Bottleneck Convex Subsets: Finding k Large Convex Sets in a Point Set
Bibliographic record
Abstract
Chvátal and Klincsek (1980) gave an [Formula: see text]-time algorithm for the problem of finding a maximum-cardinality convex subset of an arbitrary given set [Formula: see text] of [Formula: see text] points in the plane. This paper examines a generalization of the problem, the Bottleneck Convex Subsets problem: given a set [Formula: see text] of [Formula: see text] points in the plane and a positive integer [Formula: see text], select [Formula: see text] pairwise disjoint convex subsets of [Formula: see text] such that the cardinality of the smallest subset is maximized. Equivalently, a solution maximizes the cardinality of [Formula: see text] mutually disjoint convex subsets of [Formula: see text] of equal cardinality. We give an algorithm that solves the problem exactly, with running time polynomial in [Formula: see text] when [Formula: see text] is fixed. We then show the problem to be NP-hard when [Formula: see text] is an arbitrary input parameter, even for points in general position. Finally, we give a fixed-parameter tractable algorithm parameterized in terms of the number of points strictly interior to the convex hull.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".