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Elon Lindenstrauss

dblp:47/6622 · DBLP profile ↗
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1ranked-venue papers
1as first author
0since 2021 · last 2018
0000-0002-4757-6758ORCID · reported

Domains — the database's venue-derived domains; a paper can count in several

Theory of computation · 1 · 1 first-author

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Theoretical computer science
1 paper
Coding theory · 54% Mathematical optimization · 23% Information theory · 23%

Topics — the 5 heaviest of 5, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Mathematical optimization
dynamical systems		 0.312018
From Rate Distortion Theory to Metric Mean Dimension: Variational Principle · IEEE Trans. Inf. Theory 2018
Information theory › ergodic theory
metric mean dimension		 0.312018
From Rate Distortion Theory to Metric Mean Dimension: Variational Principle · IEEE Trans. Inf. Theory 2018
Coding theory › source coding › rate-distortion theory
rate-distortion function		 0.312018
From Rate Distortion Theory to Metric Mean Dimension: Variational Principle · IEEE Trans. Inf. Theory 2018
Coding theory › source coding
rate-distortion theory		 0.312018
From Rate Distortion Theory to Metric Mean Dimension: Variational Principle · IEEE Trans. Inf. Theory 2018
Coding theory › source coding
lossy source coding		 0.112018
From Rate Distortion Theory to Metric Mean Dimension: Variational Principle · IEEE Trans. Inf. Theory 2018

Methods — techniques the papers use, named apart from their topics

variational principle · 0.3
YearPublicationVenuePosition
2018 From Rate Distortion Theory to Metric Mean Dimension: Variational Principle
abstract
The purpose of this paper is to point out a new connection between information theory and dynamical systems. In the information theory side, we consider rate distortion theory, which studies lossy data compression of stochastic processes under distortion constraints. In the dynamical systems side, we consider mean dimension theory, which studies how many parameters per iterate we need to describe a dynamical system. The main results are new variational principles connecting rate distortion function to metric mean dimension.
Elon Lindenstrauss, Masaki Tsukamoto
 IEEE Trans. Inf. Theory1