An algorithm for solving a multi-valued variational inequality
© Fang et al.; licensee Springer 2013
Received: 11 August 2012
Accepted: 16 April 2013
Published: 30 April 2013
We propose a new extragradient method for solving a multi-valued variational inequality. It is showed that the method converges globally to a solution of the multi-valued variational inequality, provided the multi-valued mapping is continuous with nonempty compact convex values. Preliminary computational experience is also reported.
MSC: 47H04, 47H10, 47J20, 47J25.
Keywordsvariational inequality multi-valued mapping extragradient method
where C is a nonempty closed convex set in , F is a multi-valued mapping from C into with nonempty values, and and denote the inner product and the norm in , respectively.
Extragradient-type algorithms have been extensively studied in the literature; see [1–3]. Various algorithms for solving the multi-valued variational inequality have been extensively studied in the literature [4–15]. The well-known proximal point algorithm  requires the multi-valued mapping F to be monotone.  proposes a projection algorithm for solving the multi-valued variational inequality with a pseudomonotone mapping. In , choosing needs solving a single-valued variational inequality; see the expression (2.1) in .  presents a double projection algorithm, which is an improvement of , so that can be taken arbitrarily. In , however, choosing the hyperplane needs computing the supremum and hence is computationally expensive. To overcome this difficulty,  introduces an extragradient algorithm for solving the multi-valued variational inequality in which computing the supremum is avoided. In this paper, we present a new extragradient method for solving the multi-valued variational inequality. In our method, can be taken arbitrarily. Moreover, the main difference of our method from those of [6, 7, 11] is the procedure of Armijo-type linesearch. We also present numerical tests to compare our Algorithm 2.2 with those in [6, 11].
This paper is organized as follows. In Section 2, we present the algorithm details. We prove the preliminary results for convergence analysis in Section 3. Numerical results are reported in the last section.
Let us recall the definition of a continuous multi-valued mapping. F is said to be upper semicontinuous at if for every open set V containing , there is an open set U containing x such that for all . F is said to be lower semicontinuous at if given any sequence converging to x and any , there exists a sequence that converges to y. F is said to be continuous at if it is both upper semicontinuous and lower semicontinuous at x. If F is single-valued, then both upper semicontinuity and lower semicontinuity reduce to the continuity of F.
The property (2.2) holds if F is pseudomonotone on C.
Let denote the projector onto C, and let be a parameter.
Algorithm 2.2 Choose and two parameters . Set .
Set . If , stop.
Let and go to Step 1.
where μ is required to be strictly less than 1 or , and . In our algorithm, μ can change according to the value of in each iteration and . Secondly, the way to generate the next iterate is different. In [6, 11], the next iterate is a projection of the current iterate onto the intersection of the feasible set C and a hyperplane, while in our algorithm as well as in  the next iterate is a projection onto the feasible set C. In addition, the searching directions in  and our algorithm are also different.
Lemma 2.4 Let C be a closed convex subset of . For any and , the following statements hold.
Proof See . □
The proof of the following lemma is easy and we omit it (see Lemma 3.1 in  for example).
We first show that Algorithm 2.2 is well defined.
Proposition 2.6 If is not a solution of problem (1.1), then there exists a nonnegative integer satisfying (2.3) and (2.4).
This contradiction completes the proof. □
3 Main results
Now we obtain the following auxiliary result that will be used for proving the convergence of Algorithm 2.2.
where the second inequality follows from (2.4). This completes the proof. □
Theorem 3.2 If is continuous with nonempty compact convex values on C and the assumption (2.2) holds, then the sequence generated by Algorithm 2.2 converges to a solution of (1.1).
By the boundedness of , there exists a convergent subsequence converging to .
If is a solution of problem (1.1), we show next that the whole sequence converges to . Replacing by in the preceding argument, we obtain that the sequence is nonincreasing and hence converges. Since is an accumulation point of , some subsequence of converges to zero. This shows that the whole sequence converges to zero, hence .
where the second inequality follows from Lemma 2.5 and the equality follows from .
being continuous. Therefore, is bounded and so is .
By the boundedness of , it follows from (3.9) that . Since is continuous and the sequences and are bounded, there exists an accumulation point of such that . This implies that solves the variational inequality (1.1). Similar to the preceding proof, we obtain that .
Now we provide a result on the convergence rate of the iterative sequence generated by Algorithm 2.2. To establish this result, we need a certain error bound to hold locally (see (3.15) below). The research on an error bound is a large topic in mathematical programming. One can refer to the survey  for the roles played by error bounds in the convergence analysis of iterative algorithms; more recent developments on this topic are included in Chapter 6 in . A condition similar to (3.15) has also been used in  (see expression (10) therein) to analyze the convergence rate in a very general framework.
We say that F is Lipschitz continuous on C if there exists a constant such that, for all , , where H denotes the Hausdorff metric. □
where the second inequality follows from .
This completes the proof. □
4 Numerical experiments
[, Algorithm 1]
[, Algorithm 1]
Then the set C and the mapping F satisfy the assumptions of Theorem 3.2 and is a solution of the multi-valued variational inequality. Example 4.1 is tested in [6, 11]. We choose , for our algorithm; , , for Algorithm 1 in ; , , for Algorithm 1 in . We use as the initial point (Table 1 and Table 2).
This work is partially supported by Natural Science Foundation Project of CQ CSTC (No. 2010BB9401), Science and Technology Project of Chongqing Municipal Education Committee of China (Nos. KJ110509 and KJ100513) and Foundation of Chongqing University of Posts and Telecommunications for the Scholars with Doctorate (No. A2012-04).
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