An empirical comparison of multi-agent optimization algorithms
Why this work is in the frame
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Bibliographic record
Abstract
In the past decade a large number of distributed algorithms for solving large-scale convex optimization problems have been proposed and analyzed in the literature, especially from the perspective of multi-agent systems. Although it is fairly well understood which algorithms have the most desirable theoretical properties, there has been very little work investigating and evaluating practical implementations of these algorithms, and there is a non-trivial gap between theory and practice. For example, many of the theoretical analyses ignore important practical issues such as asynchronism and communication delays. In this paper we perform an empirical evaluation of non-doubly stochastic multi-agent distributed optimization algorithms for large-scale convex optimization and open source the code. We find that a first order asynchronous subgradient optimization algorithm can actually out-perform state-of-the-art synchronous algorithms in a practical scenario for both small and large multiagent networks running on a high performance cluster.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.002 | 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