Permanental Rank vs Determinantal Rank of Random Matrices over Finite Fields
Bibliographic record
Abstract
This paper is motivated by basic complexity and probability questions about permanents of random matrices over small finite fields, and in particular, about properties separating the permanent and the determinant. Let q be a fixed odd prime, and let k ≤ n both be growing. For a uniformly random n × k matrix A over 𝔽_q, we study the probability that all k × k submatrices of A have zero permanent; namely that A does not have full permanental rank. When k = n, this is simply the probability that a random square matrix over 𝔽_q has zero permanent, which we do not understand. We believe that the probability in this case is 1/q + o(1), which would be in contrast to the case of the determinant, where the answer is 1/q + Ω_q(1). Our main result is that when k is O(√n), the probability that a random n × k matrix does not have full permanental rank is essentially the same as the probability that the matrix has a 0 column, namely (1 +o(1)) k/qⁿ. In contrast, for determinantal (standard) rank the analogous probability is Θ(q^k/q^n). At the core of our result are some basic linear algebraic properties of the permanent that distinguish it from the determinant.
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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 | 0.005 | 0.036 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.007 |
| Scholarly communication | 0.003 | 0.009 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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 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".