Put . We use the following closed-range lemma:
Indeed,
Away from this kernel, zero is separated from the spectrum of the positive self-adjoint operator exactly when
for some . This is equivalent to closed range. Zero is then absent from the essential spectrum exactly when . Similarly,
because measures the cokernel.
Applying the lecture definitions of the three essential spectra of a closed operator now gives
and
If is normal, so is . The spectral theorem gives
and maps the spectral mass of at exactly to the spectral mass of at . Thus zero is isolated with finite multiplicity for exactly when is an isolated eigenvalue of finite multiplicity for . Consequently
Normality is essential. Let be the unilateral shift and take . Its spectrum is the closed unit disk, so is not in the discrete spectrum. But
whose zero eigenvalue is isolated and simple. Hence while .
Set . The squares of the singular values of are the eigenvalues of the finite-dimensional positive operator
The Rayleigh-Ritz variational principle and its min-max characterization show that, as the trial space grows, its -st eigenvalue counted upward cannot increase. Therefore, for each fixed ,
once . Being nonnegative, it has a limit
The core assumption ensures that these Ritz limits are the min-max values of , rather than values for a smaller closed restriction.
  • If , then for some , so every .
  • If is a discrete eigenvalue of multiplicity , exactly
vanish, while .
For , define
In the first case for all sufficiently large . In the second case its first summands tend to one and all remaining summands eventually vanish, so . In the third case . These are exactly the three values in the definition of , and hence
Let
and, for , form the finite rectangular matrix
Every entry is available from . Let its singular values, padded and ordered as in the question, be
For fixed , Parseval's identity gives convergence of the finite Gram matrices:
as . Consequently, if
then
Singular values are Lipschitz under scalar shifts, so the subsequent limit and part ii give
Define the finite-information arithmetic functions
Finite-matrix singular values can be obtained by arithmetic eigenvalue approximation, so these functions form the required arithmetic tower using only . The two inner limits recover the limiting singular values, while the outer limit is exactly the multiplicity formula from part ii:

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