Optimal detection of intersections between convex polyhedra
Bibliographic record
Abstract
For a polyhedron P in ℝd, denote by |P| its combinatorial complexity, i.e., the number of faces of all dimensions of the polyhedra. In this paper, we revisit the classic problem of preprocessing polyhedra independently so that given two preprocessed polyhedra P and Q in ℝd, each translated and rotated, their intersection can be tested rapidly. For d = 3 we show how to perform such a test in O(log |P| + log |Q|) time after linear preprocessing time and space. This running time is the best possible and improves upon the last best known query time of O(log |P| log |Q|) by Dobkin and Kirkpatrick (1990). We then generalize our method to any constant dimension d, achieving the same optimal O(log |P| + log |Q|) query time using a representation of size O(|P| ⌊d/2⌋+ε) for any ε > 0 arbitrarily small. This answers an even older question posed by Dobkin and Kirkpatrick 30 years ago. In addition, we provide an alternative O(log |P| + log |Q|) algorithm to test the intersection of two convex polygons P and Q in the plane.
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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 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".