Abstract

Nonlinearities in finite dimensions can be linearized by projecting them into infinite dimensions. Unfortunately, the familiar linear operator techniques that one would then hope to use often fail since the operators cannot be diagonalized. The curse of nondiagonalizability also plays an important role even in finite-dimensional linear operators, leading to analytical impediments that occur across many scientific domains. We show how to circumvent it via two tracks. First, using the well-known holomorphic functional calculus, we develop new practical results about spectral projection operators and the relationship between left and right generalized eigenvectors. Second, we generalize the holomorphic calculus to a meromorphic functional calculus that can decompose arbitrary functions of nondiagonalizable linear operators in terms of their eigenvalues and projection operators. This simultaneously simplifies and generalizes functional calculus so that it is readily applicable to analyzing complex physical systems. Together, these results extend the spectral theorem of normal operators to a much wider class, including circumstances in which poles and zeros of the function coincide with the operator spectrum. By allowing the direct manipulation of individual eigenspaces of nonnormal and nondiagonalizable operators, the new theory avoids spurious divergences. As such, it yields novel insights and closed-form expressions across several areas of physics in which nondiagonalizable dynamics arise, including memoryful stochastic processes, open nonunitary quantum systems, and far-from-equilibrium thermodynamics. The technical contributions include the first full treatment of arbitrary powers of an operator, highlighting the special role of the zero eigenvalue. Furthermore, we show that the Drazin inverse, previously only defined axiomatically, can be derived as the negative-one power of singular operators within the meromorphic functional calculus and we give a new general method to construct it. We provide new formulae for constructing spectral projection operators and delineate the relations among projection operators, eigenvectors, and left and right generalized eigenvectors. By way of illustrating its application, we explore several, rather distinct examples. First, we analyze stochastic transition operators in discrete and continuous time. Second, we show that nondiagonalizability can be a robust feature of a stochastic process, induced even by simple counting. As a result, we directly derive distributions of the time-dependent Poisson process and point out that nondiagonalizability is intrinsic to it and the broad class of hidden semi-Markov processes. Third, we show that the Drazin inverse arises naturally in stochastic thermodynamics and that applying the meromorphic functional calculus provides closed-form solutions for the dynamics of key thermodynamic observables. Finally, we draw connections to the Ruelle–Frobenius–Perron and Koopman operators for chaotic dynamical systems and propose how to extract eigenvalues from a time-series.

Highlights

  • Decomposing a complicated system into its constituent parts—reductionism—is one of science’s most powerful strategies for analysis and understanding

  • In the context of the current exposition, the most notable feature of the analyses across these many domains is that our questions, which entail tracking an observer’s state of knowledge about a process, necessarily induce a nondiagonalizable metadynamic that becomes the central object of analysis in each case. (This metadynamic is the so-called mixed-state presentation of Refs. 49 and 50.). This theme, and the inherent nondiagonalizability of prediction, is explored in greater depth elsewhere.[22,23]. We found that another nondiagonalizable dynamic is induced even in the context of quantum communication when determining how much memory reduction can be achieved if we generate a classical stochastic process using quantum mechanics.[24]

  • The original, abstract spectral theory of normal operators rose to central importance when, in the early development of quantum mechanics, the eigenvalues of Hermitian operators were detected experimentally in the optical spectra of energetic transitions of excited electrons

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Summary

INTRODUCTION

Decomposing a complicated system into its constituent parts—reductionism—is one of science’s most powerful strategies for analysis and understanding. We live in a complex, nonlinear world whose constituents are strongly interacting Often their key structures and memoryful behaviors emerge only over space and time. Perhaps surprisingly, many complex systems with nonlinear dynamics correspond to linear operators in abstract high-dimensional spaces.[2,3,4] And so, there is a sense in which even these complex systems can be reduced to the study of independent nonlocal collective modes. Reductionism, faces its own challenges even within its paradigmatic setting of linear systems: linear operators may have interdependent modes with irreducibly entwined behaviors. These irreducible components correspond to so-called nondiagonalizable subspaces. Though, begs the original question, What happens when reductionism fails? To answer this requires revisiting one of its more successful implementations, spectral decomposition of completely reducible operators

Spectral decomposition
Synopsis
SPECTRAL PRIMER
FUNCTIONAL CALCULI
Taylor series
Holomorphic functional calculus
Meromorphic functional calculus
MEROMORPHIC SPECTRAL DECOMPOSITION
Partial fraction expansion of the resolvent
Decomposing the identity
Evaluating the residues
Decomposing AL
Drazin inverse
Consequences and generalizations
CONSTRUCTING DECOMPOSITIONS
Projection operators of index-one eigenvalues
Normal matrices
Diagonalizable matrices
Any matrix
Simplified calculi for special cases
EXAMPLES AND APPLICATIONS
Spectra of stochastic transition operators
Randomness and memory in correlated processes
Poisson point processes
Homogeneous Poisson processes
Inhomogeneous Poisson processes
Stochastic thermodynamics
Dynamics in independent eigenspaces
Green–Kubo relations
Operators for chaotic dynamics
Ruelle–Frobenius–Perron and Koopman operators
Eigenvalues from a time series
CONCLUSION
Full Text
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