Rectangular kernels of block evaluations #
Block evaluations of ∑ᵢ Aᵢ ⊗ Xᵢ, built from coefficient matrices Aᵢ and square witness
matrices Xᵢ. If such an evaluation is injective, the matrices Aᵢ cannot map a subspace into a
smaller one. Combined with small-subspace expansion, this shows that the reverse-Taylor tests
have no nonzero kernel family of bounded support whenever the same coefficients admit an
invertible block witness.
Main definitions #
Rectangular.blockEval: the block evaluation map onFin e → Fin q → F.Rectangular.blockMatrix: the corresponding sum of Kronecker products.Rectangular.componentSpace,Rectangular.coefficientMatrix: the span of a family and the coordinates of its members in a basis of that span.Rectangular.imageCombination,Rectangular.concatMatrix: the combined images under several maps, and the concatenated coefficient matrix.
Main results #
Rectangular.no_shrunk_subspace: an injective block evaluation forbids shrinking subspaces.Rectangular.kernel_impossible: an expanding kernel family contradicts injectivity.Rectangular.blockEval_injective_of_isUnit: an invertible block matrix gives an injective block evaluation.RectangularExpansion.rectangular_kernel_zero: the reverse-Taylor tests have trivial bounded-support kernel families.
References #
The ordinary Kronecker evaluation, with the two vector indices curried in reverse order. This is only a coordinate convention.
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The subspace spanned by the components of a vector.
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A component regarded as a vector in the component span.
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Coordinates of each component in a basis of their span.
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The combined output map of the w * k component images.
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A kernel vector with sufficiently expansive component span contradicts any invertible block evaluation of the same coefficient matrices.
The rectangular Taylor tests have no nonzero kernel component family when the same coefficients have an invertible matrix witness.