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Record W3010708132 · doi:10.22215/etd/2019-13678

Improved Spanning and Routing Ratios on Geometric Graphs

2019· dissertation· en· W3010708132 on OpenAlexaff
Darryl Hill

Bibliographic record

Venuenot available
Typedissertation
Languageen
FieldComputer Science
TopicComputational Geometry and Mesh Generation
Canadian institutionsCarleton University
Fundersnot available
KeywordsDelaunay triangulationCombinatoricsConvex hullMathematicsComputer scienceRegular polygonDiscrete mathematicsGeometry

Abstract

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1 introduction 5 1.1 Navigation on Geometric Graphs 5 1.2 Classes of Geometric Graphs Considered 6 1.2.1 Θ k -Graphs and Y k -Graphs 6 1.2.2Delaunay Graphs 7 1.3 Previous Work 8 2 improved routing on the delaunay triangulation 14 2.1 Introduction 14 2.2 The MixedChordArc Algorithm 16 2.3 Bounding |P s, t | in a Balanced Configuration 18 2.3.1 Analysis Technique 19 2.3.2Proof of Lemma 2.3.4 22 2.3.3Proof of Lemma 2.3.3 23 2.4 Bounding P s, t in the General Case 24 2.5 Conclusion and Future Work 28 2.6 A Trace of MixedChordArc and an Illustration of the Proof of Theorem 2.2.1 29 2.7 Proof of Lemma 2.4.2 35 2.8 Proofs of Lemmas 2.3.8 and 2.3.9 -Analyzing Φ(C i-1 , C i ) 38 2.8.1 Analyzing dΦ(C i-1 ,C i ) dx(c i ) 40 2.8.2 Simplifying dΦ(C i-1 ,C i ) dx(c i ) contents 2 4 the spanning ratio of convex polyhedra whose vertices are on a sphere 85 4.1 Chains of Disks 86 4.2 Main Theorem 87 4.3 Conclusion 90 5 improved spanning ratio on theta-five 92 5.1 Introduction 92 5.2 Preliminaries 94 5.3 General Triangles T 1 and T 2 97 5.4 Analysis of |P a, b | 100 5.4.1 Vertex c in P 5 103 5.5 Down to K ≥ 5.70 107 5.5.1 Lemma 5.5.1,Φ ≤ Φ 1 109 5.5.2Lemma 5.5.2,Φ 1 ≤ Φ 2 110 5.6 Conclusion 111 6 conclusion 114 P R E FA C EThis thesis is in "integrated article format" in which each chapter is based on published papers, conference proceedings, or papers awaiting publication.• Chapter 2 considers competitive online local routing on the Delaunay triangulation.These results appeared in the proceedings of the European Symposium on Algorithms (ESA 18)[1].• Chapter 3 considers competitive online local routing on the Θ 4 -graph.These results appeared in the proceedings of the Symposium on Discrete Algorithms (SODA 19)[2].• Chapter 4 improves the spanning ratio of the convex hull of points on a sphere.This was part of a result published in the Journal of Computational Geometry (JoCG) [3].• Chapter 5 improves the spanning ratio of the Θ 5 -graph.It is awaiting publication.Metric Lower Bound Upper Bound L 1 , L ∞ (Square) ≈ 2.61[7] ≈ 2.61[7] (tight) TD (Equilateral triangles) 2[16] 2[16] L 2 (Circles) 1.5932[28] 1.998[27] Rectangles Open OpenΘ-graphs were introduced by Keil and Gutwin[21, 22] and Clarkson[17] as an alternative to Yao-graphs since they were easier to compute.A spanning ratio of 1/(cos θsin θ) is proven in both articles.Ruppert and Seidel[26] improved this to 1/(1 -2 sin(θ/2)).Chew's[15] paper on the spanning ratio of the L 1 -Delaunay triangulation is also a local routing algorithm on that same graph.Thus it simultaneously provides a bound on the routing ratio of the L 1 -Delaunay triangulation of √ 10.Bose and Morin [12] show that there are no 1-local memoryless routing algorithms that will work on any arbitrary graph.This implies that we must pair routing algorithms with particular classes of geometric graphs in order to route competitively.For example, in greedy routing, the message at vertex x moves to the neighbour y of x such that the distance |yt| is minimized, i.e., the neighbour of x that is closest to t.In Figures 1.3a and 1.3b we see examples of graphs where greedy routing cycles between two and three vertices respectively.However, greedy routing will always find the destination on the L 2 -Delaunay triangulation.They also give a competitive routing algorithm for the L 2 -Delaunay triangulation.However, they show that there is no competitive online routing algorithm under the Euclidean distance metric in arbitrary triangulations.Bose and Morin[13] provide a competitive local routing algorithm that works on triangulations that have the diamond property.This includes such graphs as the L 2 -Delaunay triangulation, the greedy triangulation, and the minimum weight triangulation.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.006
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.008
Threshold uncertainty score0.027

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.006
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.002
Science and technology studies0.0010.001
Scholarly communication0.0010.003
Open science0.0020.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0080.001

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.013
GPT teacher head0.253
Teacher spread0.240 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

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Citations0
Published2019
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