Two floating point LLL reduction algorithms
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Bibliographic record
Abstract
The Lenstra, Lenstra and Lov\\'{a}sz (LLL) reduction is the most popular lattice reduction and is a powerful tool for solving many complex problems in mathematics and computer science. The blocking technique casts matrix algorithms in terms of matrix-matrix operations to permit efficient reuse of data in the algorithms. In this thesis, we use the blocking technique to develop two floating point block LLL reduction algorithms, the left-to-right block LLL (LRBLLL) reduction algorithm and the alternating partition block LLL (APBLLL) reduction algorithm, and give the complexity analysis of these two algorithms. We compare these two block LLL reduction algorithms with the original LLL reduction algorithm (in floating point arithmetic) and the partial LLL (PLLL) reduction algorithm in the literature in terms of CPU run time, flops and relative backward errors. The simulation results show that the overall CPU run time of the two block LLL reduction algorithms are faster than the partial LLL reduction algorithm and much faster than the original LLL, even though the two block algorithms cost more flops than the partial LLL reduction algorithm in some cases. The shortcoming of the two block algorithms is that sometimes they may not be as numerically stable as the original and partial LLL reduction algorithms. The parallelization of APBLLL is discussed.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 0.001 |
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