Equivalence of Applicative Functors and Multifunctors
January 25, 2024 Β· Declared Dead Β· π arXiv.org
"No code URL or promise found in abstract"
Evidence collected by the PWNC Scanner
Authors
Andreas Abel
arXiv ID
2401.14286
Category
cs.PL: Programming Languages
Cross-listed
cs.LO
Citations
1
Venue
arXiv.org
Last Checked
4 months ago
Abstract
McBride and Paterson introduced Applicative functors to Haskell, which are equivalent to the lax monoidal functors (with strength) of category theory. Applicative functors F are presented via idiomatic application $\_\circledast\_ : F (A \to B) \to F A \to F B$ and laws that are a bit hard to remember. Capriotti and Kaposi observed that applicative functors can be conceived as multifunctors, i.e., by a family liftA$_n$ : $(A_1 \to ... \to A_n \to C) \to F A_1 \to ... \to F A_n \to F C$ of zipWith-like functions that generalize pure $(n=0)$, fmap $(n=1)$ and liftA2 $(n=2)$. This reduces the associated laws to just the first functor law and a uniform scheme of second (multi)functor laws, i.e., a composition law for liftA. In this note, we rigorously prove that applicative functors are in fact equivalent to multifunctors, by interderiving their laws.
Community Contributions
Found the code? Know the venue? Think something is wrong? Let us know!
π Similar Papers
In the same crypt β Programming Languages
R.I.P.
π»
Ghosted
R.I.P.
π»
Ghosted
Tensor Comprehensions: Framework-Agnostic High-Performance Machine Learning Abstractions
R.I.P.
π»
Ghosted
Glow: Graph Lowering Compiler Techniques for Neural Networks
R.I.P.
π»
Ghosted
Learnable Programming: Blocks and Beyond
R.I.P.
π»
Ghosted
Scenic: A Language for Scenario Specification and Scene Generation
R.I.P.
π»
Ghosted
Vandal: A Scalable Security Analysis Framework for Smart Contracts
Died the same way β π» Ghosted
R.I.P.
π»
Ghosted
Federated Learning: Strategies for Improving Communication Efficiency
R.I.P.
π»
Ghosted
In-Datacenter Performance Analysis of a Tensor Processing Unit
R.I.P.
π»
Ghosted
Deep Convolutional Neural Networks for Computer-Aided Detection: CNN Architectures, Dataset Characteristics and Transfer Learning
R.I.P.
π»
Ghosted