Demonstration venue · read-only. Every page can be browsed; the buttons that would change it are switched off. Create an account to run TaxoReview on your own data. A simulation from public publication records: the people listed were not invited and did not agree to review.
? Tutorial for this page

Alex Yufit

dblp:90/7660 · DBLP profile ↗
← Back
1ranked-venue papers
1as first author
0since 2021 · last 2011
—ORCID · none

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

Computer networks · 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 · 79% Mathematical optimization · 21%

Topics — the 4 heaviest of 5, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Coding theory › error-correcting codes
decoding
0.112011
Efficient Linear Programming Decoding of HDPC Codes · IEEE Trans. Commun. 2011
Coding theory › error-correcting codes › LDPC codes
linear programming decoding
0.112011
Efficient Linear Programming Decoding of HDPC Codes · IEEE Trans. Commun. 2011
Mathematical optimization › integer programming
branch-and-bound
0.012011
Efficient Linear Programming Decoding of HDPC Codes · IEEE Trans. Commun. 2011
Mathematical optimization › discrete optimization
mixed integer linear programming
0.012011
Efficient Linear Programming Decoding of HDPC Codes · IEEE Trans. Commun. 2011

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

branch-and-bound · 0.1automorphism group · 0.1
YearPublicationVenuePosition
2011 Efficient Linear Programming Decoding of HDPC Codes
abstract
We propose several improvements for Linear Programming (LP) decoding algorithms for High Density Parity Check (HDPC) codes. First, we use the automorphism groups of a code to create parity check matrix diversity and to generate valid cuts from redundant parity checks. Second, we propose an efficient mixed integer decoder utilizing the branch and bound method. We further enhance the proposed decoders by removing inactive constraints and by adapting the parity check matrix prior to decoding according to the channel observations. Based on simulation results the proposed decoders achieve near-ML performance with reasonable complexity.
Alex Yufit, Asi Lifshitz, Yair Be'ery
IEEE Trans. Commun.1