Demo Data from EDBT 2026 / reviewers in the wild / expert
Jonathan E. Atkins
dblp:21/237
· DBLP profile ↗
2ranked-venue papers
2as first author
0since 2021 · last 1998
—ORCID · none
Domains — the database's venue-derived domains; a paper can count in several
Theory of computation · 2 · 2 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 |
Algorithms and data structures · 67% Mathematical optimization · 33% |
Topics — the 2 heaviest of 3, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Mathematical optimization
combinatorial optimization |
0.0 | 1 | 1998 | A Spectral Algorithm for Seriation and the Consecutive Ones Problem · SIAM J. Comput. 1998 |
Algorithms and data structures › ranking
seriation |
0.0 | 1 | 1998 | A Spectral Algorithm for Seriation and the Consecutive Ones Problem · SIAM J. Comput. 1998 |
Methods — techniques the papers use, named apart from their topics
spectral algorithm · 0.0
| Year | Publication | Venue | Position |
|---|---|---|---|
| 1998 | A Spectral Algorithm for Seriation and the Consecutive Ones ProblemabstractIn applications ranging from DNA sequencing through archeological dating to sparse matrix reordering, a recurrent problem is the sequencing of elements in such a way that highly correlated pairs of elements are near each other. That is, given a correlation function f reflecting the desire for each pair of elements to be near each other, find all permutations $\pi$ with the property that if $\pi(i) < \pi(j) < \pi(k)$ then $f(i,j) \ge f(i,k)$ and $f(j,k) \ge f(i,k)$. This seriationproblem is a generalization of the well-studied consecutive ones problem. We present a spectral algorithm for this problem that has a number of interesting features. Whereas most previous applications of spectral techniques provide only bounds or heuristics, our result is an algorithm that correctly solves a nontrivial combinatorial problem. In addition, spectral methods are being successfully applied as heuristics to a variety of sequencing problems, and our result helps explain and justify these applications. Jonathan E. Atkins, Erik G. Boman, Bruce Hendrickson |
SIAM J. Comput. | 1 |
| 1996 | On Physical Mapping and the Consecutive Ones Property for Sparse Matrices
Jonathan E. Atkins, Martin Middendorf |
Discret. Appl. Math. | 1 |