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

Jonathan E. Atkins

dblp:21/237 · DBLP profile ↗
← Back
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

TopicWeightPapersLastEvidence papers
Mathematical optimization
combinatorial optimization
0.011998
A Spectral Algorithm for Seriation and the Consecutive Ones Problem · SIAM J. Comput. 1998
Algorithms and data structures › ranking
seriation
0.011998
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
YearPublicationVenuePosition
1998 A Spectral Algorithm for Seriation and the Consecutive Ones Problem
abstract
In 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