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Record W2753502872

Reconfiguring Graph Colorings

2017· dissertation· en· W2753502872 on OpenAlexaff
Krishna Vaidyanathan

Bibliographic record

VenueUWSpace (University of Waterloo) · 2017
Typedissertation
Languageen
FieldDecision Sciences
TopicScheduling and Timetabling Solutions
Canadian institutionsBlackberry (Canada)
Fundersnot available
KeywordsComputer scienceCombinatoricsMathematics
DOInot available

Abstract

fetched live from OpenAlex

Graph coloring has been studied for a long time and continues to receive \ninterest within the research community \\cite{kubale2004graph}. It has applications \nin scheduling \\cite{daniel2004graph}, timetables, and compiler register \nallocation \\cite{lewis2015guide}. The most popular variant of graph coloring, \nk-coloring, can be thought of as an assignment of $k$ colors to the vertices of a \ngraph such that adjacent vertices are assigned different colors. \n \nReconfiguration problems, typically defined on the solution space of search problems, \nbroadly ask whether one solution can be transformed to another solution using \nstep-by-step transformations, when constrained to one or more specific transformation \nsteps \\cite{van2013complexity}. One well-studied reconfiguration problem is the \nproblem of deciding whether one k-coloring can be transformed to another k-coloring \nby changing the color of one vertex at a time, while always maintaining a k-coloring \nat each step. \n \nWe consider two variants of graph coloring: acyclic coloring and equitable \ncoloring, and their corresponding reconfiguration problems. A k-acylic coloring is \na k-coloring where there are more than two colors used by the vertices of each \ncycle, and a k-equitable coloring is a k-coloring such that each color class, which is \ndefined as the set of all vertices with a particular color, is nearly the same \nsize as all others. \n \nWe show that reconfiguration of acyclic colorings is PSPACE-hard, and that for \nnon-bipartite graphs with chromatic number 3 there exist two k-acylic colorings \n$f_s$ and $f_e$ such that there is no sequence of transformations that can \ntransform $f_s$ to $f_e$. We also consider the problem of whether two \nk-acylic colorings can be transformed to each other in at most $\\ell$ steps, and \nshow that it is in XP, which is the class of algorithms that run in time \n$O(n^{f(k)})$ for some computable function $f$ and parameter $k$, where in this \ncase the parameter is defined to be the length of the reconfiguration sequence \nplus the length of the longest induced cycle. \n \nWe also show that the reconfiguration of equitable colorings is PSPACE-hard \nand W[1]-hard with respect to the number of vertices with the same color. We \ngive polynomial-time algorithms for Reconfiguration of Equitable Colorings when \nthe number of colors used is two and also for paths when the number of colors \nused is three.

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)0.007
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: Theoretical or conceptual
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.018
Threshold uncertainty score0.062

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.007
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.002
Science and technology studies0.0010.002
Scholarly communication0.0020.003
Open science0.0020.003
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0180.004

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 head0.066
GPT teacher head0.304
Teacher spread0.237 · 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
GenreOther

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
Published2017
Admission routes1
Has abstractyes

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