EigenvaluePositive definite matrixSymmetric matrixThe following matrix equations: A(T)XB + B(T)X(T)A = C and A(T)XB + B(T)XA = C, are encountered in many systems and control applications, and these matrix equations contain several linear matrix equations as special cases. In the ...
Inequalitiesfor EigenvalueandSingularValueofProductofMatricesoverQuaternion 四元数矩阵积的特征值与奇异值的不等式 scholar.ilib.cn 9. Discussion on theAdditionalValueofProductPackagingDesign 产品包装设计中的附加价值 www.ilib.cn 10. ComparativeStudyonValueofProductDevelopment ...
Based on these theories, we propose a simple Eigenvalue Allocation method to effectively optimize the space decomposition. We demonstrate by experiments the superiority of our methods in three applications: • When used for compact encoding for exhaustive search, our method outperforms several ...
the formxxHxxH. To minimize the difference||x1xH1−B|||x1x1H−B||,x1=λ−−√ux1=λu, whereλλis the largest eigenvalue ofBBanduuis the associated eigenvector. Since this must be done every iteration of the optimization process, it becomes very expensive for large values ofNN...
2. After the graph has been launched in a session, the value of the `Tensor` can be computed by passing it to `tf.Session.run`. `t.eval()` is a shortcut for calling `tf.compat.v1.get_default_session().run(t)`. In the following example, `c`, `d`, and `e` are symbolic ...
2) Eigenvalue inequality 特征值不等式 1. Some results of Lowner partial ordering and eigenvalue inequality are given to the generalized Schur complement for positive semidefinite matrix power products. 对半正定矩阵的幂给出广义Schur补的一些Lowner偏序和特征值不等式。
Eigenvalue Inequalities for Matrix Product 来自 Semantic Scholar 喜欢 0 阅读量: 101 作者:Zhang, F,Zhang, Q 摘要: We present a family of eigenvalue inequalities for the product of a Hermitian matrix and a positive-semidefinite matrix. Our theorem contains or extends some existing results on ...
at a proposed value \((\varvec{\tau }^2)^*\), which generally has a high computational burden: The most obvious approach to compute the pseudo-determinant (10) is to perform an eigendecomposition of the \(D\times D\) penalty matrix \(\varvec{K}((\varvec{\tau }^2)^*)\). How...
In this work, we tackle the problem of estimating the density $$ f_X $$ of a random variable $$ X $$ by successive smoothing, such that the smoothed random
These new lower bounds generalize the existing results. To validate the accuracy of our findings, we present examples in which our results outperform previous ones in certain cases.关键词: M-matrix Fan product minimum eigenvalue irreducible