Acta Numerica



Structured inverse eigenvalue problems


Moody T. Chu a1 1 and Gene H. Golub a2 2
a1 Department of Mathematics, North Carolina State University, Raleigh, North Carolina, NC 27695-8205, USA. E-mail: chu@math.ncsu.edu
a2 Department of Computer Science, Stanford University, Stanford, California, CA 94305-9025, USA. E-mail: golub@stanford.edu

Abstract

An inverse eigenvalue problem concerns the reconstruction of a structured matrix from prescribed spectral data. Such an inverse problem arises in many applications where parameters of a certain physical system are to be determined from the knowledge or expectation of its dynamical behaviour. Spectral information is entailed because the dynamical behaviour is often governed by the underlying natural frequencies and normal modes. Structural stipulation is designated because the physical system is often subject to some feasibility constraints. The spectral data involved may consist of complete or only partial information on eigenvalues or eigenvectors. The structure embodied by the matrices can take many forms. The objective of an inverse eigenvalue problem is to construct a matrix that maintains both the specific structure as well as the given spectral property. In this expository paper the emphasis is to provide an overview of the vast scope of this intriguing problem, treating some of its many applications, its mathematical properties, and a variety of numerical techniques.



Footnotes

1 This research was supported in part by NSF grants DMS-9803759 and DMS-0073056.

2 This work was supported in part by the NSF grant CCR-9971010.