### Table 3: H2-matrix approximation with adaptive cluster bases

2003

"... In PAGE 19: ... We use a constant approximation order of m = 4 and use the algorithm from Section 5 in order to compute an orthogonalized basis. Table3 reports the results of the experiment3: The time required for building the H2-matrix approximation is roughly linear in the dimension of the discrete space, as is the time required for performing the matrix-vector multiplication. The same holds for the memory requirements: The ratio of needed storage and number of degrees of freedom is bounded.... ..."

### Table 1 Performance of the TRL eigenvalue calculations using hierarchical matrix approximation for fast matrix-vector products

2005

### Table III. JD correction step. The matrix K is an approximate inverse preconditioning matrix.

### Table 1: H2-matrix constant-order approximation

2003

"... In PAGE 17: ... We have discretized the integral operator by the Galerkin method based on piecewise constant basis functions. Table1 gives the dimension of the discrete space, the time in seconds2 for building the H2-matrix approximation, the time in seconds for performing one matrix-vector multiplication, the relative approximation error in the spectral norm and the memory requirements per degree of freedom. We can see that the relative error in the Euclidean norm is almost constant and that both the runtime and the storage requirements grow linearly in the number of degrees of freedom.... ..."

### Table 2 below shows the new probability transition matrix for the auction, substituting the approximation F*(p) for

1999

"... In PAGE 3: ............................. 12 Table2 Approximate transition probabilities for the auction model .... ..."

Cited by 2

### Table 3: Corresponding left-hand Matrix Pad e approximants, M 6, N 6

2004

### Table 10.3 Approximation of the element stiffness matrix by a diagonally dominant matrix.

2004

Cited by 10

### Table 1: Approximation capability matrix among neural and fuzzy paradigms. The table states

1996

"... In PAGE 12: ...aradigm, as shown in sect. 3.1. Table1 lists approximation capabilities of neural and fuzzy paradigms; proofs are given below in the form of theorems, for several cases. 4.... ..."

Cited by 7

### Table 5. Exact and Approximate matrix inversion for PCMLLR on AURORA 2 test set A, averaged across N1-N4, WER(%).

"... In PAGE 5: ... In particular for the PST systems where the statistics must be accumulated, as well as the transform applied. Table5 shows the performance using the ma- trix inversion approximation from section 4. For the PST system (10 iterations) the effect of the approximation is very small, similarly for the PCMLLR system.... ..."