### Table 4: Performance of Hermitian routine

"... In PAGE 7: ... A more useful approach is to rst reduce the Hermitian matrix A to complex tridiagonal form using Householder transformations H = P HAP and then this complex matrix H is reduced to a real symmetric tridiagonal matrix using unitary transformations T = V HHV where P and V are the transformation matrices. Some indicative performance gures are given in Table4 . All eigenvalues and eigenvectors were found and the routine EVP based on reduction to tridiagonal was used as the underlying real eigensolver.... ..."

### TABLE I KRYLOV SUBSPACE BASED ALGORITHM FOR AN HERMITIAN MATRIX.

in Low-Complexity MIMO Multi-User Receiver: A Joint Antenna Detection Scheme for Time-Varying Channels

### Table 1. The Zoo of Zetas - A New Column for Table 2 in Katz and Sarnak [45].

"... In PAGE 27: ... It would also be nice to know if there is a functional equation for the Ihara zeta of an irregular graph. Table1 below is a zoo of zetas, comparing three types of zeta functions: number field zetas (or Dedekind zetas), zetas for function fields over finite fields, and finally the Ihara zeta function of a graph. Thus it adds a new column to Table 2 in Katz and Sarnak [45].... In PAGE 27: ... Thus it adds a new column to Table 2 in Katz and Sarnak [45]. In Table1 it is assumed that our graphs are finite, connected and regular. Here GUE means that the spacing between pairs of zeros/poles is that of the eigenvalues of a random Hermitian matrix.... ..."

### Table 5.3: Clustering with the use of descriptors based on hermitian adjacency matrix

2007

### Table[randomhermitianmatrix[d,limit]//MatrixForm,CUnCV] In[122]:= (* * * AYnumberofeigenvalues[a,lambda]AY is the number of eigenvalues of the hermitian matrix a lower than AL * **)

2007

### Table 2. Complex Hermitian Toeplitz-plus-Hankel A. The table suggests that accuracy of the symmetric pivoting on the Cauchy matrix C is comparable to that of unsymmetric pivoting. The accuracy of the decompo- sition of A is slightly lower that that of C. This is caused by the transformation of A into C, and possibly could be improved by a more careful implementation of

1998

### TABLE V TRAFFIC MATRIX FOR THE RANDOM TRAFFIC PATTERN

### Table 2: Entrywise and columnwise RMS error in ^ A in a 4 3 example. JADE algorithm with T = 700 samples and binary sources. Appendix A: A joint diagonalization algorithm. The Jacobi technique [29] for diagonalizing a unique hermitian matrix is extended for the joint approximate diagonalization of a set N = fNrj1 r sg of arbitrary n n matrices. It consists in minimizingthe diagonalization criterion (19) by successive Givens rotations. We start by describing the 2 2 case and we denote

1993

Cited by 289

### Table 15. Summary of results for AC1331 (cf. x4.7). 4.8 Harmonic Ritz values for generalized problems. Our last example shows again that for interior generalized eigenvalues the harmonic version JDQZ is superior to the adaptive version.We consider the MHD416 generalized eigenproblem of order 416 [2], [18], [3]. This problem stems from a magnetohydrodynamics (MHD) model, where the interaction of hot plasma and a magnetic eld is studied. The matrix A is non-Hermitian and the matrix B is Hermitian positive de nite. Our goal is to compute interior generalized eigenvalues corresponding to the so called \Alfv en quot; branch of the spectrum, see Fig. 14 and 15.

1999

Cited by 16