A Modified Distributed Gradient Dynamics for Multiagent Optimization on Directed Networks
Bibliographic record
Abstract
This paper considers the distributed convex optimization problem over directed multi-agent networks. We introduce a continuous-time coordination algorithm to solve unconstrained optimization problems with additive structure. The proposed algorithm can be interpreted as a modified version of the distributed subgradient method, enhanced with an augmented scalar state variable. Each agent is assumed to know its out-degree. Unlike existing methods that rely on a perturbed version of the push-sum algorithm, the proposed algorithm does not require any specific initialization. As a result, it is capable of handling strongly connected networks with sporadically varying sizes. We show that the proposed network flow is guaranteed to converge to the global minimizer of a sum of convex functions, provided that the local objective functions are strongly convex and have Lipschitz-continuous gradients. Additionally, by considering a class of admissible time-varying gains/step-sizes, our analysis substantiates an explicit sublinear rate of convergence for the proposed algorithm.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".