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Modal analysis is a widely applied method to study the vibration phenomenon of continuum structures, but there is no clear method to solve the modal truncation problem at present. To determine the contribution of different modes to the whole system, a new mode truncation method based on perturbation theory is proposed in this paper. The modes are subjected to perturbation parameters during discretization, and using norm error analysis on the stiffness matrix in different degrees of freedom (DOFs) systems confirms the model number of the continuum structure system. The results show that the DOF identified by the modal perturbation method is related to the perturbation parameter, and the smaller the perturbation parameter is, the fewer modes need to be considered. When the perturbation parameter is large enough, the response of the system can only be accurately explained by truncation to higher-order modes. Finally, the perturbation parameter is fixed to 1, and the traditional Galerkin method is connected to the modal perturbation, making traditional discretization a unique case for the modal perturbation method. This method can significantly reduce the modal truncation error, which is of great significance to the dynamic analysis of engineering applications. [ABSTRACT FROM AUTHOR]

We define row path norm and column path norm of a matrix and relate path norms with other standard matrix norms. A row (resp. column) path norm gives a path that maximizes relative row (resp. column) distances starting from the first row (resp. column). The comparison takes place from the last row (resp. column) to the first row (resp. column), tracing the path. We categorize different versions of path norms and provide algorithms to compute them. We show that brute-force methods to compute path norms have exponential running time. We give dynamic programming algorithms, which, in contrast, take quadratic running time for computing the path norms. We define path norms on Church numerals and Church pairs. Finally, we present applications of path norms in computing condition number, and ordering the solutions of magic squares and Latin squares [ABSTRACT FROM AUTHOR]

We study approximate K-optimal designs for various regression models by minimizing the condition number of the information matrix. This minimizes the error sensitivity in the computation of the least squares estimator of regression parameters and also avoids the multicollinearity in regression. Using matrix and optimization theory, we derive several theoretical results of K-optimal designs, including convexity of K-optimality criterion, lower bounds of the condition number, and symmetry properties of K-optimal designs. A general numerical method is developed to find K-optimal designs for any regression model on a discrete design space. In addition, specific results are obtained for polynomial, trigonometric and second-order response models. [ABSTRACT FROM AUTHOR]