The boosted proximal difference-of-convex algorithm
Bibliographic record
Abstract
In this paper, we propose a boosted proximal difference-of-convex algorithm for solving a minimization problem composed of the sum of a smooth convex function and a continuously differentiable convex function minus a continuous and strongly convex function.By adding an additional line search step, the convergence of proximal difference-of-convex algorithm is accelerated.We prove that any limit point of iterative sequence is a critical point of generalized difference-of-convex programming, and the corresponding objective value decreases monotonically and converges.By assuming that the objective function satisfies the strong Kurdyka-Łojasiewicz inequality, we prove the convergence of whole sequence of the proposed algorithm and give the convergence rate.The strong convexity of the convex part of the minimization problem in [F.J. Aragón Artacho, P.T. Vuong, The boosted difference of convex functions algorithm for nonsmooth functions, SIAM J. Optim.30 (2020), 980-1006] is substituted by the Lipschitz continuity of the gradient of one convex function.Numerical experiments are given to demonstrate the performance of the proposed algorithm compared with the proximal difference-ofconvex algorithm.
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.
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 | 0.000 |
| 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 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".