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Connor Colombe

dblp:270/2218 · DBLP profile ↗
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1ranked-venue papers
1as first author
1since 2021 · last 2021
—ORCID · unresolved

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

Theory of computation · 1 · 1 first-author · 1 since 2021

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
Computational geometry · 50% Approximation and online algorithms · 50%

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

TopicWeightPapersLastEvidence papers
Approximation and online algorithms
approximation algorithms
0.512021
Approximating the (Continuous) Fréchet Distance · SoCG 2021
Computational geometry › curve similarity
fréchet distance
0.512021
Approximating the (Continuous) Fréchet Distance · SoCG 2021
Computational geometry
shape analysis
0.512021
Approximating the (Continuous) Fréchet Distance · SoCG 2021
Approximation and online algorithms › approximation algorithms
subquadratic approximation
0.512021
Approximating the (Continuous) Fréchet Distance · SoCG 2021

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

decision-to-optimization reduction · 0.5
YearPublicationVenuePosition
2021 Approximating the (Continuous) Fréchet Distance
abstract
We describe the first strongly subquadratic time algorithm with subexponential approximation ratio for approximately computing the Fréchet distance between two polygonal chains. Specifically, let $P$ and $Q$ be two polygonal chains with $n$ vertices in $d$-dimensional Euclidean space, and let $α\in [\sqrt{n}, n]$. Our algorithm deterministically finds an $O(α)$-approximate Fréchet correspondence in time $O((n^3 / α^2) \log n)$. In particular, we get an $O(n)$-approximation in near-linear $O(n \log n)$ time, a vast improvement over the previously best know result, a linear time $2^{O(n)}$-approximation. As part of our algorithm, we also describe how to turn any approximate decision procedure for the Fréchet distance into an approximate optimization algorithm whose approximation ratio is the same up to arbitrarily small constant factors. The transformation into an approximate optimization algorithm increases the running time of the decision procedure by only an $O(\log n)$ factor.
Connor Colombe, Kyle Fox
SoCG1