**Applied Mathematics**

Vol.05 No.21(2014), Article ID:52232,6 pages

10.4236/am.2014.521322

On Accelerated Singular Value Thresholding Algorithm for Matrix Completion

Li Wang, Jianfeng Hu^{*}, Chuanzhong Chen

School of Mathematics and Statistics, Hainan Normal University, Haikou, China

Email: wlyoume@163.com, ^{*}lakerhjf@163.com, czchen@hainnu.edu.cn

Copyright © 2014 by authors and Scientific Research Publishing Inc.

This work is licensed under the Creative Commons Attribution International License (CC BY).

Received 16 October 2014; revised 8 November 2014; accepted 16 November 2014

ABSTRACT

An accelerated singular value thresholding (SVT) algorithm was introduced for matrix completion in a recent paper [1] , which applies an adaptive line search scheme and improves the convergence rate from O(1/N) for SVT to O(1/N^{2}), where N is the number of iterations. In this paper, we show that it is the same as the Nemirovski’s approach, and then modify it to obtain an accelerate Nemirovski’s technique and prove the convergence. Our preliminary computational results are very favorable.

**Keywords:**

Matrix Completion, Singular Value Thresholding, Nemirovski’s Line Search Scheme, Adaptive Line Search

1. Introduction

In many practical problems of interest, such as recommender system [2] -[4] , one would like to recover a matrix from a small sampling of its entries. These problems can be formulated as the following matrix completion problem:

(1)

where is the given incomplete data matrix, Ω is the set of locations corresponding to the observed entries. The problem (1) is NP-hard because the rank function is non-convex and discontinuous. However, it is known that the rank of a matrix is equal to the number of nonvanishing singular values, and the nuclear norm stand for the sum of the singular values. Thus, the rank minimization problem (1) can be relaxed as following convex program problem:

(2)

and Candès and Recht [5] proved that under suitable conditions, the program (1) and (2) are formally equivalent in the sense that they have exactly the same unique solution.

Now, there are many algorithms for solving the model (2), such as singular value thresholding (SVT) algorithm [6] , accelerated proximal gradient (APG) algorithm [7] and so on. The singular value thresholding (SVT) algorithm solves the following problem:

(3)

where P_{Ω} denotes the orthogonal projector onto the span of matrices vanishing outside of Ω so that the th component of is equal to if and zero otherwise. In [6] , Cai et al. showed that the optimal solution to the problem (3) converges to that of (2) as.

However, SVT has only a global convergence rate of, where N is the number of iterations. Then Liu Jun et al. [1] proposed an accelerated SVT algorithm by considering the Lagrange dual problem of (1.3) as follows:

(4)

where, is the soft-thresholding op-

erator [1] , and is convex and continuously differentiable with Lipschitz continuous gradient.

In the accelerated SVT algorithm, an adaptive line search scheme was adopted based on Nemirovski’s technique [8] . For reference purpose, in the following we list the related key steps of the algorithm in [1] :

Step 7: compute;

Step 8: if then;

Step 9: go to step 14;

Step 10: else;

Step 11:;

Step 12: end if;

Step 13: end while;

Step 14: set, ,

Comparing with Nemirovski’s scheme for updating L_{k}, i.e. in step 14, Liu Jun et al. [1] [9] used a more flexible scheme, in which L_{k} is not required to monotonically increase. As L_{k} is decreased, the step size is increased, it is expected that the number of iterations may be reduced. The idea is really attractive. However, it is found that the approach is just the same as the Nemirovski’s algorithm in the following section.

2. Main Procedure

First, we declare that the value of ω (in step 14) is always not bigger than 2, this means that it is just the same as Nemirovski’s line search scheme.

Since is convex and continuously differentiable with Lipschitz continuous gradient, we have

(5)

(6)

where L is the Lipschitz gradient constant [10] .

Define function, then we have

(7)

which along with gives

(8)

From (5), (6) and (7), we have

and

It follows that

that is

(9)

Combining (8), (9), and, we obtain

(10)

Then, we make a few improvement based on the original algorithm to obtain a revised algorithm. The overall steps can be organized as follows:

Algorithm 1 The Modified ASVT Algorithm

Step 1: Input:;

Step 2: Output:;

Step 3: for do;

Step 4: compute;

Step 5: compute;

Step 6: while 1 do;

Step 7: compute;

Step 8: if then;

Step 9:, go to step 3;

Step 10: else;

Step 11:;

Step 12: end if;

Step 13: end while;

Step 14: set;

Step 15: end for.

The convergence of algorithm 1 is given by the following theorem. And in this algorithm, the upper bounds of the order of convergence have nothing to do with the initial value L_{1}, which means that the modified scheme improved and accelerated the Nemirovski’s technique.

Theorem 1 For large enough k, the approximate solution Y_{k} obtained by the modified algorithm satisfies

where R_{h} is the distance from the starting point to the optimal solution set.

Proof. According to the convergence of Nemirovski’s algorithm ([8] Theorem

where. In the following we declare that for large enough k in our modified algorithm.

Suppose that there exists a positive integer k such that. Obviously, or, where l denotes the number of implementing step

and then

So we have a recurrence relation of the form, then by recurrence, we obtain

Obviously, as, we can obtain, which is impossible by finiteness of the initial value L_{1}. Thus, when k is large enough, we have. This completes the proof.

3. Computational Results

In this section, numerical experiments with MATLAB were performed to compare the performance of the Nemirovski’s technique algorithm and further to gain an insight into the behavior of our approach on synthetic dataset.

Code Ne-SVT and M-ASVT, based on the Nemirovski’s technique SVT method and our modified algorithm, respectively. Specific problems as follows: similar to the paper [1] , we generate m × n matrices M of rank r by randomly selecting two matrices of M_{L} and M_{R}, with the dimension of m × r and r × n, respectively. Each having independent identically distributed Gaussian entries, and setting. Suppose M_{Ω} is the observed part of M, and the set of observed indices is sampled uniformly at random. Let p be the ratio, that is, where n_{z} is the number of the observed entries. Then different algorithms will be used to recover the missing entries from the partially observed information by solving the optimization problem (3) with a given parameter. In our tests, was set to on the basis of Cai et al. [6] , where. We adopt the relative reconstruction error defined by following:

(11)

where X is the computed solution of an algorithm, then there is using the “error” to evaluate the quality of the algorithm. In addition, we set, for all test problems. Compiled using MATLAB2010, both Ne-SVT and M-ASVT were run under a Windows XP system on a AMD Fusion APU E-450 1.65 Ghz personal computer with 1.98 GB of memory and about 16 digits of precision.

Firstly, we compare the relative error between Ne-SVT and M-ASVT for solving the randomly generated low rank matrix problem. The initial parameters as follows: m = 100, n = 50, r = 5, p = 0.9, so meaning that 90% entries are observed. We will recover the other 10% entries by running Ne-SVT and M-ASVT separately. Table 1 reports the relative reconstruction error of different methods after 30, 60, 90 and 120 iterations. We can observe that the convergence rate of M-ASVT is really faster than that of the other, which is consistent with our analysis, and the further shows we can see Figure 1.

Then we can still test these algorithms with different settings on the following different low rank matrix completion problems: 1) Fix the matrix size, the rank r and the ratio of the observed entries p. Then test the performance with respect to different choices of the parameter τ. We fix m = 100, n = 50, r = 3, p = 0.9, and let τ change from to; 2) Fix some number remains the same, and only let p change from 0.5 to 0.9; 3) Fix some number remains the same, let rank r change from 3 to 15; 4) Fix some number remains the same, only change the size of M.

Finally, Table 2 shows the comparative results of randomly generated matrix completion problems, in which we choose error < 10^{−}^{6} as the stop condition. Clearly, we can know that the more smaller τ, the more bigger p,

Table 1. Relative error comparison between Ne-SVT and M-ASVT.

Figure 1. Convergence rate of Ne-SVT and M-ASVT on synthetic data.

Table 2. Comparisons between Ne-SVT and M-ASVT on the synthetic dataset with different settings.

the more smaller rank r and the more bigger the size, then the more smaller the error and the more better efficiently. And we can observe that the M-ASVT performance surpasses Ne-SVT in all cases.

4. Conclusion

In this paper, we point out that the ASVT algorithm [1] is essentially the same as the Nemirovski’s approach, then modify it to obtain the accelerate Nemirovski’s approach and prove the convergence. We also give the comparative results of the convergence rate of Ne-SVT and M-ASVT. The preliminary computational results show that our approach is really more efficient. We empirically choose in our test, and then we plan to study the better choice of and develop the adaptive line search scheme to further improve the algorithm.

Acknowledgements

The research is supported by Natural Science Foundation of Hainan Province of China (No. 114006).

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NOTES

^{*}Corresponding author.