## On-line version ISSN 1807-0302

### Comput. Appl. Math. vol.30 no.1 São Carlos  2011

#### http://dx.doi.org/10.1590/S1807-03022011000100005

An inexact subgradient algorithm for Equilibrium Problems

Paulo SantosI, *; Susana ScheimbergII

IDM, UFPI, Teresina, Brazil.
IIPESC/COPPE, IM, UFRJ, Rio de Janeiro, Brazil E-mails: psergio@ufpi.edu.br / susana@cos.ufrj.br

ABSTRACT

We present an inexact subgradient projection type method for solving a nonsmooth Equilibrium Problem in a finite-dimensional space. The proposed algorithm has a low computational cost per iteration. Some numerical results are reported.
Mathematical subject classification: Primary: 90C33; Secondary: 49J52.

Key words: Equilibrium problem, Projection method, Subgradient method.

1 Introduction

Let C be a nonempty closed convex subset of and let be a bifunction such that f (x, x) = 0 for all x C and C x C is contained in the domain of f. We consider the following Equilibrium Problem:

The solution set of this problem (1) is denoted by S( f, C).

This formulation gives a unified framework for several problems in the sense that it includes, as particular cases, optimization problems, Nash equilibria problems, complementarity problems, fixed point problems, variational inequalities and vector minimization problems (see for instance [4]).

In this work we assume that the function f (x, •): > (—, +] is convex and subdifferentiable at x, for all x C (see [8, 13, 15, 18]). In [8] the subdifferential of this function is called diagonal subdifferential. We define by diagonal subgradients the elements of this set.

The aim of this paper is to develop and to analyze an inexact projected diagonal subgradient method using a divergent series steplength rule. The algorithm is easy to implement and it has a low computational cost since only one inexact projection is done per iteration.

Recently, many algorithms have been developed for solving problem (1) combining diagonal subgradients with projections, see for instance, [6, 7, 15, 18, 19, 21] and references therein.

The paper is organized as follows: In Section 2 we recall useful basic notions. In Section 3 we define the algorithm and study its convergence. In Section 4, we report some computational experiments. In Section 5, we give some concluding remarks.

2 Preliminaries

In this section we present some basic concepts, properties, and notations that we will use in the sequel. Let Rn be endowed with the Euclidean inner product {•, •) and the associated norm || • ||.

Definition 2.1. Let ξ > 0 and x . A point px C is called a ξ-projection of x onto C, if px is a ξ-solution of the problem

that is

where PC(x) is the orthogonal projection of x onto C.

It is easy to show that the ξ -projection of x onto C is characterized by

Through this paper we will consider the following enlargement of the diagonal subdifferential.

Definition 2.2. The -diagonal subdifferential 2 f (x, x) of a bifunction f at x C, is given by

Let us note that the 0-diagonal subdifferential is the diagonal subdifferential 2 f (x, x), studied in [8].

The following well-known property will be useful in this paper.

Lemma 2.3. Let [vk} and {δk} be nonnegative sequences of real numbers satisfying Then the sequence (vk} is convergent.

The next technical result will be used in the convergence analysis.

Lemma 2.4. Let θ, β and ξ be nonnegative real numbers satisfying θ2β θ - ξ < 0, then,

Proof. Consider the quadratic function s( θ) = θ2β θξ, then s( θ) < 0 implies that

since θ > 0.

Multiplying the last inequality by β and using the property we obtain

The proof is complete.

In the convergence analysis we will assume that the solution set of (1) is contained in the solution set of its dual problem, which is given by

The solution set of this problem is denoted by Sd (f, C).

When f is a pseudomonotone bifunction on C (if x, y C and f (x, y) > 0, then f (y, x) < 0), it holds that S( f, C) Sd(f, C). Moreover, this inclusion is also valid for monotone bifunctions (f (x, y) + f (y, x) < 0).

Now, we are in position to define our algorithm.

3 The algorithm and its convergence analysis

Take a positive parameter p and real sequences {pk},{βk }, { k} and {ξk} verifying the following conditions:

3.1 The Inexact Projected Subgradient Method (IPSM)

Step 0: Choose x0 C. Set k = 0.

Step 1: Let xk C. Obtain gk k 2 f (xk, xk). Define

Step 2: Compute xk+1 C such that:

Notice that the point xk+1 is a ξk-projection of (xkαkgk) onto C. In particular, if ξk = 0, then xk+1 = PC (xkαkgk).

We also observe that the steplength rule (9) is similar with those given in [1] and [2]. In fact, in [1] is taking yk = max{βk, ||gk||} with Σ βk = +, while in [2] is considered pk = 1 for all k N.

In the exact version of IPSM is considered k = ξk = 0 for all k N and the following stopping criteria are included: gk = 0 (at step 1) and xk = xk+1 (at step 2).

3.2 Convergence analysis

The first result concerns the exact version of the algorithm.

Proposition 3.1. If the exact version of Algorithm IPSM generates a finite sequence, then the last point is a solution of problem EP (f, C).

Proof. Since k = 0, we have that gk 2 f (xk, xk). If the algorithm stops at step 1 we have gk = 0. So, our conclusion follows from (3).

Now, assume that the algorithm finishes at step 2, that is, xk = xk+1. Suppose, for the sake of contradiction, that xk S( f, C). Then, there exists x C such that f (xk, x) < 0. Using again (3) we get

On the other hand, by replacing xk+1 by xk in (10) and taking in account that ξk = 0, it results

Therefore, from (11) and (12) we get a contradiction because αk > 0. Hence, xk S( f, C).

From now on, we assume that the algorithm IPSM generates an infinite sequence denoted by {xk}.

We derive the following auxiliary property.

Lemma 3.2. For each k, the following inequalities hold

Proof. (i) From (9) it follows

(ii) By taking x = xk in (10) it results

where the Cauchy-Schwarz inequality is used in the second inequality and the last follows from (13).

Therefore, the desired result is obtained from Lemma 2.4 with θ = ||xk+1 — xk||, β= βk and ξ = ξk, for each k N.

The next requirement will be used in the subsequent discussions.

A1. The solution set S( f, C) is nonempty;

Notice that this is a common assumption for EP (f, C) (see for example, [11, 13, 15, 18] and references therein). Regarding the existence of solutions for equilibrium problems we refer to [9, 12, 20] and references therein.

Proposition 3.3. Assume that A1 is verified. Then, for every x* S( f,C) and for each k, the folloing assertion holds

where δk = 2αkk+ 2βk2 + 4ξ.

Proof. By a simple algebraic manipulation we have that

By combining (16) and (10) with x = x * it follows

By applying the Cauchy-Schwarz inequality and Lemma 3.2 (i), it yields

In virtue of (18) and Lemma 3.2 (ii) it results

On the other hand, from the fact that gk 2k f (xk, xk), we have that (gk, x * — xk) < f (xk, x *) + k. Therefore, since αk > 0 we obtain

The conclusion follows from (19) and (20).

The following requirement will be used to obtain the boundedness of the sequence {xk} generated by IPSM.

We point out that this assumption is weaker than the pseudomonotonicity condition. In fact, consider the following example.

Example 3.4. Let EP (f, C) be defined by

Observe that S( f, C) = [0} and f (y, x*) = f (y, 0) = 0 for all y C. Hence, A2 holds. However, the bifunction fis not pseudomonotone on C. In fact, we have f (—0.5, 0.5) = f (0.5, —0.5) = 0.25 > 0.

Notice that f (x, •) is convex for all x C and is diagonal subdifferentiable with 2 f (x, x) = [2|x |x — x2, 2|x|x + x2].

Furthermore, this example gives a negative answer to the conjecture given in [9], namely, if C is a nonempty, convex and closed set such that f(x, x) = 0, f (x, •) : C —> R is convex and lower semi-continuous, f (•, y) : C —> R is upper semi-continuous for all x C and the primal and dual equilibrium problems have the same nonempty solution set, then f is pseudomonotone.

We observe that in [12] an example which disproves the conjecture using a pseudoconvex function f (x, •) instead of a convex function is given.

Theorem 3.5. Assume that A1 and A2 are verified. Then,

Proof. (i) Let x* S( f, C) and k N. By A2 we have f (xk, x*) < 0 which together with Proposition 3.3 implies

where δk = 2αk k + 2βk2 + 4ξk.

Therefore, in virtue of (7), (8) and (9) we obtain

Hence, from (21), (22) and Lemma 2.3 it results that {||xkx*||2} is a convergent sequence.

(ii) The conclusion follows from part (i).

Now , we establish two different hypotheses on the data to obtain an asymptotic behavior of the sequence {xk} .

A3. The -diagonal subdifferential is bounded on bounded subsets of C.

A3'. The sequence {gk} is bounded.

Let us note that, condition A3 has been considered in [14] in the setting of optimization problems. Also, a similar condition has been assumed in [10] for equilibrium problems (condition (A)). We observe that A3 and A3 hold under the conditions that there is a nonempty, open and convex set U containing C such that f is finite and continuous on U x U, f (x, x) = 0 and f (x, •) : C —> R is convex for all x C ([10], Proposition 4.3). Condition A3 has been required in [7] and [15] for equilibrium problems. This condition has also been assumed in [16] and [17] for saddle point problems.

Observe that Example 3.4 satisfies both assumptions.

Theorem 3.6. Suppose that A1 and A2 are verified. Then, under A3 or A3' it holds

Proof. Let x* S( f, C). By Proposition 3.3 and 2 it results

Hence,

As m — +oo we have

which together ith (22) yields

On the other hand, by A3' or A3 we have that {||gk||} is bounded. In fact, by Theorem 3.5 we get that {xk} is bounded. Therefore, the assertion follows from A3. In consequence, using (6) and (9) we conclude that there exists L > p such that || gk || < L for all k N. Therefore

Therefore

Consequently, by (26) and (27) e have

Then, the conclusion follows from (28) and (7).

In order to obtain the convergence of the whole sequence we introduce two additional assumptions.

A5. f (•, y) is upper semicontinuous for all y C.

Assumption A4 holds, for example, when the problem EP (f, C) corresponds to an optimization problem, or when it is a reformulation of the variational inequality problem with a paramonotone operator. Moreover, the requirement A4 can be considered as an extension of the cut property given in [5] from variational inequality problems to equilibrium problems. Assumption A4 can be recovered if we assume A2 and the following condition holds

which is considered, for instance, in [3].

We also note that Assumption A5 is a common requirement for EP (f, C) (see, for example, [11, 18] and references therein).

Example 3.7. We consider the equilibrium problem defined by C = (— , 0] and f (x, y) = x2(|y| — |x|). Let us observe that A1-A5 hold. In fact, x* = 0 is the unique solution of EP(f, C), f (y, x*) = —|y|y2 < 0 for all y C, f (x, y) is continuous and implies that , that is,

Theorem 3.8. Assume that A1, A2, A3 or A3', A4 and A5 are satisfied. Then, the whole sequence {xk} converges to a solution of EP (f, C).

Proof. Let x* S( f, C). By Theorem 3.6, there exists a subsequence {xkj} of {xk} such that

In view of Theorem 3.5, we have that {xkj} is bounded. So, there is x C and a subsequence of {xkj}, without lost of generality, namely {xkj}, such that

Combining assumption A5 together with Theorem 3.6 it follows

From assumption A2 we have f (x, x*) < 0, so, it results

Therefore, A4 implies that . Using again Theorem 3.5 we obtain that the sequence is convergent, which together with (30) it yields

Notice that Theorem 3.8 remains valid if we replace assumptions A2, A4 and A5 by the t-strongly pseudomonotone condition on f with respect to x* S( f, C), that is,

This condition is weaker than the strong monotonicity of f which has been assumed in [15] for solving equilibrium problems.

4 Numerical results

In this section we illustrate the algorithm IPSM. Some comparisons are also reported. In the two first examples we compare the iterates of IPSM with such one obtained by the Relaxation Algorithm given in [6], where a constrained optimization problem and a line search have been solved at each iteration. Example 4.3 shows the computational time of IPSM versus our implementation of the Extragradient method given in [21], where a constrained optimization problem, a line search and a projection, have been performed at each iteration. Also, we present a nonsmooth example verifying our assumptions. We take ξk = k = 0, for all k N, in order to compare the performance of the algorithms.

The algorithm has been coded in MATLAB 7.8 on a 2GB RAM Pentium Dual Core.

Example 4.1. Consider the River Basin Pollution Problem given in [6] which consists of three players with payoff functions:

where u = (0.01, 0.05, 0.01) and v = (2.90, 2.88, 2.85), and the constraints are given by

We take yk = max{3, ||gk||},

Table 1 gives the results obtained by IPSM algorithm and by the Relaxation Algorithm (RA) used in [6].

Table 1 shows that both algorithms give similar approximations to x* at iteration 7, involving different computational effort. In fact, an optimization problem and an inexact line search are considered at each iteration of RA.

Example 4.2. Consider the Cournot oligopoly problem with shared constraints and nonlinear cost functions as described in [6]. The bifunction is defined by

where, n = 1.1, c = (10, 8,..., 2), K = (5, 5,..., 5), β = (1.2, 1.1,..., 0.8) and For this problem, we consider

In Table 2, we show the first three components of each iterate for sake of comparison of IPSM with the relaxation algorithm RA given in [6].

Again, despite the algorithms RA and IPSM obtain similar results at iteration 20, the computational effort is different.

Example 4.3. Consider two equilibrium problems given in [21], where

and the bifunction is of the form

The matrices P, Q and the vector q are defined by

where the i-th problem considers P = Pi, i = 1, 2.

For the first problem, we take Like in [21], we use tol = 10-3 and x0 = (1, 3, 1, 1, 2).

In Table 3, we compare IPSM with two Extragradient Algorithms (EA) given in [21].

In the second problem, we use

Like in [21], we take tol = 10—3 and x0 = (1, 3, 1, 1, 2).

In Table 4, we compare IPSM with algorithm EA given in [21].

Example 4.4. Consider the nonsmooth equilibrium problem defined by the bifunction f (x, y) = |y1| |x1| + y22 and the constraint set C = {x R2+ :x1 + x2 = 1}. The optimal point is x* = and the partial subdifferential ofthe equilibrium bifunction fis given by

We use yk = max{l, ||gk||} and ||x — x*|| < 10-4 as stop criteria. In Table 5, we show our results by considering different initial points.

5 Concluding remarks

In this paper we have presented a subgradient-type method, denoted by IPSM, for solving equilibrium problems and established its convergence under mild assumptions.

Numerical results were reported for test problems given in the literature of computational methods for solving nonsmooth equilibrium problems. The comparison with other two schemes has shown a satisfactory behavior of the algorithm IPSM in terms of the computational time.

Acknowledgements. We would like to thank two anonymous referees whose comments and suggestions greatly improved this work.

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Received: 15/08/10.
Accepted: 05/01/11.

#CAM-317/10.
*The first author is supported by CNPq grant 150839/2009-0

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