title: Parameter Reconstruction of Vibration Systems from Partial Eigeninformation.
abstract: Quadratic matrix polynomials are fundamental to vibration analysis. Because of the predetermined interconnectivity among the constituent elements and the mandatory nonnegativity of the physical parameters, most given vibration systems will impose some inherent structure on the coefficients of the corresponding quadratic matrix polynomials. In the inverse problem of reconstructing a vibration system from its observed or desirable dynamical behavior, respecting the intrinsic structure becomes important and challenging both theoretically and practically. The issue of whether a structured inverse eigenvalue problem is solvable is problem dependent and has to be addressed structure by structure. In an earlier work, physical systems that can be modeled under the paradigm of a serially linked mass-spring system have been considered via specifically formulated inequality systems. In this paper, the framework is generalized to arbitrary generally linked systems. In particular, given any configuration of interconnectivity in a mass-spring system, this paper presents a mechanism that systematically and automatically generates a corresponding inequality system. A numerical approach is proposed to determine whether the inverse problem is solvable and, if it is so, computes the coefficient matrices while providing an estimate of the residual error. The most important feature of this approach is that it is problem independent, that is, the approach is general and robust for any kind of physical configuration. The ideas discussed in this paper have been implemented into a software package by which some numerical experiments are reported.
authors: Department of Applied Mathematics, Dalian University of Technology, Dalian, Liaoning, 116024.
authors: Bo
authors: B
authors: Dong
authors: Matthew M
authors: MM
authors: Lin
authors: Moody T
authors: MT
authors: Chu
keywords: connectivity; inverse eigenvalue problem; linear inequality system; low rank approximation; maximin problem; nonnegativity; quadratic matrix polynomial; quadratic programming