by

Xiamen University

Xiamen 561005, China

Email: zjbai@xmu.edu.cn

Abstract : The inverse quadratic
eigenvalue problem (IQEP) arises in the field of structural mechanics
and vibrating structure. It aims to find three matrices, known as the
mass, the damping, and the stiffness matrices, respectively such that
they satisfy the measured data and preserve the exploitable structural
properties such as symmetry, definiteness, sparsity and bandedness,
etc., of the original model. The difficulty of this problem lies in the
fact that in applications the mass, damping, and stiffness matrices
should satisfy the requirement(s) of definiteness and/or bandedness.

In this talk, we first consider the IQEP where the mass matrix should be positive definite, the damping matrix symmetric, and the stiffness matrix positive semidefinite. Based on an equivalent dual optimization version of the IQEP, we present a quadratically convergent Newton-type method. Our numerical experiments confirm the high efficiency of the proposed method.

Next, we discuss the inverse problem for a discrete damped mass-spring system where the mass, damping, and stiffness matrices are all symmetric and tridiagonal. It is shown that the model can be constructed from two real eigenvalues and three real eigenvectors or a complex conjugate eigenpair and a real eigenvector. However, for large model order, the construction from these data may be sensitive to perturbations. To reduce the sensitivity, we fit an least-squares optimization problem to the overdetermined noised data. In addition, The physical realizability of the required model is obtained by solving a positiveness-constrained least-squares optimization problem to the overdetermined corrupted eigendata.

In this talk, we first consider the IQEP where the mass matrix should be positive definite, the damping matrix symmetric, and the stiffness matrix positive semidefinite. Based on an equivalent dual optimization version of the IQEP, we present a quadratically convergent Newton-type method. Our numerical experiments confirm the high efficiency of the proposed method.

Next, we discuss the inverse problem for a discrete damped mass-spring system where the mass, damping, and stiffness matrices are all symmetric and tridiagonal. It is shown that the model can be constructed from two real eigenvalues and three real eigenvectors or a complex conjugate eigenpair and a real eigenvector. However, for large model order, the construction from these data may be sensitive to perturbations. To reduce the sensitivity, we fit an least-squares optimization problem to the overdetermined noised data. In addition, The physical realizability of the required model is obtained by solving a positiveness-constrained least-squares optimization problem to the overdetermined corrupted eigendata.