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An interior approximal method for solving pseudomonotone equilibrium problems
Journal of Inequalities and Applications volume 2013, Article number: 156 (2013)
In this paper, we present an interior approximal method for solving equilibrium problems for pseudomonotone bifunctions without Lipschitz-type continuity on polyhedra. The method can be viewed as combining a special interior proximal function, which replaces the usual quadratic function, Armijo-type linesearch techniques and the cutting hyperplane methods. Convergence properties of the method are established, among them the global convergences are proved under few assumptions. Finally, we present some preliminary computational results to Cournot-Nash oligopolistic market equilibrium models.
Let C be a nonempty closed convex subset of and a bifunction satisfying for all . We consider equilibrium problems in the sense of Blum and Oettli  (shortly ), which are to find such that
Let denote the set of solutions of Problem . When
where , Problem is reduced to the variational inequalities:
In this article, for solving Problem , we assume that the bifunction f and C satisfy the following conditions:
A1. , where A is a maximal matrix (), , and is nonempty.
A2. For each , the function is convex and subdifferentiable on C.
A3. f is pseudomonotone on , i.e., for each , it holds
A4. f is continuous on .
Equilibrium problems appear in many practical problems arising, for instance, physics, engineering, game theory, transportation, economics, and network (see [2–5]). In recent years, both theory and applications became attractive for many researchers (see [1, 6–14]).
Most of the methods for solving equilibrium problems are derived from fixed point formulations of Problem : A point is a solution of the problem if and only if is a solution of the following problem:
Namely, the sequence is generated by and
To conveniently compute the point , Mastroeni in  proposed the auxiliary problem principle for solving Problem . This principle is based on the following fixed-point property: is a solution of Problem if and only if is a solution of the problem:
where and is a strongly convex differentiable function on C. Under the assumptions that f is strongly monotone with constant on , i.e.,
and f is Lipschitz-type continuous with constants , , i.e.,
the author showed that the sequence globally converges to a solution of Problem . However, the convergence depends on three positive parameters , , and β and in some cases, they are unknown or difficult to approximate.
Many algorithms for solving the optimization problems and variational inequalities are projection algorithms that employ projections onto the feasible set C, or onto some related set, in order to iteratively reach a solution. In particular, Korpelevich  proposed an algorithm for solving the variational inequalities. In each iteration of the algorithm, in order to get the next iterate , two orthogonal projections onto C are calculated, according to the following iterative step. Given the current iterate , calculate
where is some positive number. Recently, Tran et al.  extended these projection techniques to Problem involving monotone equilibrium bifunctions but it must satisfy a certain Lipschitz-type continuous condition. To avoid this requirement, they proposed linesearch procedures commonly used in variational inequalities to obtain projection-type algorithms for solving equilibrium problems.
It is well known that the interior approximal technique is a powerful tool for analyzing and solving optimization problems. This technique has been used extensively by many authors for solving variational inequalities and equilibrium problems on a polyhedron convex set (see [18–21]), where Bregman-type interior approximal function d replaces the function g in (1.1):
with and . Then the interior proximal linesearch extragradient methods can be viewed as combining the function d and Armijo-type linesearch techniques. Convergence of the iterative sequence is established under the weaker assumptions that f is pseudomonotone on . However, at each iteration k in the Armijo-type linesearch progress of the algorithm requires the computation of a subgradient of the bifunction , which is not easy in some cases. Moreover, most of current algorithms for solving Problem are based on Lipschitz-type continuous assumptions or the computation of subgradients of the bifunction f (see [21–25]).
Our main purpose of this paper is to give an iterative algorithm for solving a pseudomonotone equilibrium problem without Lipschitz-type continuity of the bifunction and the computation of subgradients. To summarize our approach, first we use an interior proximal function d as in , which replaces the usual quadratic function in auxiliary problems. Next, we construct an appropriate hyperplane and a convex set, which separate the current iterative point from the solution set and we also combine this technique with the Armijo-type linesearch technique. Then the next iteration is obtained as the projection of the current iterate onto the intersection of the feasible set with the convex set and the half-space containing the solution set.
The paper is organized as follows. In Section 2, we recall the auxiliary problem principle of Problem and propose a new iterative algorithm. Section 3 is devoted to the proof of its global convergence and also show the relation between the solution set of and the cluster point of the iterative sequences in the algorithm. In Section 4, we apply our algorithm for solving generalized variational inequalities. Applications to the Nash-Cournot oligopolistic market equilibrium model and the numerical results are reported in the last section.
2 Proposed algorithm
Let denote the rows of the matrix A, and
where the function d is defined by (1.2). Then the gradient of at x for every is defined by
where and .
It is well known that is a solution of the regularized auxiliary problem:
where is a regularization parameter, if and only if is a solution of Problem (see ). Motivated by this, first we solve the following strongly convex problem with the interior proximal function D:
for some positive constants β. It is easy to see that with , where , and , computing becomes Step 1 of the extragradient method proposed in . In Lemma 3.2(i), we will show that if then is a solution to Problem . Otherwise, a computationally inexpensive Armijo-type procedure is used to find a point such that the convex set and the hyperplane contain the solution set and strictly separates from the solution. Then we compute the next iterate by projecting onto the intersection of the feasible set C with and the half-space . The algorithm is described in more detail as follows.
Algorithm 2.1 Choose , and .
If then Stop. Otherwise, set , where is the smallest nonnegative number such that
Step 2. Evaluate , where
Increase k by 1, and return to Step 1.
3 Convergence results
In the next lemma, we show the existence of the nonnegative integer in Algorithm 2.1.
Lemma 3.1 For , , if then there exists the smallest nonnegative integer which satisfies (2.3).
Proof Assume on the contrary, (2.3) is not satisfied for any nonnegative integer i, i.e.,
Letting , from the continuity of f, we have
Otherwise, for each , we have . We obtain after multiplication by for each ,
Then it follows from that
Since is the solution to the strongly convex program (2.2), we have
Substituting and using assumptions , , we get
Combining (3.2) with (3.3), we obtain
Then inequalities (3.1) and (3.4) imply that
Hence, it must be either or . The first case contradicts to , while the second one contradicts to the fact . The proof is completed. □
Let us discuss the global convergence of Algorithm 2.1.
Lemma 3.2 Let be the sequence generated by Algorithm 2.1 and . Then the following hold.
If , then .
Proof (i) Since is the solution to problem (2.2) and an optimization result in convex programming, we have
where denotes the normal cone. From , it follows that . Hence,
where . Replacing in this equality, we get
Thus, . Combining this with , we obtain
which means that .
Since , , for every and is convex on C, we have
Hence, we have . This means that .
For . Then since f is pseudomonotone on C and , we have . So . To prove , we will use mathematical induction. Indeed, for we have . This holds. Suppose that
Then, from and , it follows that
and hence . It implies that . Therefore, (iii) is proved.
Since is the projection of onto and (iii), by the definition of projection, we have
So, is bounded. Otherwise, using the definition of , we have
From , it holds . Combining this and (3.5), we obtain
This implies that
Thus, the sequence is bounded and nondecreasing, and hence there exists . Consequently,
Theorem 3.3 Suppose that assumptions A1 to A5 hold, is upper semicontinuous on C, and the sequence is generated by Algorithm 2.1. Then globally converges to a solution of Problem , where
Proof For each , set
From and for every , it follows that
Then we have
On the other hand, it follows from
Substituting into (3.7), we have
Combining this and (2.3), we have
From , it implies that
Using this and (3.10), we have
From (3.9) and (3.11), it follows that
Then, since is upper semicontinuous on C and is bounded, there exists such that
Combining this, and (3.8), we have
Then, it follows from (iv) of Lemma 3.2 that
The cases remaining to consider are the following.
Case 1. .
This case must follow that . Since is bounded, there exists an accumulation point of . In other words, a subsequence converges to some such that , as . Then we see from Lemma 3.2(i) that .
Case 2. .
Since is convergent, there is the subsequence of which converges to as . Then, from the continuity of f and
there exists such that the sequence converges as , where
Since is the smallest nonnegative integer, does not satisfy (2.3). Hence, we have
Passing onto the limit in (3.12), as and using the continuity of f, we have
where . From Algorithm 2.1, we have
Since f is continuous, passing onto the limit, as , we obtain
Using this and (3.13), we have
which implies , and hence . So, all cluster points of belong to the solution set .
Set and suppose that the subsequence converges to as . By (iii) of Lemma 3.2, we have
As , we get and
The last inequality is due to . So, and the sequence has an unique cluster point . □
Now we consider the relation between the solution existence of Problem and the convergence of generated by Algorithm 2.1.
Lemma 3.4 (see )
Suppose that C is a compact convex subset of and f is continuous on C. Then the solution set of Problem is nonempty.
Theorem 3.5 Suppose that assumptions A1 to A4 hold, f is continuous, is upper semicontinuous on C, the sequence is generated by Algorithm 2.1, and . Then we have
Consequently, the solution set of Problem is empty if and only if the sequence diverges to infinity.
Proof The first, we show that for every . On the contrary, suppose that there exists such that
Then there exists a positive number M such that
where . From Lemma 3.4, it implies that the solution set of Problem is nonempty, where . Applying Algorithm 2.1 to Problem . In order to avoid confusion with the sequences , and , we denote the three corresponding sequences by , and . With , the following claims hold:
The set has at least elements.
, and for every .
is not a solution to Problem .
Using and (iii) of Lemma 3.2, we have . Then we also have , which contradicts the supposition that . So,
This implies that the inequality (3.6) also holds in this case, the sequence is still nondecreasing. We claim that
Suppose for contraction that the exists . Then is bounded and it follows from (3.6) that
A similar discussion as above leads to the conclusion that the sequence converges to , which contradicts the emptiness of the solution set . The theorem is proved. □
4 Applications to Cournot-Nash equilibrium model
Now we consider the following Cournot-Nash oligopolistic market equilibrium model (see [25–28]): There are n-firms producing a common homogenous commodity and that the price of firm i depends on the total quantity of the commodity. Let denote the cost of the firm i when its production level is . Suppose that the profit of firm i is given by
where is the cost function of firm i that is assumed to be dependent only on its production level. There is a common strategy space for all firms. Each firm seeks to maximize its own profit by choosing the corresponding production level under the presumption that the production of the other firms are parametric input. In this context, a Nash equilibrium is a production pattern in which in which no firm can increase its profit by changing its controlled variables. Thus, under this equilibrium concept, each firm determines its best response given other firms’ actions. Mathematically, a point is said to be a Nash equilibrium point if is a solution of the problem:
Then the problem of finding an equilibrium point of this model can be formulated as Problem . It follows from Lemma 3.2 (i) that is a solution of Problem if and only if . Thus, is an ϵ-solution to Problem , if . To illustrate our algorithm, we consider two academic numerical tests of the bifunction f in .
Example 4.1 We consider an application of Cournot-Nash oligopolistic market equilibrium model taken from . The equilibrium bifunction is defined by
In this case, the bifunction f is pseudomonotone on C and the interior approximal function (2.1) is defined through
It is easy to see that . Take , , , , , we get iterates in Table 1. The approximate solution obtained after 361 iterations is
The same as Example 4.1, we only change the bifunction which has the form
where , the components of d are chosen randomly in .
Then the bifunction f satisfies convergent assumptions of Theorem 3.3 in this paper and Theorem 3.1 in . We choose the parameters in Algorithm 2.1: , , , , . In the algorithm (shortly (IPLE)) proposed by Nguyen et al. , the parameters are chosen as follows: , , , , for all . We compare Algorithm 2.1 with (IPLE). The iteration numbers and the computational time for 5 problems are given in Table 2.
The computations are performed by Matlab R2008a running on a PC Desktop Intel(R) Core(TM)i5 firstname.lastname@example.org GHz 3.33 GHz 4 Gb RAM.
This paper presented an iterative algorithm for solving pseudomonotone equilibrium problems without Lipschitz-type continuity of the bifunctions. Combining the interior proximal extragradient method in , the Armijo-type linesearch and cutting hyperplane techniques, the global convergence properties of the algorithm are established under few assumptions. Compared with the current methods such as the interior proximal extragradient method, the dual extragradient algorithm in , the auxiliary principle in , the inexact subgradient method in , and other methods in , the fundamental difference here is that our algorithm does not require the computation of subgradient of a convex function. We show that the cluster point of the sequence in our algorithm is the projection of the starting point onto the solution set of the equilibrium problems. Moreover, we also give the relation between the existence of solutions of equilibrium problems and the convergence of the iteration sequence.
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We are very grateful to the anonymous referees for their really helpful and constructive comments in improving the paper. The work was supported by National Foundation for Science and Technology Development of Vietnam (NAFOSTED), code 101.02-2011.07.
The authors declare that they have no competing interests.
The main idea of this paper is proposed by PNA. PNA and PMT prepared the manuscript initially and performed all the steps of proof in this research. All authors read and approved the final manuscript.
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Anh, P.N., Tuan, P.M. & Long, L.B. An interior approximal method for solving pseudomonotone equilibrium problems. J Inequal Appl 2013, 156 (2013). https://doi.org/10.1186/1029-242X-2013-156
- equilibrium problem
- interior approximal function
- cutting hyperplane method