A New Look at Projected Gradient Method for Equilibrium Assignment
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
A new adaptation of Rosen's projected gradient algorithm for solving fixed-demand equilibrium traffic assignments is developed. It is based on a Gauss-Seidel decomposition scheme in which origin-destination pairs are considered sequentially. The method operates in the space of path flows and shares this approach with earlier work on adapting the gradient projection method, the restricted simplicial decomposition, and the projected gradient adapted for solving equilibrium traffic assignments with explicit capacity constraints. The details of the algorithm are nevertheless quite different and are intended to solve large-scale problem instances. The development of the method is provided, and then computational experiments are performed with an implementation done with the Emme software package. Performance comparisons are carried out against the linear approximation method and the origin base algorithm code of Bar-Gera. The algorithm compares well with these methods and achieves relative gaps of the order of 10 -6 or 10 -7 in reasonable computing times. It also has the advantage of reaching more modest relative gaps of the order of 10 -4 in much shorter computing times than the linear approximation method.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.008 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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