The maximum weight perfect matching problem for complete weighted graphs is in PC
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
There are efficient sequential algorithms that use linear programming (LP) for computing maximum weight matchings. Finding a deterministic parallel algorithm for computing maximum weight matchings in complete graphs has been an open problem for some time. Since LP is known to be P-complete, then, by the parallel computation thesis, it is unlikely that there exists an NC algorithm that uses LP to solve the maximum weight matching problem. The authors present an LP-based parallel algorithm for maximum weight matching in a complete weighted graph. The algorithm is designed for the EREW PRAM model of parallel computation, and runs in O(n/sup 3//p+n/sup 2/logn) time for p<or=n, where p is the number of processors and n is the number of vertices in the graph. This algorithm provides an optimal speedup with respect to the O(n/sup 3/) sequential LP-based solution of Gabow (1974) or Lawler (1976), for p<or=n/log n. This is the first deterministic optimal speedup parallel algorithm designed for the maximum weight matching problem on complete graphs.<<ETX>>
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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 it