For a Hamiltonian matrix with purely imaginary eigenvalues, we aim to determine the nearest Hamiltonian matrix such that some or all eigenvalues leave the imaginary axis. Conversely, for a Hamiltonian matrix with all eigenvalues lying off the imaginary axis, we look for a nearest Hamiltonian matrix that has a pair of imaginary eigenvalues. The Hamiltonian matrices can be allowed to be complex or restricted to be real. Such Hamiltonian matrix nearness problems are motivated by applications such as the analysis of passive control systems. They are closely related to the problem of determining extremal points of Hamiltonian pseudospectra. We obtain a characterization of optimal perturbations, which turn out to be of low rank and are attractive stationary points of low-rank differential equations that we derive. We use a two-level approach, where in the inner level we determine extremal points of the Hamiltonian $\eps$-pseudospectrum for a given $\eps$ by following the low-rank differential equations into a stationary point, and on the outer level we optimize for~$\eps$. This permits us to give fast algorithms - exhibiting quadratic convergence - for solving the considered Hamiltonian matrix nearness problems.

Low rank ODEs for Hamiltonian matrix nearness problems

GUGLIELMI, NICOLA;
2015

Abstract

For a Hamiltonian matrix with purely imaginary eigenvalues, we aim to determine the nearest Hamiltonian matrix such that some or all eigenvalues leave the imaginary axis. Conversely, for a Hamiltonian matrix with all eigenvalues lying off the imaginary axis, we look for a nearest Hamiltonian matrix that has a pair of imaginary eigenvalues. The Hamiltonian matrices can be allowed to be complex or restricted to be real. Such Hamiltonian matrix nearness problems are motivated by applications such as the analysis of passive control systems. They are closely related to the problem of determining extremal points of Hamiltonian pseudospectra. We obtain a characterization of optimal perturbations, which turn out to be of low rank and are attractive stationary points of low-rank differential equations that we derive. We use a two-level approach, where in the inner level we determine extremal points of the Hamiltonian $\eps$-pseudospectrum for a given $\eps$ by following the low-rank differential equations into a stationary point, and on the outer level we optimize for~$\eps$. This permits us to give fast algorithms - exhibiting quadratic convergence - for solving the considered Hamiltonian matrix nearness problems.
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11697/16499
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