Application of a Modified Gauss Elimination Technique for Separable Fuzzy Nonlinear Programming 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
In this study, a novel approach to resolving separable fuzzy nonlinear programming problems is presented.Utilizing a parametric form, the issues associated with separable fuzzy nonlinear programming, particularly those arising from uncertainty, ambiguity, and vagueness, are addressed.To resolve these issues, each separable function within the Separable Fuzzy Nonlinear Programming Problem (SFNPP) is approximated via a piecewise linear function.This approximation is then subjected to the standard graphical and simplex techniques to obtain a solution.Significantly, a novel variant of the Gauss elimination method for inequalities, specifically designed for separable fuzzy nonlinear programming problems, has been developed and implemented.Compared to previous methods, our approach offers notable advantages in terms of reduced computational time and enhanced precision, due to the simplicity of the calculations involved.
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.
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 | 0.001 | 0.000 |
| 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 | 0.000 | 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