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On LQP alternating direction method for solving variational inequality problems with separable structure
Journal of Inequalities and Applications volume 2014, Article number: 80 (2014)
In this paper, we proposed a logarithmic-quadratic proximal alternating direction method for structured variational inequalities. The new iterate is obtained by a convex combination of the previous point and the one generated by a projection-type method along a new descent direction. Global convergence of the new method is proved under certain assumptions. We also reported some numerical results to illustrate the efficiency of the proposed method.
MSC: 90C33, 49J40.
The problem we are concerned with in this paper is for the following variational inequalities: find such that
where , are given matrices, is a given vector, and , are given monotone operators. Studies and applications of such problems can be found in [1–11]. By attaching a Lagrange multiplier vector to the linear constraints , the problem (1.1)-(1.2) can be explained in terms of finding such that
The problem (1.3)-(1.4) is referred to as SVI (structured variational inequalities).
The alternating direction method (ADM) is a powerful method for solving the structured problem (1.3)-(1.4), since it decomposes the original problems into a series subproblems with lower scale, originally proposed by Gabay and Mercier  and Gabay . The classical proximal alternating direction method (PADM) [12–14] is an effective numerical approach for solving variational inequalities with a separable structure. To make the PADM more efficient and practical, He et al.  proposed a modified PADM as follows. For given , the new iterative is obtained via the following steps.
Step 1. Solve the following system of nonlinear equations to obtain :
Step 2. Solve the following system of nonlinear equations to obtain :
Step 3. Update via
Yuan and Li  have developed a logarithmic-quadratic proximal (LQP)-based decomposition method by applying the LQP terms to regularize the ADM subproblems, by substituting in the alternating directions method (1.5)-(1.7) the term and by and , respectively. The new iterative in  is obtained via the following procedure: From a given and , is obtained via solving the following system:
Note that the LQP method was presented originally in . Later, Bnouhachem et al. [17, 18] have proposed a new inexact LQP alternating direction method by solving a series of related systems of nonlinear equations. Very recently, Li  presented an LQP-based prediction-correction method, the new iterate is obtained by a convex combination of the previous point and the one generated by a projection-type method along this descent direction.
In the present paper, inspired by the above cited works and by the recent works going in this direction, we proposed a new LQP-based prediction-correction method; the new iterate is obtained by a convex combination of the previous point and the one generated by a projection-type method along another descent direction. Under the same conditions as those in , we prove the global convergence of the proposed algorithm. It is proved theoretically that the lower bound of the progress obtained by the proposed method is greater than that by Li’s method . The effectiveness and superiority of the proposed method is verified by our preliminary numerical experiments.
2 The proposed method
In this section, we recall some basic definitions and properties, which will be frequently used in our later analysis. Some useful results proved already in the literature are also summarized. The first lemma provides some basic properties of projection onto Ω.
Lemma 2.1 Let G be a symmetry positive definite matrix and Ω be a nonempty closed convex subset of , we denote as the projection under the G-norm, i.e.,
Then we have the following inequalities:
In course we always make the following standard assumptions.
Assumption A is monotone with respect to and is monotone with respect to .
Assumption B The solution set of SVI, denoted by , is nonempty.
Now, we suggest and consider the new LQP alternating direction method (LQP-ADM) for solving SVI as follows.
Prediction step: For a given , and , the predictor is obtained via solving the following system:
Correction step: The new iterate is given by
Remark 2.1 If , and in (2.4a), (2.4b), and (2.4c), respectively, we obtain the method proposed in .
We need the following result in the convergence analysis of the proposed method.
Lemma 2.2 
Let be a monotone mapping of u with respect to and be positive definite diagonal matrix. For given , if we let and be an n-vector whose jth element is , then the equation
has a unique positive solution u. Moreover, for any , we have
In the next theorem we show that is lower bounded away from zero and it is one of the keys to prove the global convergence results.
Theorem 2.1 For given , let be generated by (2.4a)-(2.4c), then we have the following:
Proof It follows from (2.7) that
Using (2.4c), we have
Substituting (2.13) into (2.12), we get
Therefore, it follows from (2.6) and (2.10) that
and this completes the proof. □
3 Basic results
In this section, we prove some basic properties, which will be used to establish the sufficient and necessary conditions for the convergence of the proposed method. The following results are due to applying Lemma 2.1 to the LQP systems in prediction step of the proposed method.
Lemma 3.1 For given , let be generated by (2.4a)-(2.4c). Then for any , we have
Proof Applying Lemma 2.1 to (2.4a) (by setting , , in (2.9)) and
Adding (3.2) and (3.3), we obtain
Similarly, applying Lemma 2.1 to (2.4b), substituting , , , and replacing R, n with S, m, respectively, in (2.9) and
Adding (3.5) and (3.6), we have
Since is a solution of SVI, and , we have
Using the monotonicity of f and g, we obtain
Adding (3.4), (3.7), and (3.8), we get
where the last equality follows from (2.4c). It follows from (3.9) that
Using the definition of the assertion of this lemma is proved. □
Theorem 3.1 Let , be defined by (2.5) and
then we have
Proof Similarly as in (3.4) and (3.7), we have
It follows from (3.13) and (3.14) that
Since and , it follows from (2.3) that
From (2.5), we get
Using the following identity:
for , and (3.16), we obtain
Using the definition of and (3.17), we get
Using the monotonicity of f and g, we obtain
and it follows that
Applying (3.19) to the last term in the right side of (3.18), we obtain
Adding (3.15) (multiplied by σ) to (3.20), we get
and using the notation of in (2.7), the theorem is proved. □
From the computational point of view, a relaxation factor is preferable in the correction. We are now at the position to prove the contractive property of the iterative sequence.
Theorem 3.2 Let be a solution of SVI and let be generated by (2.5). Then and are bounded, and
Proof It follows from (3.11), (2.10), and (2.11) that
Since we have
and thus is a bounded sequence.
It follows from (3.21) that
which means that
Since is a bounded sequence, we conclude that is also bounded. □
4 Convergence of the proposed method
In this section, we prove the global convergence of the proposed method. The following results can be proved by using the technique of Lemma 5.1 and Theorem 5.1 in .
Lemma 4.1 For given , let be generated by (2.4a)-(2.4c). Then for any , we have
Proof Applying Lemma 2.1 to prediction step of LQP-ADM (by setting , , and in (2.9)), it follows that
By a simple manipulation, we have
and the assertion (4.1) is proved. Similarly we can prove the assertion (4.2). □
Now, we are ready to prove the convergence of the proposed method.
Theorem 4.1 The sequence generated by the proposed method converges to some which is a solution of SVI.
Proof It follows from (3.22) that
Moreover, (4.1) and (4.2) imply that
We deduce from (4.3) that
Since is bounded, so it has at least one cluster point. Let be a cluster point of and the subsequence converges to . It follows from (4.4) and (4.5) that
which means that is a solution of SVI.
Now we prove that the sequence converges to . Since
for any , there exists an such that
Therefore, for any , it follows from (3.21) and (4.6) that
This implies that the sequence converges to , which is a solution of SVI. □
represent the new iterates generated by the algorithm presented in this paper and Li’s algorithm in , respectively, where . Let
measure the progresses made by the new iterates, respectively. From (3.11), we have
Theorem 3.5 of  indicates that
Note that the optimal step sizes used in both methods are identical. It is easy to prove that
Inequality (5.3) shows theoretically that the proposed method is expected to make more progress than that in  at each iteration, and so it explains theoretically that the proposed method outperforms the method in .
6 Preliminary computational results
In this section, we report some numerical results of the proposed method, we consider the following optimization problem with matrix variables:
where is the matrix Fröbenius norm, i.e., ,
Note that the matrix Fröbenius norm is induced by the inner product
Note that the problem (6.1) is equivalent to the following:
which is equivalent to the following variational inequality: to find such that
The problem (6.3) is a special case of (1.3)-(1.4) with matrix variables where , , , , and .
For simplification, we take , and where and are scalars. In all tests we take , and as the initial point in the test. The iteration is stopped as soon as
All codes were written in Matlab; we compare the proposed method with that in . The iteration numbers denoted by k, and the computational time for the problem (6.1) with different dimensions are given in Tables 1-3.
Tables 1-3 show that the proposed method is more flexible and efficient. Moreover, it demonstrates computationally that the new method is more effective than the method presented in  in the sense that the new method needs fewer iterations and less computational time, which clearly illustrates its efficiency and thus justifies the theoretical assertions.
In this paper, we propose a new logarithmic-quadratic proximal alternating direction method (LQP-ADM) for solving structured variational inequalities. Each iteration of the new LQP-ADM includes a prediction step where a prediction point is obtained as that in , and a correction step where the new iterate is generated by a convex combination of the previous iterate and the one generated by a projection-type method along a new descent direction. Global convergence of the proposed method is proved under mild assumptions. Further, it is proved theoretically that the lower bound of the progress obtained by the proposed method is greater than that by . Some preliminary numerical results are reported to verify the efficiency of the proposed LQP-ADM and thus justified the theoretical assertions.
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The author declares that he has no competing interests.
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Cite this article
Bnouhachem, A. On LQP alternating direction method for solving variational inequality problems with separable structure. J Inequal Appl 2014, 80 (2014). https://doi.org/10.1186/1029-242X-2014-80
- variational inequalities
- monotone operator
- logarithmic-quadratic proximal method
- projection method
- alternating direction method