A simple parallel algorithm for large‐scale portfolio problems
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
Purpose Although the mean‐variance portfolio selection model has been investigated in the literature, the difficulty associated with the application of the model when dealing with large‐scale problems is limited. The aim of this paper is to close the gap by using the quadratic risk (standard deviation risk) function and finite iteration technique to remove difficulties in quadratic programming. Design/methodology/approach Using van de Panne' approach, this paper proposes a finite technique to optimize large‐scale portfolio selection problem. Findings The proposal of parallel algorithm structure to the model provides a clearer decision framework to significantly enhance the efficiency of the portfolio selection process. Originality/value The proposal of parallel algorithm structure to the mean‐variance portfolio selection model provides a clearer decision framework to significantly enhance the efficiency of the portfolio selection process. An empirical example that illustrates the application and benefits of the method is provided.
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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 | 0.007 | 0.001 |
| 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 | 0.001 | 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