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LeanPool.DavisKahan.ForTauCeti.Analysis.OperatorIdeal.ApproximationNumber.MinMaxUpper

The min--max theorem for approximation numbers #

ForTauCeti/Analysis/OperatorIdeal/ApproximationNumber/MinMax.lean proves the easy half of the Courant--Fischer characterisation: a uniform lower modulus on a test subspace of rank greater than n bounds aₙ(T) from below. This module proves the converse, which is the half that carries the content:

r < aₙ(T)  →  ∃ s > r, ∃ n + 1 independent vectors spanning a subspace on which ‖T x‖ ≥ s ‖x‖.

Together the two say that aₙ(T) is the supremum, over (n+1)-dimensional subspaces, of the lower modulus of T there — for an arbitrary bounded operator between complex Hilbert spaces, with no compactness, separability or finite-dimensionality hypothesis.

Why this is not a spectral theorem #

The classical proof cuts the spectrum of |T| at s with a projection-valued measure. This one does not: ForTauCeti/Analysis/InnerProductSpace/SpectralCutoff.lean gets the same splitting of E from the continuous functional calculus, as the kernel of (|T| - s)₊ and its orthogonal complement. The proof here is then a dichotomy on that complement M:

Only the second branch can fail, and it fails into a contradiction, so the first branch always holds.

Consequences #

This unblocks the results that had been routed through vendor/Spectra's min--max bridge: the Ky Fan gauge triangle inequality, and with it the Ky Fan and symmetric-gauge operator ideals, and the orthogonal block-sum merge formulas.

Provenance #

A module of rank at least n carries n independent vectors.

Mathlib has the one-step extension exists_linearIndependent_snoc_of_lt_rank; this is the iterate, which is what a "there are n + 1 independent vectors" statement needs.

A module of rank greater than n carries n + 1 independent vectors.

The min--max upper bound for approximation numbers. If r is strictly below the nth approximation number of T, then T is bounded below by some s > r on a subspace spanned by n + 1 independent vectors.

This is the converse of ContinuousLinearMap.le_approximationNumber_of_linearIndependent, and the two together characterise aₙ(T) as a supremum of lower moduli. No compactness or finite-dimensionality is assumed.