### Table 1: Theorem 1 The left null space of Gn is exactly d-dimensional.

1994

"... In PAGE 10: ... However, estimates for sn can still be obtained by solving ^sn = arg min sn2 d [^sn?N+m; ; ^sn?1; sn]Gn 2 F (9) This minimization is the basis of our recursive symbol estimation algorithm which is summarized in Table 1. A procedure for initializing the algorithm is not speci ed in Table1 . To be completely \blind quot; in the sense that both the channel and the symbol sequences are unknown, a block algorithm such... In PAGE 11: ... This delay is required for forming Gn = [Gn; ; Gn+Q?1]. Steps 1 and 3 in the algorithm (see Table1 ) call for shifts. This just amounts to throwing away old data and adding the new.... In PAGE 13: ...load has been reduced from O(Jd) to O(dJ). To exploit the source separation property in the RBSE algorithm in Table1 , simply replace the d-dimensional enumeration in Step 4 with d 1-dimensional searches in (10). Next we show that the enumeration in (10) is unnecessary in some cases.... ..."

Cited by 1

### Tables 1 and 2 show that the null-spaces computed from the T DULV closely approximate the null-spaces computed from the SV D.

### TABLE II COMMANDED VALUES FOR THE DIAGONAL CARTESIAN STIFFNESS MATRIX AND THE NULL-SPACE STIFFNESS.

2003

Cited by 2

### Table 1: Inversion precision where the number of samples of the null space is 256

2001

"... In PAGE 3: ... We can thus consider that this example gives a good idea about the precision. We present the results in Table1 and Table 2 for two different sampling steps: 1. the number of samples of the null space is BEBHBI 2.... ..."

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### Table VI. Left null space of the stoichiometric matrix, L.

2006

### Table 4.4 Model potential fluid flow problem on a rectangular domain with a constant tensor of hydraulic permeability. Number of nonzeros of the projected matrix onto the null-space basis Z of the block CT (see Algorithm 3.1, Step 3), iteration counts and timings of the preconditioned conjugate gradient method applied to the orthogonally projected indefinite system compared to the memory requirements and iteration counts for the solution of the same system based on the sparse QR decomposition of its off-diagonal block ZTB.

2001

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### Table 4.1: (a) Classi cation of zs 2 Xs R, dim N (Tx(zs)) 1, (b) Null space of G(zs)

### Table 4.3 Model potential fluid flow problem on a rectangular domain with a constant tensor of hydraulic permeability. Memory requirements (the number of nonzero entries NNZ(QR) and NNZ(Z1))of the approaches using the null-space basis of the whole block (B C)T , iteration counts and timings (in brackets for both approaches) of the conjugate gradient method applied to the projected positive definite system.

2001

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