Speeding up Dualization in the Fredman-Khachiyan Algorithm B
Bibliographic record
Abstract
The problem of computing the dual of a monotone Boolean function f is a fundamental problem in theoretical computer science with numerous applications. The related problem of duality testing (given two monotone Boolean functions f and g, declare that they are dual or provide a certificate that shows they are not) has a complexity that is not yet known. However, two quasi-polynomial time algorithms for it, often referred to as FK-A and FK-B, were proposed by Fredman and Khachiyan in 1996, with the latter having a better complexity guarantee. These can be naturally used as a subroutine in computing the dual of f. In this paper, we investigate this use of the FK-B algorithm for the computation of the dual of a monotone Boolean function, and present practical improvements to its performance. First, we show how FK-B can be modified to produce multiple certificates (Boolean vectors on which the functions defined by the original f and the current dual g do not provide outputs consistent with duality). Second, we show how the number of redundancy tests - one of the more costly and time-consuming steps of FK-B - can be substantially reduced in this context. Lastly, we describe a simple memoization technique that avoids the solution of multiple identical subproblems. We test our approach on a number of inputs coming from computational biology as well as combinatorics. These modifications provide a substantial speed-up, as much as an order of magnitude, for FK-B dualization relative to a naive implementation. Although other methods may end up being faster in practice, our work paves the way for a principled optimization process for the generation of monotone Boolean functions and their duals from an oracle.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".