Superiorization of block accelerated cyclic subgradient methods
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Bibliographic record
Abstract
In this paper, we introduce a block version and a perturbed block version of the accelerated cyclic subgradient projections method with constraints and give their convergence analyses. The performance of the algorithm is illustrated with a numerical example from the computed tomography and six standard nonlinear test problems. We compare our algorithms with the accelerated cyclic subgradient projections method. Our algorithms produce better results than accelerated cyclic subgradient projections method and have ability to reduce the value of an objective function. Furthermore, the perturbed block version is able to control semiconvergence phenomenon comparing two other methods.
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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 | 0.001 |
| 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
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