** Advances in Pure Mathematics** Vol.3 No.9B(2013), Article ID:41122,17 pages DOI:10.4236/apm.2013.39A2002

From Eigenvalues to Singular Values: A Review

Hydrological Service, Jerusalem, Israel

Email: dax20@water.gov.il

Copyright © 2013 Achiya Dax. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Received August 15, 2013; revised September 15, 2013; accepted September 21, 2013

**Keywords:** Eigenvalues; Singular Values; Rayleigh Quotient; Orthogonal Quotient Matrices; The Orthogonal Quotients Equality; Eckart-Young Theorem; Ky Fan’s Extremum Principles

ABSTRACT

The analogy between eigenvalues and singular values has many faces. The current review brings together several examples of this analogy. One example regards the similarity between Symmetric Rayleigh Quotients and Rectangular Rayleigh Quotients. Many useful properties of eigenvalues stem are from the Courant-Fischer minimax theorem, from Weyl’s theorem, and their corollaries. Another aspect regards “rectangular” versions of these theorems. Comparing the properties of Rayleigh Quotient matrices with those of Orthogonal Quotient matrices illuminates the subject in a new light. The Orthogonal Quotients Equality is a recent result that converts Eckart-Young’s minimum norm problem into an equivalent maximum norm problem. This exposes a surprising link between the Eckart-Young theorem and Ky Fan’s maximum principle. We see that the two theorems reflect two sides of the same coin: there exists a more general maximum principle from which both theorems are easily derived. Ky Fan has used his extremum principle (on traces of matrices) to derive analog results on determinants of positive definite Rayleigh Quotients matrices. The new extremum principle extends these results to Rectangular Quotients matrices. Bringing all these topics under one roof provides new insight into the fascinating relations between eigenvalues and singular values.

1. Introduction

Let be a real symmetric matrix and let

be a given nonzero vector. Then the well-known Rayleigh Quotient is defined as

(1.1)

One motivation behind this definition lies in the following observation. Let be a given real number. Then there exists an eigenvalue of such that

(1.2)

and the value of that solves the minimum norm problem

(1.3)

is given by. In other words, provides an estimate for an eigenvalue corresponding to. Combining this estimate with the inverse iteration yields the celebrated Rayleigh Quotient Iteration. Other related features are the Courant-Fischer minimax inequalities, Weyl monotonicity theorem, and many other results that stem from these observations. In particular, the largest and the smallest eigenvalues of satisfy

(1.4)

and

(1.5)

respectively. For detailed discussion of the Rayleigh Quotient and its properties see, for example, [1-41].

The question that initiates our study is how to extend the definition of Rayleigh Quotient in order to estimate a singular value of a general rectangular matrix, where the term “rectangular” means that the matrix is not necessarily symmetric or square. More precisely, let be a real matrix, , and let and be a pair of nonzero vectors. Then we seek a scalar function of, and, say, whose value approximates the “corresponding” singular value of. The answer is given by the Rectangular Quotient,

(1.6)

where denotes the Euclidean vector norm. The justifications behind this definition are given in Section 3. It is demonstrated there that the properties of the Rectangular Quotient (1.6) resemble (extend) those of the Rayleigh Quotient (1.1). Indeed, as our review is aimed to show, the above similarity reflects a more general rule: the optimality properties of Orthogonal Quotient matrices resemble (extend) those of Rayleigh Quotient matrices.

Let be a real matrix with

orthonormal columns. Let be a real

matrix with orthonormal columns. Then an matrix of the form

(1.7)

is called an Orthogonal Quotient matrix. Note that

(1.8)

so the entries of have the same absolute value as the corresponding rectangular quotients. Matrices of the form (1.7) can be viewed as “rectangular” Rayleigh Quotient matrices. The traditional definition of symmetric Rayleigh Quotient matrices refers to symmetric matrices of the form

(1.9)

where and are defined as above, e.g., [20,30,36]. Symmetric Rayleigh Quotient matrices of this form are sometimes called sections. A larger class of Rayleigh Quotients matrices is considered in [34]. These matrices have the form

(1.10)

where is a general (nonnormal) square matrix of order. The matrix is assumed to be of full column rank, and the matrix denotes a left inverse of. That is, a matrix satisfying. Matrices of the forms (1.9) and (1.10) play important roles in the Rayleigh-Ritz procedure and in Krylov subspace methods, e.g., [30,34]. In this context there is rich literature on residual bounds for eigenvalues and eigenspaces. See, for example, [19-21,30,32,34,36]. Another applications of Rayleigh Quotient matrices arise in optimization algorithms that try to keep their approximations on a specific Stiefel manifold, e.g., [6,7,9,35].

A third class of Rayleigh Quotient matrices is obtained from (1.7) by taking. These matrices are involved in residual bounds for singular values and singular spaces, e.g., [2,21].

However, our review turns into different directions. It is aimed to explore the optimality properties of Orthogonal Quotients matrices. Comparing these properties with those of symmetric Rayleigh Quotients matrices reveals highly interesting observations. At the heart of these observations stands a surprising relationship between Eckart-Young’s minimum norm theorem [5] and Ky Fan’s maximum principle [10].

The Eckart-Young theorem considers the problem of approximating one matrix by another matrix of a lower rank. The solution of this problem is also attributed to Schmidt [31]. See [17, pp.~137,138] and [33, p.~76]. The need for low-rank approximations of a matrix is a fundamental problem that arises in many applications, e.g., [3-5,8,14,15,18,33]. The maximum principle of Ky Fan considers the problem of maximizing the trace of a symmetric Rayleigh Quotient matrix. It is also a wellknown result that has many applications, e.g., [1,10-12, 16,22,28]. Yet, so far, the two theorems have always been considered as independent and unrelated results which are based on different arguments. The Orthogonal Quotients Equality is a recent result that converts EckartYoung’s minimum norm problem into an equivalent maximum norm problem. This exposes a surprising similarity between the Eckart-Young theorem and Ky Fan’s maximum principle. We see that the two theorems reflect two sides of the same coin: there exists a more general maximum rule from which both theorems are easily derived.

The plan of our review is as follows. It starts by introducing some necessary notations and facts. Then it turns to expose the basic properties of the Rectangular Quotient (1.6), showing that it solves a number of least norm problems that resemble (1.3). An error bound, similar to (1.2), enables us to bound the distance between and the closest singular value of.

Another aspect of the analogy between eigenvalues and singular values is studied in Section 4, in which we consider “rectangular” versions of the Courant-Fischer minimax theorem and Weyl theorem. This paves the way for “traditional” proof of Eckart-Young theorem. Then, using Ky Fan’s dominance theorem, it is easy to conclude Mirsky’s theorem.

The relation between symmetric Rayleigh Quotient matrices and Orthogonal Quotients matrices is studied in Section 5. It is shown there that the least squares properties of Orthogonal Quotient matrices resemble those of symmetric Rayleigh-Quotient matrices. One consequence of these properties is the Orthogonal Quotients Equality, which is derived in Section 6. As noted above, this equality turns the Eckart-Young least squares problem into an equivalent maximum problem, which attempts to maximize the Frobenius norm of an Orthogonal Quotients matrix of the form (1.7).

The symmetric version of the Orthogonal Quotients Equality considers the problem of maximizing (or minimizing) traces of symmetric Rayleigh Quotients matrices. The solution of these problems is given by the celebrated Ky Fan’s Extremum Principles. The similarity between the maximum form of Eckart-Young theorem and Ky Fan’s maximum principle suggests that both observations are special cases of a more general extremum principle. The derivation of this principle is carried out in Section 8. It is shown there that both results are easily concluded from the extended maximum principle.

The review ends by discussing some consequences of the extended principle. One consequence is a minimum-maximum equality that relates Mirsky’s minimum norm problem with the extended maximum problem. A second type of consequence is about traces of Orthogonal Quotients matrices. The results of Ky Fan on traces of symmetric Rayleigh Quotients matrices [10] were extended in his latter papers [11,12] to products of eigenvalues and determinants. The new extremum principle enables the extension of these properties to Orthogonal Quotients matrices.

The current review brings together several old and new results. The “old” results come with appropriate references. In contrast, the “new” results come without references, as most of them are taken from a recent research paper [3] by this author. Yet the current paper derives a number of contributions which are not included in [3]. One contribution regards the extension of Theorem 11 behind the Frobenius norm. Another contribution is the minimum-maximum equality, which is introduced in Section 9. The main difference between [3] and this essay lies in their concept. The first one is a research paper that is aimed at establishing the extended extremum principle. The review exposes some fascinating features of the analogy between eigenvalues and singular values. For this purpose we present several apparently unrelated results. Putting all these topics under one roof gives a better insight into these relations. The description of the results concentrates on real valued matrices and vectors. This simplifies the presentation and helps to focus on the main ideas. The treatment of the complexvalued case should be quite obvious.

2. Notations and Basic Facts

In this section we introduce notations and facts which are needed for coming discussions. As before denotes a real matrix with. Let

(2.1)

be an SVD of, where is an

orthogonal matrix, is an orthogonal matrix, and is an diagonal matrix. The singular values of are assumed to be nonnegative and sorted to satisfy

(2.2)

The columns of and are called left singular vectors and right singular vectors, respectively. These vectors are related by the equalities

(2.3)

A further consequence of (2.1) is the equality

(2.4)

Moreover, let denote the rank of. Then, clearly,

(2.5)

So (2.4) can be rewritten as

(2.6)

Let the matrices

(2.7)

be constructed from the first columns of U andrespectively. Let be a diagonal matrix. Then the matrix

(2.8)

is called rank- Truncated SVD of.

Let, denote the entries of the matrices, respectively. Then (2.4) indicates that

(2.9)

and

(2.10)

where the last inequality follows from the CauchySchwarz inequality and the fact that the columns of and have unit length.

Another useful property regards the concepts of majorization and unitarily invariant norms. Recall that a matrix norm on is called unitarily invariant if the equalities

(2.11)

are satisfied for any matrix, and any pair of unitary matrices and. Let and be a given pair of matrices with singular values

respectively. Let and

denote the corresponding -vectors of singular values. Then the weak majorization relation means that these vectors satisfy the inequalities

(2.12)

In this case we say that is weakly majorized by, or that the singular values of B are weakly majorized by those of. The Dominance Theorem of Ky Fan [11] relates these two concepts. It says that if the singular values of are majorized by those of then the inequality

(2.13)

holds for any unitarily invariant norm. For detailed proof of this fact see, for example, [1,11,17,22]. The most popular example of a unitarily invariant norm is, perhaps, the Frobenius matrix norm

(2.14)

which satisfies

(2.15)

Other examples are the Schatten -norms,

(2.16)

and Ky Fan -norms,

(2.17)

The trace norm,

(2.18)

is obtained for and, while the spectral norm (the 2-norm)

(2.19)

corresponds to and.

Finally, let and be a pair of positive integers such that

Then

(2.20)

and

(2.21)

denote the corresponding Stiefel manifolds. That is, denotes the set of all real matrices with orthonormal columns, while is the set of all real matrices with orthonormal columns.

3. Rectangular Quotients

Let be a real matrix with, and let

and be a pair of nonzero vectors. To simplify the coming discussion we make the assumptions that, and that and are unit vectors. That is,

With these assumptions at hand the Rectangular Quotient (1.6) is reduced to the bilinear form

(3.1)

In this section we briefly derive the basic minimum norm properties that characterize this kind of bilinear forms. We shall start by noting that solves the one parameter minimization problem

(3.2)

This observation is a direct consequence of the equalities

and

(3.3)

Similar arguments show that solves the least squares problems

(3.4)

and

(3.5)

Furthermore, substituting the optimal value of into (3.3) yields the Rectangular Quotient Equality

(3.6)

which means that solving the rank-one approximation problem

(3.7)

is equivalent to solving the maximization problem

(3.8)

Using the SVD of the unit vectors in the last problem can be expressed in the form

Therefore, since

(3.9)

the objective function of (3.8) satisfies

where the last inequality comes from the CauchySchwarz inequality. Moreover, since, this pair of vectors solves (3.8), while satisfies

(3.10)

The last result is analogous to (1.4). Yet, in contrast to (1.5), here the orthogonality relations (3.9) imply that

(3.11)

Further min-max properties of scalar rectangular quotients are obtained from the Courant-Fischer theorem, see the next section.

Another justification behind the proposed definition of the Rectangular Quotient comes from the observation that the Rayleigh quotient corresponding to the matrix

and the vector is.

Hence in this case the bound (1.2) implies the existence of a singular value of, , that satisfies

(3.12)

The last bound can be refined by applying the following retrieval rules, derived in [3]. Let be a given unit vector that satisfies, and let

and

provide the corresponding estimates of a left singular vector, and a singular value, respectively. Then

and (3.12) is reduced to

(3.13)

Similarly let be a given unit vector that satisfies, and let

and

denote the corresponding estimates of a singular vector, and a singular value, respectively. Then here

and (3.12) is reduced to

(3.14)

We shall finish this section by noting the difference between the Rectangular Quotient (1.6) and the Generalized Rayleigh Quotient (GRQ) proposed by Ostrowski [26]. Let be a general (nonnormal) square matrix of order n and let and be two -vectors that satisfy where denotes the conjugate transpose of. Then the GRQ,

(3.15)

is aimed to approximate an eigenvalue of that is “common” to and. For detailed discussions of the GRQ and its properties see [25-27,29,40].

4. From Eigenvalues to Singular Values

The connection between the singular values of and the eigenvalues of the matrices A^{T}A, AA^{T}, andis quite straightforward. Indeed, many properties of singular values are inherited from this connection. Yet, as our review shows, the depth of the relations is far beyond this basic connection. The Courant-Fischer minimax theorem and Weyl’s theorem provide highly useful results on eigenvalues of symmetric matrices. See [1,30] for detailed discussions of these theorems and their consequences. Below we consider the adaption of these results when moving from eigenvalues to singular values. The adapted theorems are used to provide a “traditional” proof of the Eckart-Young theorem.

As before denotes a real matrix whose SVD is given by (2.1)-(2.7). The first pair of theorems provides useful “minimax” characterizations of singular values. In these theorems denotes an arbitrary subspace of that has dimension. Similarly, denotes an arbitrary subspace of that has dimension.

**Theorem 1 (Right Courant-Fischer Minimax Theorem)**The jth singular value of satisfies

(4.1)

and

(4.2)

where the integer is defined by the equality

(4.3)

(The maximum in (4.1) is over all dimensional subspaces of. The minimum in (4.2) is over all dimensional subspaces of.) Moreover, the maximum in (4.1) is attained for, while the minimum in (4.2) is attained for

.

Yet the solutions of both problems are not necessarily unique.

**Theorem 2 (Left Courant-Fischer Minimax Theorem)** The ith singular value of satisfies

(4.4)

and

(4.5)

where the integer is defined by the equality

(4.6)

Moreover, the maximum in (4.4) is attained for

while the minimum in (4.5) is attained for

.

Note that Theorem 2 is essentially Theorem 1 for. The proof of Theorem 1 is based on the following idea. The condition (4.3) ensures the existence of a unit vector, , that belongs both to and. Thus

and

So the proof is concluded by verifying that equality holds when using the specified subspaces.

Let denote another real matrix and let

(4.7)

denote the corresponding difference matrix. The singular values of and are denoted as

(4.8)

respectively. The coming corollaries of Theorem 1 answer the question of how the rank of affects the singular values of.

**Lemma 3 **Assume that. In this case

(4.9)

Proof. Take. Then and. Consequently

where the last inequality follows from (4.2).

**Theorem 4 (Weyl)** Let and be as in (4.7) - (4.8). Then

(4.10)

under the convention that when.

Proof. Let be a rank Truncated SVD of, and let to be a rank Truncated SVD of. Then the largest singular value of is, while the largest singular value of is. That is,

(4.11)

and

(4.12)

Let the matrix be defined by the equality

Then, and

Hence from (4.11) and (4.12) we see that

where the last inequality follows from Lemma 3.

**Corollary 5 (Majorization)** Assume further that, which means for. Then substituting in (4.10) gives

(4.13)

In other words, if then the singular values of majorize the singular values of.

Recall that denotes a rank- Truncated SVD of, as defined in (2.8). Roughly speaking the last corollary says that a rank- perturbation of may cause the singular values to “fall” not more than “levels”. The next corollary shows that they are unable to “rise” more than levels.

**Corollary 6 **Observe that (4.7) can be rewritten as while has the same singular values as. Hence a further consequence of Theorem 4 is

(4.14)

Moreover, if then

(4.15)

The next results provide useful bounds on the perturbed singular values.

**Corollary 7 (Bounding and Interlacing)** Using (4.10) and (4.14) with gives

(4.16)

and

(4.17)

Furthermore, consider the special case when is a rank-one matrix. Then using (4.13) and (4.15) with gives

(4.18)

where and.

**Theorem 8 (Eckart-Young)** Let and be as in (4.7)-(4.8) and assume that. Then

(4.19)

Moreover, let be a rank- Truncated SVD of, as defined in (2.8). Then solves the minimum norm problem

(4.20)

giving the optimal value of

Proof. Using (4.13) we see that

The last theorem says that is a best rank- approximation of, regarding the Frobenius norm. Observe that Lemma 3 proves a similar claim for the 2- norm. The next extension is due to Mirsky [23].

**Theorem 9 (Mirsky)** Let denote a unitarily invariant norm on. Then the inequality

(4.21)

holds for any matrix such that. In other words, solves the minimum norm problem

(4.22)

Proof. From Corollary 5 we see that the singular values of majorize those of. Hence (4.21) is a direct consequence of Ky Fan Dominance Theorem.

Another related problem is

(4.23)

where denotes the set of all real matrices of rank. Below we will show that the residual matrix

(4.24)

solves this problem. In other words, is the smallest perturbation that turns into a rank- matrix.

**Theorem 10** Let and C be as in (4.7) and assume that

Then

(4.25)

Proof. Using the Eckart-Young theorem we obtain

Observe that remains the solution of (4.23) when this problem is defined by any other unitarily invariant norm. Note also that Total Least Squares problems give rise to a special form of (4.23) in which. In this case the solution matrix, , is reduced to the rank-one matrix, e.g., [14,15]. Further consequences of the Eckart-Young theorem are presented in Section 6.

5. Least Squares Properties of Orthogonal Quotients Matrices

The optimality properties of symmetric Rayleigh Quotient matrices form the basis of the celebrated RayleighRitz procedure, e.g., [30,34]. In this section we derive the corresponding properties of Orthogonal Quotients matrices. As we shall see, Orthogonal Quotient matrices extend symmetric Rayleigh Quotient matrices in the same way that Rectangular Quotients extend Rayleigh Quotients.

**Theorem 11** Let

and

be a given pair of matrices with orthonormal columns, and let

(5.1)

denote the corresponding orthogonal quotient matrix. Then solves the following three problems.

(5.2)

(5.3)

and

(5.4)

Proof. Completing the columns of to be an orthonormal basis of gives an orthogonal matrix, , whose first columns are the columns of. Similarly there exists an orthogonal matrix, , whose first columns are the columns of. Therefore, since the Frobenius norm is unitarily invariant,

(5.5)

where

(5.6)

The validity of the last equality is easily verified by noting that the matrix is composed of the first columns of the identity matrix, while the matrix is composed of the first rows of the identity matrix. Note also that the corresponding principle submatrix of is. Hence the choice minimizes.

The other two problems are solved by similar arguments, using the equalities

(5.7)

and

(5.8)

where

(5.9)

**Remark** A further inspection of relations (5.7)-(5.9) indicates that solves problems (5.3) and (5.4) even if the Frobenius norm is replaced by any other unitarily invariant matrix norm. However the last claim is not valid for problem (5.2), since “punching” a matrix may increase its norm. To see this point consider the matrices

and, whose trace norms are 2 and, respectively. That is, punching increases its trace norm. Nevertheless, punching a matrix always reduces its Frobenius norm. Hence the proof of Theorem 11 leads to the following powerful results.

**Corollary 12** Let denote the set of all real matrices that have a certain pattern of zeros. (For example, the set of all tridiagonal matrices.) Let the matrix be obtained from by setting zeros in the corresponding places. Then Theorem 11 remains valid when and are replaced by and , respectively.

**Corollary 13** Assume that and let

denote the set of all real diagonal matrices. Let and be a given pair of matrices with orthonormal columns. Then the matrix

(5.10)

solves the following three problems.

(5.11)

(5.12)

and

(5.13)

As in the scalar case, the residual functions, and, enable us to bound the “distance” between the singular values of and. Assume for a moment that and let denote the singular values of. Then there exists a permutation of such that

(5.14)

see [2,21]. The relations (5.7)-(5.9) indicate that the minimal values of and equal the Frobenius norm of the corresponding off-diagonal blocks in the matrix. The next observations regard the minimal value of.

6. The Orthogonal Quotients Equality and Eckart-Young Theorem

In this section we derive the Orthogonal Quotients Equality and discuss its relation to the Eckart-Young theorem.

**Theorem 14 (The Orthogonal Quotients Equality)** Let and be a given pair of matrices with orthonormal columns. Then

(6.1)

Proof. Following the proof of Theorem 11 we see that

(6.2)

where

Therefore, since is a principal submatrix of,

**Corollary 15** Let be any matrix whose entries satisfy the following rule: Either or

. In other words, is obtained from by setting some entries to zero. Then

(6.3)

**Corollary 16 (The Orthogonal Quotients Equality in Diagonal Form)** Let and be a given pair of matrices with orthonormal columns. Then the diagonal matrix (5.10) satisfies

(6.4)

In vector notations the last equality takes the form

(6.5)

Let us return now to consider the Eckart-Young problem (4.20). One way to express an matrix, whose rank is at most, is

(6.6)

where, and. Alternatively we can write in the form

(6.7)

where is a real diagonal matrix. (The first form results from complete orthogonal decomposition of, while the second form is obtained from the SVD.) Substituting (6.6) in the objective function of (4.20) results in the function. Hence, by Theorem 11, there is no loss of generality in replacing with. Similarly D can be replaced with, the solution of (5.11). These observations lead to the following conclusions.

**Theorem 17 (Equivalent Formulations of the EckartYoung Problem)** There is no loss of generality in writing the Eckart-Young problem (4.20) in the forms

(6.8)

or

(6.9)

Moreover, both problems are solved by the SVD matrices and. (See (2.1)-(2.7) for the definition of these matrices.)

The objective functions of the last problems form the left sides of the Orthogonal Quotients Equalities

(6.10)

and

(6.11)

These relations turn the minimum norm problems (6.8)-(6.9) into equivalent maximum norm problems.

**Theorem 18 (Maximum Norm Formulations of the Eckart-Young Problem)** The Eckart-Young problems (6.8) and (6.9) are equivalent to the problems

(6.12)

and

(6.13)

respectively. The SVD matrices and solve both problems.

Let, denote the singular values of the orthogonal quotients matrix. Then, clearly,

(6.14)

and the Eckart-Young problem (6.12) can be rewritten as

(6.15)

In the next sections we consider extended problems of this type. The key for solving the extended problems lies in the properties of symmetric orthogonal quotients matrices.

7. The Symmetric Quotients Equality and Ky Fan’s Extremum Principles

Let be a real symmetric matrix with spectral decomposition

(7.1)

where is a diagonal matrixand is an orthogonal matrix,

. It is assumed that the eigenvalues of are sorted to satisfy

(7.2)

Let be a given matrix with orthonormal columns and let

denote the diagonal matrix which forms the diagonal of. Recall that

is invariant under (orthogonal) similarity transformations. Hence by following the proof of (6.1) we obtain the following results.

**Theorem 19 (The Symmetric Quotients Equality)** Using the above notations,

(7.3)

and

(7.4)

where

Note that Theorem 19 remains valid when is replaced by any real matrix. The role of symmetry becomes prominent in problems that attempt to maximize or minimize, as considered by Ky Fan [10]. In our notations Ky Fan’s problems have the form

(7.5

and

(7.6)

The solution of these problems lies in the following well-known properties of symmetric matrices, e.g., [16,30,40].

**Theorem 20 (Cauchy Interlace Theorem)** Let the matrix be obtained from by deleting rows and the corresponding columns. Let

denote the eigenvalues of. Then

(7.7)

In particular, when,

(7.8)

**Corollary 21 (Poincaré Separation Theorem) **Let be a given matrix with orthonormal columns, and let be an orthogonal matrix, whose first columns are the columns of. Then is obtained from by deleting the last rows and the last columns. Therefore, since has the same eigenvalues as, the eigenvalues of satisfy (7.7) and (7.8).

**Corollary 22 (Ky Fan’s Extremum Principles) **Consider the spectral decomposition (7.1)-(7.2) and let the matrix be constructed from the first k columns of. Then solves (7.5), giving the optimal value of. That is,

(7.9)

The minimum trace problem (7.6) is solved by the matrix

which is composed of the last columns of. The optimal value of (7.6) is, therefore,. That is,

(7.10)

The symmetric quotients equality (7.3) means that Ky Fan’s problems, (7.5) and (7.6), are equivalent to the problems

(7.11)

and

(7.12)

respectively. Note the remarkable similarity between Eckart-Young problems (6.8) and (6.12), and Ky Fan problems (7.11) and (7.5), respectively.

A further insight is gained by considering the case when G is positive semidefinite. In this case the spectral decomposition (7.1)-(7.2) coincides with the SVD of and the matrix is also positive semidefinite. Let

(7.13)

denote the eigenvalues (the singular values) of this matrix. Then here the interlacing relations (7.7) imply majorization relations between the singular values of and the singular values of the matrices

and. Consequently, for any unitarily invariant norm on, the matrix solves the problem

(7.14)

while solves the problem

(7.15)

In the next section we move from symmetric orthogonal quotients matrices to rectangular ones. In this case singular values take the role of eigenvalues. Yet, as we shall see, the analogy between the two cases is not always straightforward.

8. Maximizing (Minimizing) Norms of Orthogonal Quotients Matrices

Let us return to consider orthogonal quotient matrices of the form (5.1). Define

and let

denote the singular values of the orthogonal quotients matrix. We shall start by investigating the problems

(8.1)

and

(8.2)

where is a given positive real number,. Yet the coming results enable us to handle a larger family of objective functions. Perhaps the more interesting problems of this type occur when and. In these cases the objective function is reduced to

(8.3)

and

(8.4)

respectively. In particular, when and problem (8.1) coincides with the Eckart-Young problem (6.12). The solution of (8.1) and (8.2) is based on “rectangular” extensions of Theorems 20 and 21. The first theorem is due to Thompson [37]. We outline its proof to clarify its close relation to Cauchy Interlace Theorem.

**Theorem 23 (A Rectangular Cauchy Interlace Theorem)** Let the matrix be obtained from by deleting rows and columns of. That is, and. Define

and let

denote the singular values of. Then

(8.5)

Furthermore, the number of positive singular values of is bounded from below by

where. Consequently, and if the first singular values of satisfy the lower bounds

(8.6)

Proof. The proof is by induction on, where is the overall number of deleted rows and columns. For there are two cases to consider. Assume first that is obtained by deleting one row of. Then using Theorem 20 with and gives the desired results. The second possibility is that is obtained by deleting one column of. In this case Theorem 20 is used with and. Similar arguments enable us to complete the induction step.

Observe that the bounds (8.5) and (8.6) are “strict” in the sense that these bounds can be satisfied as equalities. Take, for example, a diagonal matrix.

**Corollary 24 (A Rectangular Poincaré Separation Theorem)** Consider the matrix, where and. Let

denote the singular values of, where. Then

(8.7)

Furthermore, define where r = rank (A), , and. Then and if the first singular values of satisfy the lower bounds

(8.8)

Proof. Let the matrix be obtained by completing the columns of to be an orthonormal basis of. Let the matrix be obtained by completing the columns of to be an orthonormal basis of. Then the matrix has the same singular values as, and is obtained from be deleting the last rows and the last columns.

**Corollary 25 **Using the former notations,

(8.9)

and

(8.10)

Furthermore, if then

(8.11)

and

(8.12)

Similar inequalities hold when the power function is replaced by any other real valued function which is increasing in the interval.

**Theorem 26 (A Rectangular Maximum Principle)** Let the matrix be constructed from the first columns of, and let the matrix be constructed from the first columns of. (Recall that and form the SVD of, see (2.1)-(2.8).) Then this pair of matrices solves the maximum problem (8.1), giving the optimal value of

. That is,

(8.13)

However, the solution matrices are not necessarily unique.

Proof. The proof is a direct consequence of (8.10) and the fact that is a diagonal matrix whose diagonal entries are.

**Corollary 27 (A rectangular Ky Fan Maximum Principle)** Consider the special case when. In this case

(8.14)

and the optimal value is attained for the matrices and.

**Corollary 28 **Consider the special case when. In this case

(8.15)

and the optimal value is attained for the matrices and. Furthermore, if then (8.15) is reduced to (6.12). This gives an alternative way to prove the Eckart-Young theorem.

**Theorem 29 (A Rectangular Minimum Principle)** Let the matrix be obtained from by deleting the first columns of. Let the matrix be obtained from by deleting

columns in the following way: If then is composed from the first columns of. Otherwise, when, the first columns of are the first columns of, and the rest columns of are the last columns of. Then the matrices and solve the minimum problem (8.2). The optimal value of (8.2) depends on the integer. If the optimal value equals zero. Otherwise, when

, the optimal value equals.

Proof. Let the matrix be obtained from the identity matrix, , by deleting the first columns of. Then, clearly,. Similarly, define to be an matrix such that. That is, is obtained from the identity matrix by deleting the corresponding columns. With these notations at hand (2.1) implies the equalities

So the matrix is obtained from by deleting the corresponding rows and columns.

Observe that the remaining nonzero entries of are the singular values of this matrix. Note also that the rule for deleting rows and columns from is aimed to make the size of the remaining nonzero entries as small as possible: The product deletes the first rows of, which contain the largest singular values. Then the product annihilates the next largest singular values. The remaining nonzero entries of are, therefore, the smallest that we can get. The number of positive singular values in is, clearly,. The optimality of our solution stems from (8.8) and (8.12).

Another pair of matrices that solve (8.2) is gained by reversing the order in which we delete rows and columns from: Start by deleting the first columns of, which contain the largest singular values. Then delete the rows of that contain the next largest singular values.

**Corollary 30 (A rectangular Ky Fan Minimum Principle) **Consider the special case when and. In this case

(8.16)

and the optimal value is attained for the matrices and.

**Corollary 31 **Consider the special case when and. In this case

(8.17)

and the optimal value is attained for the matrices and.

The next theorem extends our results to arbitrary unitarily invariant norms.

**Theorem 32** Let be a unitarily invariant norm on. Then the matrices and, which solve (8.1), also solve the problem

(8.18)

Similarly the matrices and, which solve (8.2), also solve the problem

(8.19)

Proof. From (8.7) we see that the singular values of are majorized by those of. This shows that

(8.20)

which proves the first claim. Similarly, (8.8) means that the singular values of are majorized by those of. This shows that

(8.21)

which proves the second claim.

9. A Minimum-Maximum Equality

The Orthogonal Quotients Equality (6.1) connects the Eckart-Young minimum problem with an equivalent maximum problem. The validity of this equality depends on specific properties of the Frobenius matrix norm. The question raised in this section is whether it is possible to extend this equality to other unitarily invariant matrix norms. In other words, Mirsky’s minimum norm problem (4.22) is related to the maximum norm problem (8.18) The next theorems answer this question when using the Shatten -norm (2.16) in its power form,

(9.1)

**Theorem 33 (A Minimum-Maximum Equality)** Assume that. In this case the power function (9.1) satisfies the equality

(9.2)

Proof. The optimal value of the minimized term is given by Theorem 9 (Mirsky’s theorem), and this value equals. The optimal value of the other problem is determined by the maximum principle (8.13), and this value equals.

If the power function (9.1) coincides with the trace norm (2.18). In this case (9.2) yields the following elegant result.

(9.3)

We have seen that Mirsky’s minimization problem (4.22) and the maximum problem (8.18) share a common feature: The optimal values of both problems are obtained for the SVD matrices, and. This observation enables us to extend the minimum-maximum equality to other unitarily invariant norms. Consider, for example, the spectral norm (2.19). In this case (9.3) is replaced with

(9.4)

Recall further that the jth columns of the SVD matrices form a pair of singular vectors that correspond to. Indeed, it is this property that ensures the equality in (9.2). This paves the way for another variant of the Orthogonal Quotients Equality.

**Theorem 34 **Let the matrices and be composed from pairs of singular vectors of. That is, , and for, the jth columns of these matrices satisfy:

, and the rectangular quotient is a singular value of. In this case the power function (9.1) satisfies the equality

(9.5)

Proof. The term consists of powers of singular values, while the other term consists of the rest powers.

10. Traces of Rectangular Matrices

We have seen that Ky Fan’s extremum principles maximize and minimize traces of symmetric Rayleigh Quotient matrices. In this section we bring analog results in terms of rectangular matrices. For this purpose we define the trace of a rectangular matrix as

(10.1)

where. With this definition at hand the new problems to solve are

(10.2)

and

(10.3)

Using (2.10) we see that

and

(10.4)

On the other hand, the matrices and that solve (8.1) satisfy

(10.5)

and

(10.6)

which leads to the following conclusions.

**Corollary 35** The matrices and solve (10.2)

giving the optimal value of. That is,

(10.7)

**Corollary 36** The matrices and (or and

) solve (10.3) giving the optimal value of.

That is

(10.8)

Finally we note that when the matrix turns to be a square matrix and Corollary 35 is reduced to the following known result.

(10.9)

e.g., [17, p.~195], [22, p.~515], [24].

11. Products of Eigenvalues versus Products of Singular Values

Ky Fan has used his extremum principles (Corollary 22) to derive analog results on determinants of positive semidefinite Rayleigh Quotient matrices (see below). In this section the new principle (Theorem 32) is used to extend these results to Orthogonal Quotient matrices. The interest in these problems stems from the following properties of symmetric matrices. Let G be a real symmetric positive semidefinite matrix with eigenvalues

Let be an arbitrary matrix with orthonormal columns, and let

denote the eigenvalues of the matrix. Then, clearly,

(11.1)

while Corollary 21 implies the inequalities

(11.2)

Let the matrices and be defined as in Corollary 22. Then the eigenvalues of the matrices and are

respectively. Hence from (11.2) we see that

(11.3)

and

(11.4)

where optimal values are attained for the matrices and, respectively. A further strengthening of (11.4) is gained by applying Hadamard determinant theorem, which says that the determinant of a symmetric positive semidefinite matrix, , is smaller than the product of its diagonal entries. That is,

(11.5)

where denotes the jth column of. Combining (11.5) with (11.1) and (11.2) gives the inequality

(11.6)

for any matrix. Also, as we have seen, equality holds in (11.6) when. This brings us to the following observation of Ky Fan [12].

**Theorem 37 (Ky Fan)**

(11.7)

and the optimal value is attained for.

Let us return now to consider rectangular orthogonal quotients matrices. Using the notations of Section 8, the problems that we want to solve are

(11.8)

and

(11.9)

(Compare with (8.1) and (8.2), respectively.) Let the matrices and be defined as in Theorem 26. Using (2.1) one can verify that, which is the maximal possible value, see (8.7). This brings us to the following conclusions.

**Theorem 38**

(11.10)

and the optimal value is attained for and.

**Corollary 39 **If then

(11.11)

where and are defined in (2.7).

The solution of (11.9) is found by following the notations and the proof of Theorem 29.

**Theorem 40** The matrices and solve (11.9). The number of positive singular values of the matrix is. If then

(11.12)

Otherwise, when,

(11.13)

Recall that. Hence the equality is possible only when r = n. If then the equalities and r = n imply and. Otherwise, when, the equality implies that either or.

**Corollary 41** If then

(11.14)

12. Concluding Remarks

According to an old adage, the whole can sometimes be much more than the sum of its parts. The Rayleigh-Ritz procedure, the Eckart-Young theorem, and Ky Fan maximum principle are fundamental results that have several applications. The observation that these topics are closely related is new and surprising. It illuminates these issues in a new light.

The extended maximum principle is a powerful tool that has important consequences. In particular we see that both Eckart-Young’s maximum problem and Ky Fan’s maximum problem are special cases of this observation. The minimum-maximum theorem connects the extended maximum problem with Mirsky’s minimum norm problem.

The review provides a second look at results of Ky Fan that consider eigenvalues of symmetric Rayleigh Quotient matrices. It extends these results to “rectangular” versions that consider singular values of Orthogonal Quotients matrices. The proofs illustrate the usefulness of Ky Fan’s dominance theorem. With this theorem at hand Mirsky’s theorem is easily derived from Weyl theorem. Similarly, it helps to establish the extended extremum principle.

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