Generalized eigenvalue problem of matrix pair is an active research task 矩阵广义特征值问题是当前迅速发展的计算机科学和数值代数中的一个非常活跃的研究课题。
A posteriori error estimation based on stress super - convergence recovery technique for generalized eigenvalue problems 基于应力超收敛恢复技术的广义特征值问题后验误差估计
This paper studies inverse design problem of generalized eigenvalue problem of linear parameter discrete vibration system 本文研究了具有线性参数的离散振动系统广义特征值逆设计问题。
This thesis investigates parallel solving the generalized eigenvalue problem ax - bx deeply , and proposes some new algorithms 本文从理论和实验两方面深入研究了分布式环境下实矩阵广义特征值问题ax = bx的并行计算,提出了一些新的算法。
Massively parallel processing system ( mpp ) and pc cluster provide distributed - memory environments for parallel solving the generalized eigenvalue problem 大规模并行处理系统( mpp )和pc机群为并行求解矩阵广义特征值问题提供了分布式存储环境。
On these grounds , the sensitivity of semisimple multiple eigenvalues of generalized eigenvalue problems is defined , and the sensitive elements of matrix pairs can be determined 以所得结论为基础,定义了广义特征值问题半单重特征值的灵敏度,给出了确定矩阵对中敏感元素的方法。
Then use the finite element method to analyse the dielectric loaded resonant cavity , and get the generalized eigenvalue equation ax = k02bx , in matrix b includes the unknown r 然后采用有限元分析的方法来分析介质加载谐振腔,并得到待求广义特征方程ax = k02bx ,其中b中含有未知数r 。
Moreover , the corresponding online optimization problem is converted to the generalized eigenvalue problem in terms of lmis . and the robust feasibility and robust stability are converted to the feasibility of a set of lmis ; 2 同时,在线优化问题被转化为基于线性矩阵不等式的广义特征值问题,而其鲁棒可行性问题及鲁棒稳定性问题则被转化为线性矩阵不等式之可行解的存在性问题; 2
( 2 ) the symmetric - definite band generalized eigenvalue problems i ) a parallel divide - and - conquer algorithm combined with multisection method is proposed , the sum of the subproblem ' s scales is equal to the original problem ' s scale ( 2 )关于实对称带状矩阵广义特征值问题? )提出了一种结合多分法的并行分治算法,给出了特征值分割定理及其证明。在该算法中,子问题的规模之和等于原问题的规模。
Based on the theory of reyleigh minimum , the minimum of energy function of neural network was mapped to the eigenvector that was mapped to the minimal eigenvalue of the generalized eigenvalue problem , by which the precise solution of minimal eigenvalue was gained while the neural network moving to the minimum of energy function 本文应用reyleigh极小值原理,将神经网络的能量函数的极小点对应于广义特征值问题的极小特征值所对应的特征向量,在神经网络向着能量函数极小点运动的同时得到了极小特征向量的精确解答。