English

Deductive Systems and Coherence for Skew Prounital Closed Categories

Logic in Computer Science 2021-01-12 v1 Category Theory

Abstract

In this paper, we develop the proof theory of skew prounital closed categories. These are variants of the skew closed categories of Street where the unit is not represented. Skew closed categories in turn are a weakening of the closed categories of Eilenberg and Kelly where no structural law is required to be invertible. The presence of a monoidal structure in these categories is not required. We construct several equivalent presentations of the free skew prounital closed category on a given set of generating objects: a categorical calculus (Hilbert-style system), a cut-free sequent calculus and a natural deduction system corresponding to a variant of planar (= non-commutative linear) typed lambda-calculus. We solve the coherence problem for skew prounital closed categories by showing that the sequent calculus admits focusing and presenting two reduction-free normalization procedures for the natural deduction calculus: normalization by evaluation and hereditary substitutions. Normal natural deduction derivations (beta-eta-long forms) are in one-to-one correspondence with derivations in the focused sequent calculus. Unexpectedly, the free skew prounital closed category on a set satisfies a left-normality condition which makes it lose its skew aspect. This pitfall can be avoided by considering the free skew prounital closed category on a skew multicategory instead. The latter has a presentation as a cut-free sequent calculus for which it is easy to see that the left-normality condition generally fails. The whole development has been fully formalized in the dependently typed programming language Agda.

Keywords

Cite

@article{arxiv.2101.03809,
  title  = {Deductive Systems and Coherence for Skew Prounital Closed Categories},
  author = {Tarmo Uustalu and Niccolò Veltri and Noam Zeilberger},
  journal= {arXiv preprint arXiv:2101.03809},
  year   = {2021}
}

Comments

In Proceedings LFMTP 2020, arXiv:2101.02835

R2 v1 2026-06-23T21:59:03.844Z