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
As many real-world optimization problems are large-scale and expensive, the large search space and expensive gradient computation may lead to failure of metaheuristic and classical algorithms. The problem even gets more crucial as we move from continuous domain to the discrete or mixed type one, because most of the discrete optimization problems are NP-hard and cannot be treated as convex or linear optimization, therefore there exists no cost-effective algorithm to cope with large-scale discrete global optimization (LSDGO) problems. However, due to the low memory demand and computational cost of coordinate descent (CD) search methods they are appropriate algorithms for optimizing large-scale expensive problems. In this paper, we propose a discrete version of CD algorithm called Discrete Coordinate Descent (DCD) as an effective method for solving LSDGO problems. Our proposed algorithm makes the most of two essential phases referred to as finding the region of interest and folding the search space, which shrinks it into two halves per variable and results in ${\left( {\frac{1}{2}} \right)^D}$ shrinking of the whole search space at each iteration (D indicates the problem's dimension). Since the proposed algorithm shrinks the search space rapidly, it requires a low computational budget to find the optimal value for each coordinate. In order to investigate the efficiency of our algorithm precisely, we tested it on 20 well-known large-scale problems with dimensions of 30, 50, 100, and 1000. The results demonstrate the potency of DCD not only in low-scale discrete problems, but in large-scale discrete optimization problems as well.
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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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