Bibliographic record
Abstract
The split feasibility problem (SFP), which provides a unified framework to model a wide range of inverse problems, has received much considerable attention in the literature.However, how to efficiently solve SFPs is still an interesting topic.In this paper, we introduce a block-wise formulation for algorithmic design.Specifically, we first introduce an auxiliary variable to formulate the original SFP as a constrained minimization problem with a block structure, which paves a new way to find solutions of SFPs.Then, we show that the employments of some classical gradient-type optimization algorithms produce very simple, yet quite efficient iterative schemes to find a solution of SFPs when the underlying block structure could be exploited.The parallel iterative schemes of the proposed blockwise algorithms are not only efficient to deal with the case that the projections onto the convex sets have explicit representations, but also are possibly valuable for solving large-scale SFPs without explicit projections onto the underlying sets.Some numerical results on synthetic examples support the idea of this paper.
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How this classification was reachedexpand
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 | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".