Digital Signal Processing Reference
In-Depth Information
since a k > 0 . The lower bound on the right-hand side can therefore be achieved
if we choose the optimal U f to be the unitary matrix that diagonalizes H H
completely and arranges diagonal elements as in Eq. (12.44). The proof of the
above lemma is therefore complete once we prove Eq. (12.45):
Proof of Eq. (12.45). The proof is based on a beautiful theorem on Hermi-
tian matrices which we first review (see Horn and Johnson [1985], p. 189).
Recall first that all eigenvalues of a Hermitian matrix are real. Let A be a
P
×
P Hermitian matrix and let A M be any M
×
M principal submatrix,
that is, a matrix obtained by deleting any P
M rows and the corresponding
columns from A . Let the eigenvalues of A be arranged as
λ 0 ( A ) ≥ λ 1 ( A ) ...≥ λ P− 1 ( A )
(12 . 47)
and let the eigenvalues of A M be arranged as
λ 0 ( A M ) ≥ λ 1 ( A M ) ...≥ λ M− 1 ( A M ) .
(12 . 48)
Then the eigenvalues λ k ( A M ) are interleaved between the eigenvalues λ k ( A )
as follows:
λ 0 ( A )
λ 0 ( A M )
λ L ( A ) ,
λ 1 ( A )
λ 1 ( A M )
λ L +1 ( A ) ,
.
λ M− 1 ( A )
λ M− 1 ( A M )
λ P− 1 ( A ) ,
where L = P
M . This is demonstrated in Fig. 12.4. Now consider any
unitary matrix U f which has performed the partial diagonalization (12.37).
The eigenvalues of the M
M leading principal submatrix of U f H HU f are
clearly μ k , whereas the eigenvalues of U f H HU f are just σ h,k (eigenvalues
of H H ) regardless of U f . The interlacing property
×
σ h,k
σ h,k + L ,
μ k
0
k
M
1 ,
(12 . 49)
therefore holds. This implies in particular that Eq. (12.45) is true.
12.4.3 Finding the optimal Σ f
With the optimal unitary U f shown to be the matrix that diagonalizes H H ,
we now turn to the computation of the optimal coe cients a k in Eq. (12.38)
which determine the diagonal elements of Σ f . For this, recall again the neces-
sary condition (12.32) for optimality derived earlier based on stationarity of the
Lagrangian. This condition is reproduced below:
( F H HF ) 1 = η F F ,
(12 . 50)
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