC}^1$) and of the functions computable in polynomial space ($\textbf{FPSPACE}$). The latter characterization solves an open problem in the field. Finally, we investigate separation results. While it is unknown whether $\textbf{FNC}^1$ and $\textbf{FCH}$ are different, there is an oracle relative to which (relativized) $\textbf{FNC}^1$ is strictly contained in (relativized) $\textbf{FCH}$. We use this fact to separate the theories characterizing them, by exploring their ability to represent higher-order functionals.

翻译：暂无翻译