Variable Space Search for Graph Coloring
Bibliographic record
Abstract
Let G = (V,E) be a graph with vertex set V and edge set E. The k-coloring problem is to assign a color (a number chosen in {1,..., k}) to each vertex of G so that no edge has both endpoints with the same color. We present a new local search algorithm, called Variable Space Search (VSS), which we apply to the k-coloring problem. VSS extends the Formulation Space Search (FSS) methodology by considering sev-eral non equivalent formulations of a same problem, each one being associated with a set of neighborhoods and an objective function. The search moves from one formulation to another when it is blocked at a local optimum with a given formulation. The k-coloring problem is thus solved by combining different formulations of the problem which are not equivalent, in the sense that some constraints are possibly re-laxed in one search space and always satisfied in another. We show that the proposed algorithm improves on every local search used inde-pendently (i.e., with a unique search space), and is competitive with the currently best coloring methods, which are complex hybrid evolu-tionary algorithms. 1
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".