A multi-view on the CQ algorithm for split feasibility problems: From optimization lens
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
The split feasibility problem (SFP) provides a powerful unified model to characterize many real-world inverse problems arising from image reconstruction and intensity-modulated radiation therapy. As we know, the original CQ algorithm, which is essentially a gradient-projection method, is one of the most popular methods in the SFP literature. In this paper, we revisit the CQ algorithm and give a multi-view on such an algorithm from another four different optimization methods. Specifically, we show that the CQ algorithm can be viewed as applications of partially linearized alternating minimization algorithms, fixed-point methods, DC (Difference-of-Convex) algorithms, and majorization-minimization (MM) algorithms to some structured optimization reformulations of the SFP. Our analysis could provide some new insights into the treatment of SFPs and related topics.
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 | 0.000 | 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 | 0.000 |
| 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 it