On the Clique Decomposition Impact to the Optimal Power Flow Semidefinite Relaxation Solve Time
Bibliographic record
Abstract
Managing intermittent generation in electric power systems with high penetration of renewable sources of energy presents major operational challenges. Faster, more efficient optimization techniques are essential to mitigate this intermittency and ensure grid reliability. Convex relaxations of optimal power flow (OPF) problem offer tractable means of solving the nonlinear, non-convex OPF problem. Specifically, the semidefinite relaxation yields the tightest lower bound for the OPF but require careful exploitation of sparsity to remain computationally viable when scaling to large problem instances. This exploitation can be achieved through clique decomposition of the semidefinite constraint. In this work, we experiment with various clique decomposition algorithms and demonstrate that the resulting OPF solve time is highly sensitive to the choice of decomposition. Our main contribution is showing that the optimal decomposition depends on both the network topology and the demand profile. We find that some networks have a preferred decomposition that performs well across demands, while others require demand-dependent choices, suggesting a learning-based approach to predict the optimal decomposition for minimizing OPF solve time.
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 | 0.003 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".