## 1 Introduction

The generalized singular value decomposition (GSVD) used in mathematics and numerical computations

[10, 11, 22] is a very useful and versatile tool. The GSVD of two matrices having the same number of columns was first proposed by Van Loan [22]. It is very useful in many matrix computation problems and practical applications, such as the Kronecker canonical form of a general matrix pencil, the linearly constrained least-squares problem, the general Gauss-Markov linear model, the generalized total least-squares problem, real-time signal processing, comparative analysis of DNA microarrays and so on; see for example [2, 3, 4, 5, 6, 7, 8, 10, 11, 14, 18, 20, 22, 23, 24, 26].Numerical methods and perturbation analysis of GSVD have been well developed; see for instance [4, 7, 8, 14, 18, 20, 21, 22, 23, 24, 26]. The GSVD of two matrices having the same number of columns was first proposed by Van Loan [22]. Van Loan [22] and Paige [18] provided algorithms for computing the generalized singular value decomposition. Bai and Demmel [4] described a variation of Paige’s algorithm for computing the GSVD introduced by Van Loan [22] and Paige and Saunders [19]. Ewerbring and Luk [8] and Zha [26] proposed a GSVD for matrix triplets. Stewart [20] and Van Loan [23] proposed two algorithms for computing the GSVD. On perturbation analysis of the GSVD, Sun [21] and Li [14] presented several perturbation bounds of generalized singular values (GSVs) of a Grassman matrix pair (GMP) and their associated subspaces. Xu et al. [24] provided the explicit expression and sharper bounds of the chordal metric between GSVs of a GMP.

Recently, the GSVD plays an important role in the analysis of DNA microarrays and gene data. For arbitrary generalized singular value of a GMP, the ratio can be used in comparison analysis of gene data as an indicator; see for example [2, 3]

. Moreover, the formulation for arbitrary GSV of a GMP has not been studied. Usually, the GSVD are computed by making decompositions (e.g., QR decomposition and CS decomposition). In these cases, computing the whole decomposition makes computational cost higher. The main goal of this paper is to propose new model formulations for computing arbitrary GSV of a GMP. We first derive new formulations for computing arbitrary GSV of the GMP. By using truncated filter matrices, the matrix optimization problems can be reformulated to locate the

-th GSV of the GMP. The resulting optimization problems can be solved by using Newton’s method on Grassmann manifold. Numerical examples on synthetic data sets and gene expression data sets are reported to demonstrate the high accuracy and the fast computation of the proposed method for computing arbitrary GSV of a GMP.### 1.1 Organization

The rest of this paper is organized as follows. In Section 2, we provide mathematical preliminaries and derive new formulations for computing GSVs of a GMP. In Section 3, we present the numerical method for solving the resulting optimization models. In Section 4, we provide numerical examples to show the efficiency of the theoretical results. Finally, some concluding remarks are given in Section 5.

### 1.2 Notation

Throughout this paper we always use the following notation and definitions. denotes imaginary unit and , , , and are the sets of real numbers, complex numbers,

-dimensional real vectors,

complex matrices and unitary matrices accordingly. stands for the absolute value of a complex number. andstand for the identity matrix of order

and the zero matrix, respectively. , , , denote the conjugate, transpose, conjugate transpose, inverse, determinant and trace of a matrix accordingly. By we denote the spectral norm of a matrix. The singular value set of is denoted by . For given matrices , means is a positive (semi-)definite matrix. For a matrix , we denote by its singular values, arranged in decreasing order. We denote the largest integer less than or equal to a real number . The symbol “” means the Kronecher product and creates a column vector from a matrix by stacking its column vectors below one another. Finally, denotes a block diagonal matrix in which the diagonal blocks are square matrices and .###### Definition 1.1

[10] Let and . A matrix pair is an -GMP if rank .

For any -GMP , one may see as a point of the complex projective space of all -dimensional subspaces of the -dimensional complex space , i.e., one can identify with the linear subspace (see e.g. [15, 17, 21]). Clearly, if is an -GMP, then is a definite matrix pair, i.e., for all nonzero . The definite pair has

generalized eigenvalues, and thus the GMP

has generalized singular values. A well-developed perturbation theory for the generalized eigenvalue problem of definite pencils is available. Perturbation bounds for the generalized singular value problem can be obtained with the help of the close relation between the two problems. However, the bounds obtained in this way are often unsatisfactory, just like the perturbation bounds for the singular values (SVs) of a single matrix A obtained through the perturbation bounds for the eigenvalues of the Hermitian matrix . Therefore, special attention deserves to be paid to perturbations for the generalized singular value problem. Therefore, research on numerical methods and perturbation analysis of the GSVD of a GMP is an important topic (see e.g. [4, 14, 21, 22, 24]).###### Definition 1.2

[10] Let be an -GMP. A nonnegative number pair is a GSV of the GMP if

The set of GSV of is denoted by Evidently,

In the literature [22, 23], there are several formulations of the GSVD. Here we adopt the following form.

###### Definition 1.3

Let {A,B} be an (m,p,n)-GMP. Then there exist unitary matrices and a nonsingular matrix such that

(1.1) |

(1.2) |

where

with

and

## 2 Trace function optimization model formulation

In this section we give new model formulation of any GSV of a GMP by trace function under one variable.

###### Lemma 2.1

###### Lemma 2.2

[12] Let be an analytical function of several complex variables on the domain . Then attains its maximum modulus on the characteristic manifold .

We now give new formula model of any GSV of a GMP by trace function optimization under one variable.

###### Lemma 2.3

Proof: a) If then for , we let be defined by (2.2). We set and for any , we let with . Using the GSVD of in (1.1) and (1.2) we have

Observe is an analytical function of several complex variables on the domain . By Lemma 2.2 we have attains its maximum modulus on the characteristic manifold . Therefore,

Thus, by Lemma 2.1 we have

(2.9) |

Similarly, we have

(2.10) |

b) If , then for , let be defined by (2.4). Let . For any , let with . Using the GSVD of in (1.1) and (1.2) we have

which attains its maximum modulus on the characteristic manifold by Lemma 2.2. Then by Lemma 2.1 we have

(2.11) |

Similarly, we have

(2.12) |

Using (2.11) and (2.12) we can conclude that (2.3) holds for .

c) If , then it is easy to check that . For , let be defined by (2.6). We set . Using the GSVD of in (1.1) and (1.2) and Lemma 2.1 we have

(2.13) |

Similarly, we have

(2.14) |

d) If , then . For , let be defined by (2.8). We set . From the GSVD of in (1.1) and (1.2) and Lemma 2.1 we have

(2.15) |

Similarly, we have

(2.16) |

Using (2) and (2.16) we have (2.7) holds for . The proof is complete.

Next, we give new formula model of any GSV of a GMP by trace function optimization under two variables.

###### Lemma 2.4

Proof: a) If then for , we let be defined by (2.18). We set . For any , let with . By the GSVD of in (1.1) and (1.2) we have

Observe is an analytical function of several complex variables on the domain . By Lemma 2.2 we know that attains its maximum modulus on the characteristic manifold . Therefore,

and it follows from Lemma 2.1 that

(2.25) |

Similarly, we have

(2.26) |

Comments

There are no comments yet.