Another Updated Parameter for the Hestenes-Stiefel Conjugate Gradient Method
Bibliographic record
Abstract
The conjugate gradient (CG) methods are considered as one of the most popular methods for solving linear and non-linear unconstrained optimization problems, especially the problems of large-scale, that is because they are characterized by low memory requirements and strong local and global convergence properties. The method of Hestenes-Stiefel (HS) usually gives good numerical results in the practical computation. However, theoretically, its convergence properties are uncertain. To address the convergence failure of HS method, many choices for its update parameter have been proposed such as the choice of Gilbert and Nocedal in 1992, of Hager and Zhang in 2005, and of Yousif et al. in 2022. In this paper, motivated by these updated parameters, we propose another updated parameter for HS, and hence another CG method which inherits all the convergence properties of Gilbert and Nocedal, Hager and Zhang, and of Yousif et al. and has better numerical results. To show the efficiency and robustness of the new modified method in practice, a numerical experiment was done.
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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 | 0.001 | 0.004 |
| 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 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".