A Novel Optimization-Based Pruning Strategy for Concave Minimum Distance Problems
Bibliographic record
Abstract
The simulation of multibody dynamical systems often requires the determination of the separation or interference distance between two moving objects. That is the case of robotic systems where it is often required to perform such distance queries at a very high speed. Some algorithms obtain the exact separation or interference distance while sacrificing computational speed, whereas other algorithms give fast results by sacrificing precision. In this paper, a novel two-stage optimization-based strategy is proposed to allow fast and precise distance calculations. In the first stage, a novel pruning strategy is used in order to obtain features that are closest to the other object and vice-versa. In the second stage, the set of closest features are used in a more common local optimization technique in order to solve a simple constrained optimization problem. The set of surfaces obtained through the pruning method is only a small subset of those describing the entire objects. As a result, the solution time needed to find the exact distance using the local optimization method in the second stage is drastically reduced. Numerical examples showing the algorithm’s capabilities are included
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 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) | 0.001 | 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".