Graphical Quadratic Algebra

Fuente: arXiv
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Autores principales: Stein, Dario, Zanasi, Fabio, Piedeleu, Robin, Samuelson, Richard
Formato: Preprint
Publicado: 2024
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author Stein, Dario
Zanasi, Fabio
Piedeleu, Robin
Samuelson, Richard
author_facet Stein, Dario
Zanasi, Fabio
Piedeleu, Robin
Samuelson, Richard
contents Convex analysis and Gaussian probability are tightly connected, as mostly evident in the theory of linear regression. Our work introduces an algebraic perspective on such relationship, in the form of a diagrammatic calculus of string diagrams, called Graphical Quadratic Algebra (GQA). We show that GQA is a complete axiomatisation for the category of quadratic relations, a compositional formulation of quadratic problems. Moreover, we identify a sub-theory of GQA which is complete for the category of Gaussian probabilistic processes. We show how GQA may be used to study linear regression and probabilistic programming.
format Preprint
id arxiv_https___arxiv_org_abs_2403_02284
institution arXiv
publishDate 2024
record_format arxiv
spellingShingle Graphical Quadratic Algebra
Stein, Dario
Zanasi, Fabio
Piedeleu, Robin
Samuelson, Richard
Logic in Computer Science
Category Theory
Optimization and Control
G.3; F.3.2; F.4.0
Convex analysis and Gaussian probability are tightly connected, as mostly evident in the theory of linear regression. Our work introduces an algebraic perspective on such relationship, in the form of a diagrammatic calculus of string diagrams, called Graphical Quadratic Algebra (GQA). We show that GQA is a complete axiomatisation for the category of quadratic relations, a compositional formulation of quadratic problems. Moreover, we identify a sub-theory of GQA which is complete for the category of Gaussian probabilistic processes. We show how GQA may be used to study linear regression and probabilistic programming.
title Graphical Quadratic Algebra
topic Logic in Computer Science
Category Theory
Optimization and Control
G.3; F.3.2; F.4.0
url https://arxiv.org/abs/2403.02284