Sentence examples for matrices in practice from inspiring English sources

However, due to estimation error and feedback delay, there is usually certain mismatch between the estimated and the real channel matrices in practice.

Since we can never know exactly the true covariance matrix in practice, the SINR loss curve training over 64 samples with high homogeneity was superimposed here as an approximation of the real case.

If Γ ≠ ∅, for ∀ x ∗ ∈ Γ and n ≥ 0, such that D n ∇ f g ( x ∗ ) = D n A T A g ( x ∗ ) − D n A T P Q n A g ( x ∗ ) = 0, under the ideal condition, if D n A T A ≈ I, the solution is done, but unfortunately, ( A T A ) − 1 cannot be calculated directly when A is a large matrix in practice.

Note that C p B does not have to be stored as such during the calculation, since (H C p B ) † and H C p B H † are the sole matrices needed in practice (see below).

Inevitable noises and limited computational time are major issues for time-variant matrix inversion in practice.

Although M s is positive definite in theory, the matrix encountered in practice is highly singular due to the fact that rotational masses are omitted.

Since the operation objects are matrices and vectors in practice, the orthocomplement designing problem turns out to be orthogonal vector designing.

The dimensions of these covariance matrices are huge in practice (in the order of thousands), so a brute force calculation would be expensive.

Otherwise, it is advisable to clean matrix A from those undesirable features before applying any matrix updating method in practice.

Note that r and n ∈ ℝ 2 M 2 × 1 and A ∈ ℝ 2 M 2 × G. Unfortunately, the spatial covariance matrix is unknown in practice and is normally replaced by the sample covariance matrix obtained from a set of N observations R ^ = 1 N ∑ k = 1 N y [ k ] y H [ k ].

These approaches are based on the nuclear norm of the matrix, and thus in practice the rank may not be well approximated.