Novel Approaches to Hard Discrete Optimization
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
On the distribution of values in the quadratic assignment problem by A. Barvinok and T. Stephen Modeling and optimization in massive graphs by V. Boginski, S. Butenko, and P. M. Pardalos A tale on guillotine cut by M. Cardei, X. Cheng, X. Cheng, and D.-Z. Du Wavelength assignment algorithms in multifiber networks by M. X. Cheng, Z. Gong, X. Huang, H. Zhao, X. Jia, and D. Li Indivisibility and divisbility polytopes by D. Coppersmith and J. Lee The dual active set algorithm and the iterative solution of linear programs by W. W. Hager Positive eigenvalues of generalized words in two Hermitian positive definite matrices by C. J. Hillar and C. R. Johnson Semi-infinite linear programming approaches to semidefinite programming problems by K. Krishnan and J. E. Mitchell SDP versus LP relaxations for polynomial programming by J. B. Lasserre An approximation scheme for the rectilinear Steiner minimum tree in presence of obstructions by M. Min, S. C.-H. Huang, J. Liu, E. Shragowitz, W. Wu, Y. Zhao, and Y. Zhao A convex feasibility problem defined by a nonlinear separation oracle by F. S. Mokhtarian Efficient algorithms for the smallest enclosing ball problem in high dimensional space by G. Zhou, J. Sun, and K.-C. Toh.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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