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Norm Control for Inverses of Convolutions and Large Matrices Nikolai Nikolski University Bordeaux 1 Steklov Institute / Chebyshev Lab St.Petersburg Motivation: Effective Inversions (constructive, algorithmic, norm controlled)


  1. Norm Control for Inverses of 
 Convolutions and Large Matrices Nikolai Nikolski University Bordeaux 1 Steklov Institute / Chebyshev Lab St.Petersburg

  2. • Motivation: Effective Inversions (constructive, algorithmic, norm controlled) • Example 1: Convolution T: f ↦ f ⋆ S on a group G as a map on a Banach function space X • The visible spectrum of T: the range of the Fourier transform Sˆ(Gˆ) . A necessary condition for inversion: δ =: inf| Sˆ| > 0 «Well posed inversion»: ||T ⁻ ¹ || ≤ c( δ ), δ >0.

  3. • Motivation: Effective Inversions (constructive, algorithmic, norm controlled) • Example 2: Condition Numbers of Matrices T, n ⨯ n: CN(T)= ||T|| · || T ⁻ ¹ || . • The visible spectrum of T : eigenvalues λ ᵢ (T), i= 1,…,n; an invertibility condition: δ =: inf| λ ᵢ (T)| > 0 «Well posed inversion»: CN(T)= ||T|| · || T ⁻ ¹ || ≤ c( δ /||T||), δ >0.

  4. Enough motivations?.. Finally, let him who has never used a convolution or a large matrix cast the first stone…

  5. My goal in effective inversions is to understand: 
 • Relations «Full Spectrum»/ «Visible Spectrum» (the Wiener- Pitt phenomenon) • «Invisible» but Numerically Detectable Spectrum (c( δ )= ∞ )

  6. Plan for today: 1.Convolutions/Fourier multipliers 2.Large Matrices 3.Some Integration Operators

  7. II. Large Matrices • A is an n ⨉ n matrix • ||A|| ≤ 1 ⇒ ||A ⁻ ¹ || ≤ 1/|det(A)| ≤ 1/ δ ⁿ where δ = min| σ (A)| • For a Banach normed ℂⁿ , | · | it is √ n times worth: ||A ⁻ ¹ || ≤ √ en/|det(A)| - J.Schäffer (1970); sharpness – E.Gluskin, M.Meyer, A.Pajor; J.Bourgain; H.Queffelec (1993) • My subject below: Matrices commuting with a given A (or, just functions of A)

  8. Summary • The nature of «invisible spectrum» is different (characters measurable wrt «thin σ -algebras», or forced holomorphic extensions, or complex homomorphisms in fibers over the boundary). • The «invisible» but numerically detectable spectrum comes from a «true invisible spectrum» and its discontinuity wrt to a weak approximation.

  9. The End 
 ***

  10. Thank you! And Happy Birthday to N.G.M.!!

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