Asset Details
MbrlCatalogueTitleDetail
Do you wish to reserve the book?
Representing the special linear group with block unitriangular matrices
by
Urschel, John
in
Brief Report
/ Group theory
/ Lower bounds
/ Machine learning
/ Mathematical research
/ Matrices
2023
Hey, we have placed the reservation for you!
By the way, why not check out events that you can attend while you pick your title.
You are currently in the queue to collect this book. You will be notified once it is your turn to collect the book.
Oops! Something went wrong.
Looks like we were not able to place the reservation. Kindly try again later.
Are you sure you want to remove the book from the shelf?
Oops! Something went wrong.
While trying to remove the title from your shelf something went wrong :( Kindly try again later!
Do you wish to request the book?
Representing the special linear group with block unitriangular matrices
by
Urschel, John
in
Brief Report
/ Group theory
/ Lower bounds
/ Machine learning
/ Mathematical research
/ Matrices
2023
Please be aware that the book you have requested cannot be checked out. If you would like to checkout this book, you can reserve another copy
We have requested the book for you!
Your request is successful and it will be processed during the Library working hours. Please check the status of your request in My Requests.
Oops! Something went wrong.
Looks like we were not able to place your request. Kindly try again later.
Representing the special linear group with block unitriangular matrices
Journal Article
Representing the special linear group with block unitriangular matrices
2023
Request Book From Autostore
and Choose the Collection Method
Overview
Abstract
We prove that every element of the special linear group can be represented as the product of at most six block unitriangular matrices, and that there exist matrices for which six products are necessary, independent of indexing. We present an analogous result for the general linear group. These results serve as general statements regarding the representational power of alternating linear updates. The factorizations and lower bounds of this work immediately imply tight estimates on the expressive power of linear affine coupling blocks in machine learning.
Publisher
Oxford University Press
Subject
This website uses cookies to ensure you get the best experience on our website.