Demo Data from EDBT 2026 / reviewers in the wild / expert
Alex Yufit
dblp:90/7660
· DBLP profile ↗
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
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Coding theory › error-correcting codes
decoding |
0.1 | 1 | 2011 | Efficient Linear Programming Decoding of HDPC Codes · IEEE Trans. Commun. 2011 |
Coding theory › error-correcting codes › LDPC codes
linear programming decoding |
0.1 | 1 | 2011 | Efficient Linear Programming Decoding of HDPC Codes · IEEE Trans. Commun. 2011 |
Mathematical optimization › integer programming
branch-and-bound |
0.0 | 1 | 2011 | Efficient Linear Programming Decoding of HDPC Codes · IEEE Trans. Commun. 2011 |
Mathematical optimization › discrete optimization
mixed integer linear programming |
0.0 | 1 | 2011 | 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
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2011 | Efficient Linear Programming Decoding of HDPC CodesabstractWe 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 |