A full Fejér transmission at one positive time determines the dissipative generator exactly. A scalar budget or a compressed measurement supplies different information; this chapter tracks those distinctions.
5. The Dissipative / Lambda Face
We develop the first canonical functional-calculus face of K. All objects are functions of K alone.
5.1 Contraction semigroup and Fejér transmission
Definition 5.1.Contraction semigroup.
For K≥0 selfadjoint, St:=e−tK=∫0∞e−tλdEK(λ), t≥0. (St) is a strongly continuous semigroup of contractions, St=I on kerK.
On kerK, ΛT(K) is the identity. On Hdiss=(kerK)⊥, nonzero point modes of K are mapped into (0,1). In general,
0≤ΛT(K)≤I,kerΛT(K)={0},
but 0 or 1 may occur in the spectrum as non-eigen spectral/accumulation points when K is unbounded or when 0 lies in the continuous spectrum of K∣Hdiss.
Proposition 5.3.Properties of ΛT.
ΛT(K) is bounded selfadjoint with 0≤ΛT(K)≤I, and kerΛT(K)={0}. It need not be bounded below when K is unbounded.
ΛT(K)∈W∗(K); commutes with K.
ΛT(K)→I strongly as T↓0.
ΛT(K)→PkerK strongly as T→∞.
5.2 Exact recovery of the generator from the Lambda face
Theorem 5.4.Exact Fejér/Lambda recovery of the generator.
Fix T>0 and define
fT(λ)=Tλ1−e−Tλ,fT(0):=1.
Then fT:[0,∞)→(0,1] is continuous, strictly decreasing, and Borel-invertible. Equivalently, it is a homeomorphism from [0,∞) onto (0,1] with the subspace topology.
Let K≥0 be selfadjoint and set A:=ΛT(K)=fT(K). Let ℓT:(0,1]→[0,∞) denote the inverse function of fT, and extend it to a Borel function ψT:[0,1]→[0,∞) by assigning an arbitrary finite value at 0, e.g. ψT(0)=0, and setting ψT(y)=ℓT(y) for 0<y≤1. Then
K=ψT(A).
Thus, with this harmless spectral convention at 0,
K=fT−1(ΛT(K)).
Moreover,
kerK=ker(I−ΛT(K)).
Consequently the stationary/dissipative splitting is recovered from the Lambda face alone:
H0=kerK=EA({1})H,Hdiss=(kerK)⊥=EA([0,1))H,
where EA is the spectral measure of A=ΛT(K).
Proof. The integral formula
fT(λ)=T1∫0Te−tλdt
holds for all λ≥0, including λ=0. Continuity follows from dominated convergence. If 0≤λ1<λ2, then e−tλ2<e−tλ1 for every t∈(0,T], hence
Thus fT is strictly decreasing. Also fT(0)=1, fT(λ)>0 for every finite λ≥0, and limλ→∞fT(λ)=0. Therefore fT maps [0,∞) bijectively onto (0,1]. Since it is continuous and strictly monotone, its inverse ℓT=fT−1:(0,1]→[0,∞) is continuous, hence Borel.
Now let K≥0 be selfadjoint and let A=fT(K). By the spectral mapping and composition rules for the Borel functional calculus,
EA(B)=EK(fT−1(B))
for Borel sets B⊆[0,1]. In particular,
EA({0})=EK(fT−1({0}))=EK(∅)=0.
Thus the assigned value of ψT at 0 is invisible to the functional calculus. Since (ψT∘fT)(λ)=λ for λ≥0, the unbounded Borel functional calculus gives
ψT(A)=ψT(fT(K))=(ψT∘fT)(K)=K,
with equality of selfadjoint operators, including equality of domains.
For the kernel statement, since fT−1({1})={0}, we have EA({1})=EK({0}). For a selfadjoint contraction A, ker(I−A)=EA({1})H, while kerK=EK({0})H. Hence ker(I−ΛT(K))=kerK. The dissipative sector is the orthogonal complement of this stationary sector, equivalently
Hdiss=(kerK)⊥=(I−EA({1}))H=EA([0,1))H.□
◻
Corollary 5.5.Coherence recovery from the Lambda face.
Let K≥0 be selfadjoint, fix T>0, and set A:=ΛT(K). Let h:[0,∞)→R be a declared admissible coherence symbol. Then the coherent generator C=h(K) is determined by the Lambda face A through
C=h(ψT(A))=(h∘ψT)(A),
where ψT is the Borel inverse convention from Theorem 5.4. In particular, once h is declared, the exact Lambda operator at one fixed scale determines the coherent functional-calculus face generated by h(K).
Proof. By Theorem 5.4, K=ψT(A). Applying the Borel functional calculus to the declared symbol h gives h(K)=h(ψT(A))=(h∘ψT)(A). The value of ψT at 0 is again immaterial because EA({0})=0. ◻
Theorem 5.4 is an exact operator-recovery statement from the full bounded operator ΛT(K) at a fixed scale. It is not a noisy finite empirical recovery theorem: finite samples, scalar determinants, matrix elements, or approximate readouts require separate inverse-problem and stability hypotheses. In particular, the inverse ψT is unbounded near 0, so high-spectral recovery may be ill-conditioned without additional quantitative control.
5.3 The dissipative budget
Definition 5.6.Dissipative / lambda budget.
Under (D1)–(D2),
DT(K):=−logdet∗(ΛT(K)Hdiss).
If Hdiss={0}, we set DT(K)=0. For finite-rank K with positive eigenvalues λ1,…,λr,
The Fredholm condition at one positive scale therefore implies it at every positive scale.
Proof. Write rT(λ)=1−fT(λ). From the time average,
rT(λ)=T1∫0T(1−e−tλ)dt≤2Tλ.
If Kd is trace class, positivity and heredity of the trace ideal show that rT(Kd) is trace class. Moreover Kd is bounded, and [−logfT(λ)]/λ extends continuously at zero with value T/2. It is bounded on [0,∥Kd∥], so −logfT(Kd) is trace class as well.
Conversely suppose R=rT(Kd) is trace class. It is a positive compact contraction. Since kerfT(Kd)=0, the number one is not an eigenvalue of R. Compactness implies ∥R∥<1: otherwise the compact positive operator would have eigenvalue one. Hence fT(Kd)=I−R≥(1−∥R∥)I. As fT(λ)→0 at infinity, the spectral measure of Kd is supported in a bounded interval [0,M]. The function λ/rT(λ) extends continuously at zero with value 2/T, so Kd≤cR on this interval for a finite constant c. Thus Kd is trace class. Also
0≤−log(I−R)≤1−∥R∥R,
which gives trace class directly. The Fredholm determinant product for I−R and the absolutely convergent sum of its logarithms yield
−log∗det(I−R)=j∑−logfT(λj)=Tr[−logfT(Kd)].
See [24] for the determinant identities used here. ◻
5.4 Asymptotic expansions
Proposition 5.8.Small-T expansion.
Under (D1) with K1K>0 trace-class on Hdiss,
DT(K)=2TTr(K1K>0)−24T2Tr(K21K>0)+O(T3),T↓0.
Proof. The scalar expansion is g(s)=s/2−s2/24+O(s3) near zero. Trace-class positivity implies that Kd is bounded and Kd3 is trace class. On 0≤λ≤∥Kd∥, the scalar remainder is bounded by a constant times T3λ3 for all sufficiently small T. Summing the remainder proves the formula. ◻
Proposition 5.9.Large-T expansion, finite rank.
For finite-rank K with positive eigenvalues λ1,…,λr,
DT(K)=rlogT+j=1∑rlogλj+o(1),T→∞.
Proof. For every positive eigenvalue, g(Tλ)=logT+logλ−log(1−e−Tλ). The last term tends to zero, and the sum is finite. ◻
Strict monotonicity in T when Hdiss={0} (since g is strictly increasing).
Spectral determinant form on finite rank.
Proof. Unitary covariance of the functional calculus gives invariance; Fredholm determinants multiply under direct sums, giving additivity. The spectral product gives the remaining dependence. Finally g′(s)=1/s−1/(es−1)>0 for s>0, since es>1+s. Every nonzero positive mode therefore increases its contribution with T. ◻
5.7 Empirical spectral measures and Lambda distances
For finite-rank dissipative sector Hdiss with positive eigenvalues λ1,…,λr, define the empirical spectral measure
μK:=r1j=1∑rδλj.
The rank-normalized Lambda profile may then be written as
NK(T)=∫[g(Tλ)−log(Tλ)]dμK(λ).
Let λˉK=∫λdμK(λ) and μK:=(λ↦λ/λˉK)#μK. The dimensionless profile is
NK(τ)=∫[g(τx)−log(τx)]dμK(x).
For a declared scale window I=[τ−,τ+]⊂(0,∞), define the Lambda-profile distance
dΛ(K1,K2;I):=τ∈IsupNK1(τ)−NK2(τ).
On compact spectral classes bounded away from 0, dΛ is Lipschitz controlled by Wasserstein-type distances between the normalized empirical spectral measures:
dΛ(K1,K2;I)≤CIW1(μK1,μK2),
where CI depends on the declared spectral window and compact class. In fact, if both normalized measures are supported in [a,b]⊂(0,∞), one may take CI=1/a. To see this, put Fτ(x)=−log(1−e−τx). Then
∣Fτ′(x)∣=eτx−1τ≤x1≤a1.
For any coupling γ of the two measures, their profile difference is ∫(Fτ(x)−Fτ(y))dγ(x,y), whose absolute value is at most a−1∫∣x−y∣dγ. Taking the infimum over couplings and the supremum over τ∈I proves the bound. Thus small Wasserstein perturbations of the normalized spectrum give small profile perturbations.
Proposition 5.11.Profile injectivity on compact positive spectral classes.
Let 0<a<b<∞, and let μ1,μ2 be finite positive Borel measures supported in [a,b]. If
∫[a,b]−log(1−e−Tλ)dμ1(λ)=∫[a,b]−log(1−e−Tλ)dμ2(λ)for all T>0,
then μ1=μ2.
Consequently, on compact positive spectral classes, the rank-normalized Lambda profile
determines the empirical spectral measure μK. The dimensionless profile
NK(τ)=∫−log(1−e−τx)dμK(x)
determines the mean-normalized measure μK; it does not determine the absolute spectral scale unless λˉK is also supplied.
Proof. This is Theorem B.1 in Appendix B. The identity
g(s)−logs=−log(s1−e−s)−logs=−log(1−e−s)
identifies the profile used here with the transform treated there. ◻
Proposition 5.11 is a compact-positive-support identifiability theorem. It applies to the declared finite-rank / compact-spectral setting and to compact spectral windows bounded away from 0. It does not assert that finitely many empirical samples of the profile determine an infinite-dimensional carrier, and it does not recover the absolute scale from the dimensionless profile without the mean scale λˉK.