Reduction of Laurent-series subspaces at zero #
Subspaces of ι → K⸨X⸩ are reduced to subspaces of ι → K by taking constant coefficients of
their integral (power-series) members. Valuation-adapted Gaussian elimination produces an
integral frame, which shows that this reduction preserves dimension. Multiplying coordinates by
powers of the parameter then gives a rank inequality between filtrations, which is the order
reversal step of the small-subspace expansion argument.
Main definitions #
LaurentReduction.F: the Laurent-series fieldK⸨X⸩used as coefficient field.LaurentReduction.Integral,LaurentReduction.IntegralVector: membership inK⟦X⟧, coordinatewise.LaurentReduction.reduction,LaurentReduction.reduce: constant coefficients.LaurentReduction.IntegralFrame: an integral spanning family with integral dual coordinates and linearly independent reductions.LaurentReduction.reducedSubspace: the span of reductions of integral members.LaurentReduction.weightMap,LaurentReduction.liftMap,LaurentReduction.conjugatedWeight: coordinate weights, constant coordinate changes, and their composite.
Main results #
LaurentReduction.exists_integralFrame: every subspace has an integral frame.LaurentReduction.finrank_reducedSubspace: reduction preserves dimension.LaurentReduction.weighted_filtration_rank,LaurentReduction.conjugated_filtration_rank: the rank form of order reversal.
References #
The Laurent-series coefficient field used for reduction at the parameter zero.
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The constant coefficient of a Laurent series.
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An integral spanning frame with integral dual coordinates and independent reductions.
- length : ℕ
Number of frame vectors.
The frame vectors in the ambient Laurent-series space.
Dual coordinate functionals on the full ambient space.
The frame spans the specified subspace.
- vector_integral (j : Fin self.length) : IntegralVector (self.vector j)
Every frame vector has integral coordinates.
- coordinate_integral (j : Fin self.length) : IntegralFunctional (self.coordinate j)
The dual coordinates preserve the integral lattice.
The coordinate functionals are dual to the frame vectors.
- reduced_independent : LinearIndependent K fun (j : Fin self.length) => reduce (self.vector j)
Reduction at zero preserves independence.
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Valuation-adapted Gaussian elimination constructs an integral frame for any Laurent-series subspace. The coordinate functionals guarantee saturation.
Full integral-lattice saturation: every integral member has integral basis coordinates, explicitly given by the constructed functionals.
The key valuation step of order reversal: an integral normalized input having a nonzero low reduction cannot give a high leading output coefficient.
Low coordinates detect reduction throughout U. This controls its full integral image lattice, not only the images of an arbitrary integral basis.
Restrict an integral frame to a selected family of basis vectors.
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Choose an integral-lattice subspace on whose reduction a specified projection is injective, of dimension equal to that projection's rank.
Filtration version of order reversal, before a constant coordinate change. This uses the whole integral lattice on both sides.
Extend a linear map to Laurent-series vectors using its constant coefficient matrix.
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Extend a linear equivalence to Laurent-series vectors.
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Constant coordinates on the input and output may differ (Taylor on the input, reverse Taylor on the output in the application).