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5.3: Perturabtions Measured in the Frobenius Norm

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We will now demonstrate that, for the multiplicative and additive perturbation cases where we minimized the induced 2-norm, we also minimized the Frobenius norm.

Let ACm×n, and let rank(A)=r.

AF(nj=1mi=1|aij|2)12 

=(trace(AA))12 

=(Σri=1σ2i)12(the trace of a matrix is the sum of its eigenvalues) 

σ1(A) 

Therefore,

AFA2 

which is a useful inequality.

In both the perturbation problems that we considered earlier, we found a rank-one solution, or dyad, for Δ:

Δ=αuv 

where αC, uCn, vCn such that u2=v2=1. It is easy to show that the Frobenius norm and induced 2-norm are equal for rank one matrices of the form in Equation (5.13). It follows from this that the Δ which minimizes the induced 2-norm also minimizes the Frobenius norm, for the additive and multiplicative perturbation cases we have examined. In general, however, minimizing the induced 2-norm of a matrix does not imply the Frobenius norm is minimized (or vice versa.)

Example 5.1

This example is intended to illustrate the use of the singular value decomposition and Frobenius norms in the solution of a minimum distance problem.

Solution

Suppose we have a matrix ACn×n, and we are interested in finding the closest matrix to A of the form cW where c is a complex number and W is a unitary matrix. The distance is to be measured by the Frobenius norm. This problem can be formulated as

min

where W^{\prime}W = I. We can write

\begin{aligned} \|A-c W\|_{F}^{2} &=\operatorname{Tr}\left((A-c W)^{\prime}(A-c W)\right) \\ &=\operatorname{Tr}\left(A^{\prime} A\right)-c^{\prime} \operatorname{Tr}\left(W^{\prime} A\right)-c \operatorname{Tr}\left(A^{\prime} W\right)+|c|^{2} \operatorname{Tr}\left(W^{\prime} W\right) \end{aligned}\nonumber

Note that \operatorname{Tr}(W^{\prime}W) = \operatorname{Tr}(I) = n. Therefore, we have

\|A-c W\|_{F}^{2}=\|A\|_{F}^{2}-2 \operatorname{Re}\left(c^{\prime} \operatorname{Tr}\left(W^{\prime} A\right)\right)+n|c|^{2} \ \tag{5.14}

and by taking

c=\frac{1}{n} \operatorname{Tr}\left(W^{\prime} A\right)\nonumber

the right hand side of Equation (5.14) will be minimized. Therefore we have that

\|A-c W\|_{F}^{2} \geq\|A\|_{F}^{2}-\frac{1}{n}\left|\operatorname{Tr}\left(W^{\prime} A\right)\right|^{2}\nonumber

Now we must minimize the right hand side with respect to W, which is equivalent to maximizing \left|\operatorname{Tr}\left(W^{\prime} A\right)\right|. In order to achieve this we employ the singular value decomposition of A as U \Sigma V^{\prime}, which gives

\begin{aligned} \left|\operatorname{Tr}\left(W^{\prime} A\right)\right|^{2} &=\left|\operatorname{Tr}\left(W^{\prime} U \Sigma V^{\prime}\right)\right|^{2} \\ &=\left|\operatorname{Tr}\left(V^{\prime} W^{\prime} U \Sigma\right)\right|^{2} \end{aligned}\nonumber

The matrix Z = V^{\prime} W^{\prime} U satisfies

\begin{aligned} Z Z^{\prime} &=V^{\prime} W^{\prime} U U^{\prime} W V \\ &=I \end{aligned}\nonumber

Therefore,

|\operatorname{Tr}(Z \Sigma)|^{2}=\left|\sum_{i=1}^{n} \sigma_{i} z_{i i}\right|^{2} \leq\left(\sum_{i=1}^{n} \sigma_{i}\right)^{2}\nonumber

implies that

\min _{c, W}\|A-c W\|_{F}^{2} \geq\|A\|_{F}^{2}-\frac{1}{n}\left(\sum_{i=1}^{n} \sigma_{i}\right)^{2} \ \tag{5.15}

In order to complete this example we show that the lower bound in Equation (5.15) can actually be achieved with a specific choice of W. Observe that

\operatorname{Tr}\left(W^{\prime} U \Sigma V^{\prime}\right)=\operatorname{Tr}\left(W^{\prime} U V^{\prime} \Sigma\right)\nonumber

and by letting W^{\prime}= V U^{\prime} we obtain

\operatorname{Tr}\left(W^{\prime} A\right)=\operatorname{Tr}(\Sigma)=\sum_{i=1}^{n} \sigma_{i}\nonumber

and

c=\frac{1}{n} \sum_{i=1}^{n} \sigma_{i}\nonumber

Putting all the pieces together, we get that

\min _{c, W}\|A-c W\|_{F}^{2}=\sum_{i=1}^{n} \sigma_{i}^{2}-\frac{1}{n}\left(\sum_{i=1}^{n} \sigma_{i}^{2}\right)^{2}\nonumber

and the minimizing unitary matrix is given by

c W=\frac{1}{n}\left(\sum_{i=1}^{n} \sigma_{i}\right) U V^{\prime}\nonumber

It is clear also that, in order for a matrix to be exactly represented as a complex multiple of a unitary matrix, all of its singular values must be equal.


This page titled 5.3: Perturabtions Measured in the Frobenius Norm is shared under a CC BY-NC-SA 4.0 license and was authored, remixed, and/or curated by Mohammed Dahleh, Munther A. Dahleh, and George Verghese (MIT OpenCourseWare) via source content that was edited to the style and standards of the LibreTexts platform.

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