Abstract

In this paper, using the near orthogonal property, we analyze the performance of greedy block coordinate descent (GBCD) algorithm when both the measurements and the measurement matrix are perturbed by some errors. An improved sufficient condition is presented to guarantee that the support of the sparse matrix is recovered exactly. A counterexample is provided to show that GBCD fails. It improves the existing result. By experiments, we also point out that GBCD is robust under these perturbations.

1. Introduction

Greedy block coordinate descent (GBCD) algorithm was presented by [1] for direction of arrival (DOA) estimation. In the work of [1], the DOA estimation is treated as the multiple measurement vectors (MMV) model that recovers a common support shared by multiple unknown vectors from multiple measurements. The authors provided a sufficient condition, based on mutual coherence, to guarantee that GBCD exactly recover the nonzero supports with noiseless measurements.

Recently, the work of [2] discussed the following method: with inputs and . denotes the measurement noise and denotes the system perturbation. The perturbations and are quantified with the following relative bounds:where and are nonzero. Here, denotes the largest spectral norm taken over all -column submatrices of . Throughout the paper, we are only interested in the case where and are far less than 1. In (1), is a -group sparse matrix; that is, it has no more than nonzero rows, and , is the th row of . It is assumed that all columns of are normalized to be of unit-norm [3]. Both and are totally perturbed in (1). This case can be found in source separation [4], radar [5], remote sensing [6], and countless other problems. In addition, the total perturbations have also been discussed in [7–9].

One of the most commonly known conditions is the restricted isometry property (RIP). A matrix satisfies RIP of the order if there exists a constant such that for all -sparse vector . In particular, the minimum of all constants satisfying (3) is called the restricted isometry constant (RIC) .

There are many papers [8, 10–14] discussing the sufficient condition for orthogonal matching pursuit (OMP) that is one of the widely greedy algorithms for sparse recovery. In [3], using the near orthogonality property, the authors improved the sufficient condition of OMP. As cited in [3], the near orthogonality property can further develop the orthogonality characterization of columns in ; it will play a fundamental role in the study of the signal reconstruction performance in compressed sensing. In the noiseless case, the work of [15] analyzed the performance of GBCD using near orthogonality property and improved the results in [2].

In this paper, under the total perturbations, we use near orthogonality property to improve the theoretical guarantee for the GBCD algorithm. In [2], the authors stated that is a sufficient condition for GBCD. We improve this condition to . We also present a counterexample to show that GBCD fails. The example is superior to that in [2]. Under the total perturbations, the robustness of GBCD is shown by experiments.

Now we give some notations that will be used in this paper. denotes the th column of a matrix . denotes the transpose of . denotes an identity matrix. The symbol denotes the vectorization operator by stacking the columns of a matrix one underneath the other. The cardinality of a finite set is denoted by . Let . . The support of is denoted by (). denotes the largest spectral norm taken over all -column submatrices of . Let denote the maximum norm of the rows of . We write for the column submatrix of whose indices are listed in set of and for the row submatrix of whose indices are listed in the set . denotes the th unit standard vector.

2. Problem Formulation

Analogous to [1], (1) can be rewritten as

Assume that . Obviously, . The objective function in (4) can be written as where with denoting the Kronecker product and . Combining the quadratic approximation for and standard BCD algorithm, the solution to the th subproblem can be given by a soft-thresholding operator. The authors in [1] only update the block that yields the greatest descent distance. Now, we list GBCD algorithm (Algorithm 1).

Suppose that satisfies the th order RIC . Recall that has no more than nonzero rows. According to the fact , we can obtain from (3).

Combining Lemma in [3] and (6), we have

Lemma 1 (near orthogonality property, see [3]). Let and be two orthogonal sparse vectors with supports and fulfilling . Suppose that satisfies RIP of order with RIC . Then we have where denotes the angle between and .

Lemma 2 (see [3]). Under the same assumptions as in Lemma 1, we have

Lemma 3. For finite sets and , let and . Here, , and . If satisfies the RIP condition (3) with , then we have

Proof. Note that the Frobenius norm of is derived from the Frobenius inner product. where (15) and (17) follow from Lemma 2 and Cauchy-Schwarz inequality, respectively.

3. RIP Based Recovery Condition

In this section, we firstly present the upper bound of the noise matrix and provide the recovery condition for GBCD.

Lemma 4 (see [2]). Suppose that satisfies the th order RIC . Then we have

According to steps () and () of Algorithm 1, at the th iteration, GBCD can obtain a correct index if

Theorem 5. Consider model (4). Let . If the matrix satisfies RIP of order withwhere , then GBCD can exactly recover the support set .

Proof. Consider . The initial value is . In order to guarantee that GBCD selects a correct index , combining step () of Algorithm 1 and (20), we should verify the following inequality: If , the right-hand-side is 0. Then inequality (26) holds. Thus, we only consider . Using Remark in [2], inequality (26) is true when Now, it is sufficient to verify (27). Let us construct an upper bound for . By step () of Algorithm 1, we have where (32) is from the property of norm and (34) follows from each column of which is of unit-norm, Lemmas 3 and 4.
To prove (27), we only need to prove We then go on to show by contradiction that (35) is true. For all , assume that Then we have Using the triangle inequality, we can get where (40) is from (10) and the property of norm.
After straightforward manipulations, we have where (41) follows from (21) and (43) follows from and (22).
Obviously, (44) contradicts (37), so this fact guarantees (27).
Assume that GBCD always picks up indices from the support for ( is an integer). Consider . In order to prove that GBCD can choose a correct index , analogous to [2], inequality (46) should be verified.Combining step () of Algorithm 1 with (46) yields It is sufficient to prove that (48) holds. Note that ; we have Now, we only need to prove We then show that (52) is true by contradiction. For all , assume that Using the definition of Frobenius norm, we have Combining , (21), and (22), we have where (59) follows from This contradicts (53). Thus, (48) is true.