Ranking Decomposition for the Discrete Ordered Median Problem
Bibliographic record
Abstract
Given a set [Formula: see text] of size n, a nonnegative, integer-valued distance matrix D of dimensions [Formula: see text], an integer [Formula: see text] and an integer-valued weight vector [Formula: see text], the discrete ordered median problem (DOMP) consists of selecting a subset [Formula: see text] of exactly p points from [Formula: see text] (also referred to as the centers) so as to: 1) assign each point in [Formula: see text] to its closest center in [Formula: see text]; 2) rank the resulting distances (between every point and its center) from smallest to largest in a sorted vector that we denote [Formula: see text]; 3) minimize the scalar product [Formula: see text]. The DOMP generalizes several classical location problems such as the p-center, the p-median and the obnoxious median problem. We introduce an exact branch-and-bound algorithm to solve the DOMP. This branch-and-bound decouples the ranking attribute of the problem to form a series of simpler subproblems which are solved using innovative binary search methods. We consider several acceleration techniques such as warm-starts, primal heuristics, variable fixing, and symmetry breaking. We perform a thorough computational analysis and show that the proposed method is competitive against several MIP models from the scientific literature. We also comment on the limitations of our method and propose avenues of future research. History: Accepted by Andrea Lodi, Area Editor for Design & Analysis of Algorithms—Discrete. Funding: This work was supported by the Natural Sciences and Engineering Research Council of Canada [Grants 2017-06106, 2020-06311, and 2021-03327]. Supplemental Material: The software that supports the findings of this study is available within the paper and its Supplemental Information ( https://pubsonline.informs.org/doi/suppl/10.1287/ijoc.2023.0059 ) as well as from the IJOC GitHub software repository ( https://github.com/INFORMSJoC/2023.0059 ). The complete IJOC Software and Data Repository is available at https://informsjoc.github.io/ .
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.018 | 0.003 |
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".