International Journal of Scientific & Engineering Research, Volume 2, Issue 6, June-2011 1

ISSN 2229-5518

Physical Behavior of Eigenvalues and

Singular Values in Matrix Decompositions

Azmol Huda, R. L. Aguiar

Abstract— An apposite as well as realistic treatment of eigenvalue and singular value problems are potentially of interest to a wide variety of people, including among others, design engineers, theoretical physicists, classical applied mathematics and numerical analysis who yearn to carry out research in the matrix field. In real world, it is extensively used but at sometimes scantily understood. This paper focuses on building a solid perception of eigenvalue as well as singular value with their substantial meanings. The main goals of this paper are to present an intuitive experience of both eigenvalue and singular value in matrix decompositions throughout a discussion by largely building on ideas from linear algebra and will be proficiently to gain a better perceptive of their physical meanings from graphical representation.

Index Terms—Eigenvalue, singular value, matrix decomposition, orthonormal basis, linear mapping.

—————————— • ——————————


IGENVALUE as well as singular value has wide- spread application in diverse fields of empirical science from mathematics to neuroscience because
they are a straightforward, non-parametric value that extracts pertinent information from a large matrix. With nominal effort they make available a roadmap to divulge the hidden, simplified structures in a large matrix that frequently lie beneath it. Eigenvalues take part in a signif- icant role in situations where the matrix is a transforma- tion form one vector space onto itself. The primary para- digms of it are systems of linear ordinary differential eq- uations. The eigenvalues of a matrix be capable of keep- ing up a correspondence to frequencies of vibration, or critical values of stability factors or energy level of atoms. The most application of eigenvalue probably in the field of dimension reduction.
Singular values also play a vital role where the matrix
is a transformation from one vector space to a different
vector space, possibly with a dissimilar dimension. Sys-
tems of over or undetermined algebraic equations are the
most important examples. The term “singular value” re-
of the sample covariance matrix have a tendency to a lim- it under certain conditions and the limit of the cumulative distribution function of the eigenvalues is likely to deter- mine by using a technique of moments. In paper [10], C.B. Moler, presents a graphical depiction, but the singular value decomposition as well as the relation with the ei- genvalues is not discussed in a broader consideration. In this paper, we put in plain words an instictive feel of the physical meaning of eigenvalues and singular values along with emphasizing more on their graphical conse- quence with numerical example in a meticulous way.
The rest of this paper is structured as follows. The no-
tations used all the way throughout of this paper are giv-
en in section 2. We discuss the eigenvalue and singular
value matrix decompositions in section 3. Relation be-
tween eigenvalue and singular value and their geometric- al elucidation are given in section 4 and 5 respectively. Graphical representations are presented in section 6. Con- cluding remarks are presented in section 7.


lates to the distance between a matrix and the set of sin-
gular matrices. From the seminal study of eigen and sin- gular value, there has been a lot of research work related to this field [1], [2], [3], and incredibly common to all ma-

Symbol Meaning

A : square or rectangular matrix of order n over a field F ;
thematicians or engineers although nearby there is very


: eigenvalues of matrix A ;
little works straightforwardly in attendance to the rela- tion of eigenvalues and singular values simultaneously with their graphical properties, in an efficient and repre- sentative way. In paper [4], the author addresses a num- ber of numerical issues of singular value arising in the

A : n-by-n diagonal matrix with the 'A j on the

diagonal ;

X : denotes n-by-n diagonal matrix whose j-th

column is x j ;

U ,V : orthogonal or unitary matrices;

study of models of linear systems with its applications.

cr i

: singular values of A;
Numerical computation of the characteristic values of a real symmetric matrix and numerically stable, fairly fast

: n-by-n diagonal matrix with the cr r on the

technique for achieving the singular values are discussed A
in paper [5] and [6]. Authors in paper [7], describe the A
: transpose matrix of A;
: Hermitian matrix of A;
solution of large scale eigenvalue problems. In paper [8]
and [9], the authors illustrate that the largest eigenvalue

: set of real numbers;

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2 International Journal of Scientific & Engineering Research, Volume 2, Issue 6, June-2011

ISSN 2229-5518


1 I1

if i = j


ui = cr

Avi where ui .u j = �0 otherwise

3.1 Eigenvalue Decompositions

Let A be a square matrix of order n over a field F. A scalar

cr i

is related with the eigenvalues by the relation
'A E F is an eigenvalue of a matrix A if there exists a non- zero column vector x for which

cr i =


'Ai . This makes an unexpected property,

= cr i . A scalar cr E F is called a singular value of a

Ax = 'A x

rectangular matrix A , with a pair of singular vectors

The eigenvalue-eigenvector equation for a square ma- trix can be written as ( A - 'A I ) x = 0, x =1 0 where I denotes the identity matrix of order n. This implies that ( A - 'A I ) is

U and V if it satisfies the relation

AV = crU

This can be written as in matrix form AV = U



H where stands for the n-by-n diagonal matrix

singular and hence that det ( A - 'A I ) = 0 . This is christened

the characteristic equation or characteristic polynomial of A. The degree of polynomial is the order of the matrix and an n-by-n matrix has n eigenvalues, counting re-
with the cr r on the diagonal. The superscript H stands for Hermitian transpose. The mathematical intuition behind the construction of the matrix is that we craving to ex-
press all n scalar equations in just one equation. The ulti-
peated roots. Suppose

'A1, 'A2 ,..., 'An be the eigenvalues of a

mate form of SVD is thick and it is uncomplicated to
matrix A and

x1, x2 ,..., xn be a set of corresponding eigen-

comprehend this process in graphically. The complete
vectors. Let A denote the n-by-n diagonal matrix with the
'A j on the diagonal, and let X denotes the n-by-n matrix
whose j-th column is x j , then we can write
AX = X A (2) From equation (2), it is indispensable to put A on the right side so that each column of X can be multiplied by its resultant eigenvalues. A noteworthy key conjecture is that, this statement is not factual for all matrices. At this juncture, we assume that the eigenvectors are linearly independent, afterward the inverse of matrix X exists and
structures of singular value decomposition are described
in Figure 1.
Figure 1.(a) is the basic form of singular value decom-
position of a matrix, whereas, Figure 1.(b) and 1.(c) are
the form of singular value decomposition of the matrix when m < n and m > n respectively. It yields that singular vectors can constantly be chosen to be perpendicular to each other, so the matrices U and V, whose columns are the normalized singular vectors, satisfy

U H U = I and V HV = I . In other words, U and V are ortho- gonal, if they are real and unitary, if they are complex.

A = X AX -1

From equation (4), we can effortlessly derive
with the non singular matrix X. This is well-known as the

A = U V H

eigenvalue decomposition of matrix A. When it exists, it

consents us to investigate the properties and characteriza- tion of A by exploring the diagonal elements of A . For illustration, repeated matrix powers can be put across in terms of powers of scalars i.e.
with the diagonal matrix and orthogonal or unitary matrices U and V . This is well-known as the singular value decomposition or SVD, of matrix A.

Ak = X AK X -1

(4) n
But, if the eigenvectors of A are not linearly indepen- dent, then such a diagonal does not exist and the powers of A give us an idea or an evidence of a more complex behavior. If T is any non singular matrix, then A = TQT -1 is known as similarity transformation and A and Q are said to be similar. If Ax = 'A x and x = Ty then Qy = 'A y . So a simi- larity transformation preserves eigenvalues. Commonly, the eigenvalue decomposition is an attempt to find a simi-


m m num ber n

F igure. 1 (a) General form of SV D

A U ?


larity transformation to diagonal form.

(m ×n)

(m ×m )


3.2 Singular value Decompositions

Let A be a rectangular matrix of order m-by-n and the

Figure 1.(b) when m <n


rank of
AAT is r . Therefore,

AAT is a square symmetric

matrix of order m-by-m. Let us define some more quanti-

A U ?

ties: let

V = {v1 , v2 ,...vr } be the set of orthonormal

m x1 ei-


genvectors with associated eigenvalues {'A1, 'A2 ,...'Ar } for the

symmetric matrix AAT . Therefore, ( AAT )v = 'A v . Also let,

U = {u1 , u2 ,...ur } be the set of n x1 vectors defined by


(m ×m)

Figure 1.(c) when m >n

(m ×n)

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Fig.1. Structures of singular value decompositions.

International Journal of Scientific & Engineering Research, Volume 2, Issue 6, June-2011 3

ISSN 2229-5518


In our earlier section, we have established that A = U V H . Now we can obtain the relation between eigenvalues and singular values by a trouble-free computation.

decomposition is possible for any rectangular matrix. In SVD, we strive to find one change of basis in the domain and a typically different change of basis in the range with the intention that the matrix becomes diagonal. Such bases always exist and real if A is real. Usually, the transforming
matrices are orthogonal or unitary so as they preserve

AH A = (U V H )H (U V H ) = V

HU HU V H = V ( H

)V H

lengths and angles and do not magnify errors. The geome- trical outlook of the SVD decompositions can be summa-

AAH = (U V H ) (U V H )H = U V HV

HU H = U (

H )U H

rized as follows: for every linear map

Q : F n F m of the

The right hand side of these two equations expresses the
field F one can find orthonormal bases of F n and F m such
eigenvalue decompositions of the left hand sides. It is
that Q maps the i-th basis vector of

F n to a non-negative

evidently seen that, the squares of the non-zero singular
values of A are equal to the non-zero eigenvalues of their

AH A and AAH . Furthermore, the columns of U are eigen-

multiple of the i-th basis vector of F m . With respect to these bases, the map Q is therefore represented by a diagonal matrix with non-negative real diagonal entries. Finally, let
vectors of

of AH A .

AAH and the columns of V are eigenvectors
we look a more visual flavor of singular values and singu- lar value decompositions, at least when it works on a real vector space. If S is a sphere of radius one in n , after that


the linear map Q : F n F m

maps this sphere onto an ellip-


Let us take a closer look carefully of equation (1), Ax = 'A x and let us now ask whether there are any vectors which are not changed in direction by the deformation? The an- swer may be found easily from the equation (1). We could make some quick concluding remarks of equation (1). The square matrix A can be thought of as a transforma- tion matrix. The multiplication of the matrix A, on the left of a vector x, the answer is another vector that is trans- formed from its original position. The transformed vector x does not change its direction, only changes its magni- tude.
Moreover, it is effortlessly seen that, even if we scale

the vector x, by a few amount before we multiply it, we at a standstill get the same multiple of it as a result. This is for the reason that, if we scale by some amount, all we are doing is, building it longer, not change its direction. Per- haps the preeminent way to think of an eigenvector x of a matrix A is that it represents a direction which remains invariant under multiplication by A. The corresponding eigenvalues of A are then the representation of A in the subspace spanned by the eigenvector x. The benefit of having this representation is that multiplication by A is reduced to a scalar operation along the eigenvector. For instance, from the equation (4), Ak = X AK X -1 we can state that the effect of a power of A along x can be determined by taking the subsequent power of A. From the point of linear algebra, eigenvalues are pertinent of a square n-by- n matrix A which is reflection of as a mapping of n dime sional space onto itself. Here, we attempt to find a basis for the space so that the matrix is converted into diagonal. Even if A is real, this basis might be complex. In fact, if the eigenvectors are not linearly independent, such a ba- sis does not subsist.
On the contrary, SVD is relevant to a possibly rectangu-
lar m-by-n matrix A which is thought of as mapping n-
space onto m-space. Eigenvalue decomposition is applica- ble only for square matrix; in contrast, the singular value
soid in m and usually, nonzero singular values are simply
the lengths of the semi-axes of this ellipsoid.

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Fig. 2. Plot of eigenvalues.

4 International Journal of Scientific & Engineering Research, Volume 2, Issue 6, June-2011

ISSN 2229-5518

Fig. 3. Plot of singular values.


Here we demonstrate a graphical view of eigenvalues and singular values by Mat Lab with a numerical example. We use the transformation matrix

f 1 3 l

we have characterized and discussed the effect of the tra- verse orbit both of eigenvalues and singular values. It puts on display of a mapping from sphere onto an ellipsoid. When the vector x moves on a circle, Ax moves on an ellip- soid and the length of Ax is the eigenvalue. In singular val- ue mode, non-zero singular values are the simply the lengths of the semi axes of the ellipsoid.


[1] G.W. Stewart, “Matrix Algorithms: Basic Decompositions,” SIAM, Philadelphia, 1998.

[2] H. Anton, C. Rorres, “Elementary Linear Algebra with Applications,”

9-th Edition, John Wiley and Sons, 2005.

[3] V.C. Klema, “The Singular Value Decomposition: Its Computation and

Some Applications,” IEEE Trans. Automatic Control, Vol. 25, pp.164-

176, 1980.

[4] W. Givens, “Numerical Computation of the Characteristic Values of a

Real Symmetric Matrix,” ORNL Rep. 1574.

[5] G.H. Golub and W. Kahan, “Calculating the Singular Values and Pseu-

do-Inverse of a Matrix,” SIAMJ. Nwner. Anal., vol. 2, pp. 205-224, 1965.

A =

4 4

[6] R.B. Lehoucq, D.C. Sorensen and C. Yang, “ARPACK User’s Guide:

1 1 L 2J

of order 2 and a unit vector x = [1 0]' to facilitate of draw- ing a unit circle.

The resulting trajectory of Ax is plotted. In Figure-2,
the first four subplots, Figure-2.(a), 2.(b), 2.(c) and 2.(d),
are the intermediate steps of their traversed orbits. The
goal is to find the vectors x so that Ax is parallel to x.
Generally, for such a direction x, the transformation ma- trix A is simply a stretching or shrinking by a factor of 'A . Each such x is an eigenvector and the length of Ax corres- ponding eigenvalue. From the last two subplots Figure-
2.(e), 2.(d) of Figure-2 , the first eigenvalue is positive, so Ax lies on top of the eigenvector x and the second eigen- value is negative and Ax is parallel to x but points in the opposite direction. We have plotted the SVD orbit in Fig- ure-3 with subplots 3.(a) and 3.(b) respectively. The vec- tors x and y are perpendicular each other and the resulted Ax and Ay are plotted. In SVD mode, the axes of the el- lipse do play a key role. The resulting Ax and Ay traverse on the ellipse, but are not perpendicular to each other though x and y are perpendicular. When Ax and Ay are perpendicular, they form the axes of the ellipse. In this case, x and y are right singular vectors as well as the col- umns of U in the SVD, the vectors Ax and Ay are mul- tiples of the columns of V and the lengths of the axes of ellipse are the singular values.


In this paper, we have discussed several aspects of eigen- values and singular values from the point of view of their underlying relations. To catch a better understanding, a pictorial visualization and elucidation, are presented. Here,

Solution of Large-Scale Eigenvalue Problems with Implicitly Restarted

Arnoldi Menhods,” SIAM, Philadelphia, 1998.

[7] D. Jonsson, “Some Limit Theorems for the Eigenvalues of a Sample

Covariance Matrix,” J. Multivariate Analysis 12, pp.1-38, 1982.

[8] Y.Q. Yin, Z.D. Bai and P.R. Krishnajah, “On the Limit of the Largest

Eigenvalue of the Large Dimension Sample Covariance Ma-

trix,”Probability Theory and Related Fields, Volume 78, Number 4, 509-

521, 1998.

[9] C.B. Moler, “Numerical Computing with MATLAB,” February 15,


[10] Y. Jiao, B. Yang, H. Wang, X. Niu, “SVD Based Robust Image Content Retrieval,” International Conference on Intelligent Information Hiding and Multimedia Signal Processing, pp. 351-354, Dec. 2006.

[11] Y.H. Wang, T.N. Tan and Y. Zhu, “Face Verification Based on Singular Value Decomposition and Radial Basis Function Neural Network,” (NLPR), Institute of Automation, Chinese Academy of Sciences, 2005.

[12] C.L., Sun, J. Hahn, “Parameter Reduction for Stable Dynamical Sys-

tems Based on Hankel Singular Values and Sensitivity Analysis,”

Chem. Eng.Sci., 61(16), pp. 5393–5403, 2006.

[13] S. Lall, J.E. Marsden, S. Glavaski, “Empirical Model Reduction of Con-

trolled Nonlinear Systems,” 14th IFAC World Congress, Beijing, 1999. [14] C. Sun, J. Hahn, “Reduction of Differential-Algebraic Equation Systems

Via Projections and System Identification,” Journal of Process Control, vol. 15, pp. 639-650, 2005.

[15] G.H. Golub and C. Reinsch, “Singualr Value Decomposition and Least

Squares Solutions,” Numer. Math., vol. 14, pp. 403-420, 1970.

[16] M.H.A. Biswas, M.H. Azmol, Munnujahan Ara, M.A. Rahman, “Op-

timal Control Theory and its Applications in Aerospace Engineering,” International Journal on Academic Research, Vol. 3, No. 2, March 2011.

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