A new look at projected gradient method for equilibrium assignment
Bibliographic record
Abstract
This paper presents a very efficient implementation of a projected gradient variant for the solution of the network equilibrium traffic assignment in the space of path flows. The new algorithm exploits certain properties of the method in order to reduce the necessary computations for flow changes. One can compute several measures of relative gap that are common in the literature and practice of traffic assignment. The novelty of the results obtained demonstrates that this method is efficient even though it was not considered to be so in previous work. It obtains very fine solutions oftraffic assignment problems which exhibit relative gaps of the order of 10(super -6). The method as well as extensive computational results are presented. The test problems originate from transportation planning practice on five continents. Some examples of the computational times and comparative results with the linear approximation (F&W) method are given for mediumsize network of 3000 links (Winnipeg): and a large size network of 30,000links (Sydney). For the latter network a solution equivalent to 500 iterations of F&W is obtained after 10 iterations of the projected gradient method after 842 s. Similar results were obtained on other large scale networks. The performance of this new algorithm compares favourably to the Bar Gera origin based method. For the covering abstract see ITRD E145999
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".