A fully distributed algorithm for the nonconvex constrained optimization problem
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 addresses the nonconvex constrained optimization problem over multi-agent systems, where each agent possesses a nonconvex local objective function and a bounded convex constraint set. A fully distributed primal-dual algorithm is proposed to resolve this problem without relying on prior knowledge of network connectivity or problem-specific parameters. The key innovations include (i) the integration of a differential projection operator to handle local convex constraints, and (ii) the introduction of node-based adaptive control parameters to eliminate dependency on global information such as Lipschitz constants or Laplacian eigenvalues. By leveraging Lyapunov stability theory, we rigorously prove that the proposed algorithm asymptotically converges to a local optimal solution of the nonconvex problem. Furthermore, the algorithm’s effectiveness is validated through two numerical simulations. Comparative results demonstrate superior convergence and robustness against existing methods.
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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.003 | 0.001 |
| 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