Bibliographic record
Abstract
In this thesis we will analyse the two algorithms for linear programming (LP) presented by Stojkovic and Stanimirovic [15] in 2001. One of the methods, which the authors call the "minimal angles method" (MA method) was designed to determine either an optimal extreme point or an extreme point adjacent to an optimal extreme point. Unfortunately, the theorem upon which the MA method is based is not valid. This was shown by Li [10] in 2004 with two counterexamples. We will show that one of the counterexamples itself is not valid, and will provide an alternate, valid counterexample. We will also provide a careful study of the MA method to see if there is a class of LP, where it can be applied. This leads to a method we call the "active cone method" which can be used for 2 variable LPs. The second method of Stojkovic and Stanimirovic [15] in 2001 uses an analogy to Game Theory to devise a process, based on "dominated strategies", to simplify a certain class of LP. We extend this idea and present an iterative method which provides further reduction to a large class of LPs.Dept. of Mathematics and Statistics. Paper copy at Leddy Library: Theses & Major Papers - Basement, West Bldg. / Call Number: Thesis2004 .W365. Source: Masters Abstracts International, Volume: 43-03, page: 0987. Advisers: R. J. Caron; T. Traynor. Thesis (M.Sc.)--University of Windsor (Canada), 2004.
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 | 0.005 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.003 | 0.006 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.004 | 0.008 |
| Insufficient payload (model declined to judge) | 0.133 | 0.038 |
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".