K_ε(A) scales as Θ(A √log A) for ε = A^{-β} (β ≥ 1) and between A √log A and A^{3/2} for exponentially small ε, via approximation theory and χ²-divergence control.
An algorithm for computing the capacity of arbitrary discrete memoryless channels
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The rate-distortion infimum is attained for lower semi-continuous distortions on locally compact Polish alphabets via one-point compactification for bounded distortions and concentration-compactness for unbounded coercive distortions.
EM's monotonicity and local rate are unified by the spectral operator G = I - DT that equals the missing-information ratio and observed-likelihood Hessian, enabling accelerated local updates.
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Support Size of $\varepsilon$-Capacity-Achieving Inputs for the Amplitude-Constrained AWGN Channel
K_ε(A) scales as Θ(A √log A) for ε = A^{-β} (β ≥ 1) and between A √log A and A^{3/2} for exponentially small ε, via approximation theory and χ²-divergence control.
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Rate-distortion Theory with Lower Semi-continuous Distortion on Noncompact Alphabets
The rate-distortion infimum is attained for lower semi-continuous distortions on locally compact Polish alphabets via one-point compactification for bounded distortions and concentration-compactness for unbounded coercive distortions.
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Expectation-Maximization as a Spectrally Governed Relaxation Flow
EM's monotonicity and local rate are unified by the spectral operator G = I - DT that equals the missing-information ratio and observed-likelihood Hessian, enabling accelerated local updates.