Bibliographic record
Abstract
A nonlinear complementarity method for solving nonlinear optimal power flow problems is presented. This method stems from proposed reformulation of complementarity problems as nonlinear systems of equations which are, in turn, solved by a Newton-type method. To reformulate optimal power flow problems as nonlinear systems of equations we employ a function /spl psi//sub /spl mu//:/spl Rscr//sup 2//spl rarr//spl Rscr/ a that satisfies the property /spl psi//sub /spl mu//(a, b)=0/spl hArr/a>0, b>0 and ab=/spl mu/, for any /spl mu/>0. Then, unlike interior-point methods, the new method handles the complementarity conditions for optimality, s/sub i/>0, /spl pi//sub i//spl ges/0 and s/sub i//spl pi//sub i/=0, without requiring that s/sub i/>0 and /spl pi//sub i//spl ges/0 be satisfied at every iterate. Numerical results illustrate the viability of the proposed method as applied to several power networks. A comparison with two interior-point algorithms is discussed.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".