2-categories and cyclic homology

Slevin, Paul (2016) 2-categories and cyclic homology. PhD thesis, University of Glasgow.

Full text available as:
[thumbnail of 2016slevinphd.pdf] PDF
Download (729kB)


The topic of this thesis is the application of distributive laws between comonads to the theory of cyclic homology. The work herein is based on the three papers 'Cyclic homology arising from adjunctions', 'Factorisations of distributive laws', and 'Hochschild homology, lax codescent,and duplicial structure', to which the current author has contributed. Explicitly, our main aims are:

1) To study how the cyclic homology of associative algebras and of Hopf algebras in the original sense of Connes and Moscovici arises from a distributive law, and to clarify the role of different notions of bimonad in this generalisation.
2) To extend the procedure of twisting the cyclic homology of a unital associative algebra to any duplicial object defined by a distributive law.
3) To study the universality of Bohm and Stefan’s approach to constructing duplicial objects, which we do in terms of a 2-categorical generalisation of Hochschild (co)homology.
4) To characterise those categories whose nerve admits a duplicial structure.

Item Type: Thesis (PhD)
Qualification Level: Doctoral
Keywords: Mathematics, category theory, 2-categories, higher category theory, homological algebra, distributive laws, hopf algebras, hopf algebroids.
Subjects: Q Science > QA Mathematics
Colleges/Schools: College of Science and Engineering > School of Mathematics and Statistics > Mathematics
Funder's Name: Engineering & Physical Sciences Research Council (EPSRC), Engineering & Physical Sciences Research Council (EPSRC)
Supervisor's Name: Kraehmer, Dr. Ulrich
Date of Award: 2016
Depositing User: Mr Paul Slevin
Unique ID: glathesis:2016-7381
Copyright: Copyright of this thesis is held by the author.
Date Deposited: 31 May 2016 10:06
Last Modified: 28 Jun 2016 12:22
URI: https://theses.gla.ac.uk/id/eprint/7381

Actions (login required)

View Item View Item


Downloads per month over past year