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Christian Staib

dblp:415/6514 · DBLP profile ↗
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1ranked-venue papers
0as first author
1since 2021 · last 2025
—ORCID · none

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

Artificial intelligence and machine learning · 1 · 1 since 2021Graphics, computer vision, multimedia, augmented reality and games · 1 · 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
Graph algorithms and graph theory · 57% Computational complexity · 19% Computational geometry · 19%

Topics — the 6 heaviest of 6, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Graph algorithms and graph theory › shortest path
euclidean shortest path
0.912025
Fast and Stronger Lower Bounds for Planar Euclidean Shortest Paths · IJCAI 2025
Graph algorithms and graph theory
graph algorithms
0.912025
Fast and Stronger Lower Bounds for Planar Euclidean Shortest Paths · IJCAI 2025
Computational complexity
lower bounds
0.912025
Fast and Stronger Lower Bounds for Planar Euclidean Shortest Paths · IJCAI 2025
Graph algorithms and graph theory
shortest path
0.912025
Fast and Stronger Lower Bounds for Planar Euclidean Shortest Paths · IJCAI 2025
Computational geometry › visibility
visibility graph
0.912025
Fast and Stronger Lower Bounds for Planar Euclidean Shortest Paths · IJCAI 2025
Algorithms and data structures › search algorithms › heuristic search
a* search
0.312025
Fast and Stronger Lower Bounds for Planar Euclidean Shortest Paths · IJCAI 2025

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

instance simplification · 0.9distance queries · 0.9
YearPublicationVenuePosition
2025 Fast and Stronger Lower Bounds for Planar Euclidean Shortest Paths
abstract
We consider the problem of quickly providing strong lower bounds for the planar Euclidean shortest path (ESP) problem. Such lower bounds are crucial for guiding the search in A* type approaches or for proving quality guarantees for algorithms that compute approximate solutions. Our contributions are two-fold: we show how to simplify ESP instances such that computing and storing a visibility graph becomes feasible while distances within the simplified instance are guaranteed to constitute lower bounds for the original problem instance. Furthermore we show how to precompute a space efficient data structure that allows to perform distance queries on visibility graphs within few microseconds with negligible space overhead.
Stefan Funke, Claudius Proissl, Christian Staib, Felix Weitbrecht
IJCAI4