The Use of Eigenvalues for Finding Equilibrium Probabilities of Certain Markovian Two-Dimensional Queueing Problems
Bibliographic record
Abstract
A number of papers have appeared recently using eigenvalues for solving steady-state queueing problems. In this paper, we analyze Markovian systems with two state variables, the level X 1 and the phase X 2 , X 1 ≤ 0, 0 ≤ X 2 ≤ N. Except for some boundary levels, the rates of the events are independent of the level, and no event can change X 1 , X 2 , or X 1 + X 2 by more than 1. In this case, the eigenvectors are essentially Sturm sequences, which implies that all eigenvalues are real. The properties of the Sturm sequences allow us to design an extension of the binary search to find all eigenvalues. As it turns out, once the interval containing an eigenvalue is narrowed down sufficiently, it is preferable to use Newton's method. A computational-complexity study indicates that the resulting algorithm should be signicantly faster than matrix-iterative methods. Two numerical examples are discussed involving servers with breakdowns, and in both cases, our method yields highly accurate results. Tentative reasons why this is to be expected are provided.
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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 | 0.002 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".