Projection-type methods with alternating inertial steps for solving multivalued variational inequalities beyond monotonicity
Bibliographic record
Abstract
In solving variational inequalities, the inertial extrapolation step is a highly powerful tool in algorithmic designs and analyses mainly due to the improved convergence speed that it contributes to the algorithms. However, it has been discovered that the presence of the inertial extrapolation steps in these methods for solving variational inequalities makes them lose some of their attractive properties, for example, the Fejr monotonicity (with respect to the solution set) of the sequence generated by projection-type methods for solving variational inequalities is lost when the iterative steps involve an inertial term, which makes these methods sometimes not converge faster than the corresponding algorithms without an inertial term. To avoid such a situation, we present two new projection-type methods with alternated inertial extrapolation steps for solving multivalued variational inequality problems, which inherit the Fejr monotonicity property of the projection-type method to some extent. Furthermore, we prove the convergence of the sequence generated by our methods under much relaxed assumptions on the inertial extrapolation factor and the multivalued mapping associated with the problem. Moreover, we establish the convergence rate of our methods and provide several numerical experiments of the new methods in comparison with other related methods in the literature.
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 machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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 source (direct Gemma or distilled Codex), 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".