New approximate optimality conditions of strong type in multiobjective generalized Nash equilibrium problems
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
This paper aims to derive optimality conditions for a multiobjective generalized Nash equilibrium problem (MGNEP) involving constraints in multiobjective games.We demonstrate that any efficient solution satisfies the standard approximate strong Karush-Kuhn-Tucker (KKT) condition.However, recognizing that these conditions may be overly stringent, we propose a new approximate strong KKT condition specifically tailored for MGNEPs, named MGNEP-ASKKT.To ensure the convergence of an MGNEP-ASKKT sequence towards an MGNEP-SKKT point, we introduce the concept of conecontinuity regularity, adapted for MGNEPs.Under this framework, we present an enhanced Lagrangiantype algorithm for solving MGNEPs and establish its global convergence to an MGNEP-SKKT point.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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