claimroutine
Random parametrization is injective and nonproportional
Random matrix entries give an injective, nonproportional map except with small probability
models the random subspace by a random matrix
Statement (paper)
Let be independent and uniform, and let for . Then the binary linear map from to is non-injective with probability at most . Conditioned on injectivity, its image is a uniform binary -dimensional subspace of , and the nonproportionality condition fails with probability apart from the event of noninjectivity.
In plain English
Pick the entries independently and uniformly, and set . The map from to is non-injective with probability at most . Given injectivity, its image is a uniform binary -dimensional subspace, and the nonproportionality condition fails with probability apart from noninjectivity.
Why it's here
This lets the proof of the lemma replace a uniformly random binary subspace by the image of an explicit random matrix with independent uniform entries, at the cost of a vanishing error. Later claims, such as the specialization step via the polynomial zero bound, can then reason about the independent entries directly.
Classified routine:
Elementary counting/union bound over the nonzero , each giving a uniform vector in , plus the symmetry argument that a uniform random parametrization yields a uniform subspace; a standard reduction step inside the genericity lemma.
Uses
Nothing else in the paper
Used by
Sources
PaperOpen
- Section
- §3.2 Generic spaces of characters
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sections/02-latent.tex:262
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