A convergence result on random products of mappings in metric trees

Let X be a metric space and {T₁, ..., T_N} be a finite family of mappings defined on D ⊂ X. Let r : ℕ → {1,..., N} be a map that assumes every value infinitely often. The purpose of this article is to establish the convergence of the sequence (x_n) defined by

In particular we prove Amemiya and Ando's theorem in metric trees without compactness assumption. This is the first attempt done in metric spaces. These type of methods have been used in areas like computerized tomography and signal processing.

Mathematics Subject Classification 2000: Primary: 06F30; 46B20; 47E10.

Many problems in mathematics [1] and physical sciences [2-4] uses a techniques known as search for a common fixed point. Indeed, let X be a metric space and suppose T₁,..., T_N are pairwise distinct self-mappings of some nonempty and closed subset D of X. Suppose further that the fixed point set, Fix(T_i) = {x ∈ D; T_i (x) = x}, of each mapping T_i is nonempty and that C = Fix ( T 1 ) ∩ ⋯ ∩ Fix ( T N ) ≠ ∅ . The aim is to find a common fixed point of these mappings. One frequently employed approach is the following.

Let r be a random mapping for {1,..., N}, i.e., a surjective mapping from ℕ onto {1,...,N} that takes each value in {1,...,N} infinitely often. Then generate a random sequence (x_n) by x₀ ∈ D arbitrary, and

and hope that this sequence converges to a point in C. We call it a random or unrestricted product (resp. iteration). For products generated by using control sequence, there are many results: for instance, cyclic control arises when r(n) = n + 1 mod N (see, for example, [5]).

In general, this random product fails to have well convergence behavior. The first positive results were done in the case when D = X is a Hilbert space and each mapping T_i, is the projection onto some nonempty, closed and convex subset C_i of X ; hence Fix(T_i) = C_i, i = 1, ..., N. The problem of finding a common fixed point is then the well-known convex feasibility problem (see, for example, [5]). Combettes article [6] proposed several interesting applications of this problem. Some of the early known results in this case are.

(1) Amemiya and Ando [7]: If each set C_i, is a closed subspace, then the random product converges weakly to the projection onto C.

(2) Bruck [8]: If some set C_i, is compact, then the random product converges in norm to a point in C. If N = 3 and each set C_i, is symmetric, then the random product converges weakly to a point in C (see also [[9], Theorem 2]).

(3) Aharoni and Censor [10], Flam and Zowe [11], Tseng [12], Eisner et al. [13]: If X is finite dimensional, then the random product converges in norm to a point in C.

The authors in [14] were successful in their extension of Amemiya and Ando's [7] results from Hilbert spaces to Banach spaces. In this study, we investigate Amemiya and Ando's result and [14]'s results in metric spaces. Such extension is the first attempt so far.

The main difficulty faced in such extensions is the heavy use of the linearity structure of either the Hilbert space in [7] or Banach spaces in [14]. Indeed when one tries to extend concepts from linear functional analysis, one has to pay attention to look deep into the supporting basic ideas and what intrinsic interrelations exist between them. Most of the main theorems in nonlinear functional analysis were done in the framework of linear Banach spaces. So it was interesting to investigate the extension of these fundamental results in nonlinear structures like metric spaces. As an example of this research is Kirk's fixed point theorem [15]. Many researchers have tried to do it but the best approach is the one given by Penot [16]. The impact of this approach went beyond what it intended to do initially. This research follows the same motivations. In particular we investigate the concept of weak convergence in metric spaces which is central, for instance, in [14]. We consider the case of metric trees to illustrate some of these ideas.

Metric trees were first introduced by Tits [17] in 1977. A metric tree is a metric space (M, d) such that for every x, y in M there is a unique arc between x and y and this arc is isometric to an interval in ℝ. For example, a connected graph without loop is a metric tree. One basic property of metric trees is their one dimensionality. Again in the late seventies, while studying t-RNA molecules of the E. Coli bacterium Eigen raised several questions which led Dress [18,19] to construct metric trees, (named as T-theory). Metric trees also arise naturally in the study of group isometries of hyperbolic spaces. For metric properties of trees we refer to [20].

Since a metric tree is a space in which there is only one path between two points x and y, this would imply that if z is a point between x and y, by which we mean if d(x, z) + d(z, y) = d(x, y) then we know that z is actually on the path between x and y. This will motivate the next concept of a metric interval.

Definition 2.1. A metric interval or metric segment [x, y] in a metric space M is defined by

First let us give the definition of a metric tree.

Definition 2.2. A metric tree is a nonempty metric space M satisfying:

(a) Any two points x and y in M are the endpoints of a metric segment [x, y].

(b) If x,y, z ∈ M then [x, y]∩[x, z] = [x, w] for some w ∈ M. (i.e., if we have two metric segments with a common endpoint, then their intersection is a metric segment.)

(c) If x, y, z ∈ M and [x, y] ∩ [y, z] = {y} then [x, y] ∪ [y, z] = [x, z] (i.e., If two metric segments intersect in a single point, then their union is a metric segment.)

Metric trees are very special. They enjoy properties which are shared by l^∞ and Hilbert space. In particular, Kirk [21] showed that complete metric trees are hyperconvex. Since the weak topology has an intimate relationship with convexity, let us define convex subset in this setting.

Definition 2.3. Let M be a metric tree and C ⊂ M. We say that C is convex if for all x, y ∈ C we have [x, y] ⊂ C.

Clearly a metric tree M and the empty set ∅ are convex. Also any closed ball B(a, r) = {z ∈ M: d(a, z) ≤ r} in a metric tree is also convex. Let C ( M ) denotes the collection of all closed and convex subsets of M, we set:

Note that C ( M ) is invariant by intersection, i.e. the intersection of any family of convex subsets of M is convex. We need the following result of Baillon [22] in order to prove our first fact about C ( M ) .

Theorem 2.1. [22]Let M be a bounded metric space and let {H_β}_β∈Γ be a decreasing family of nonempty hyperconvex subsets of M then ⋂ β ∈ Γ H β ≠ ∅ and is hyperconvex.

Since convex subsets of a metric tree are metric trees, then they are hyperconvex by [21]. This combined with Baillon's result we get the following theorem.

Theorem 2.2. Let M be a bounded complete metric tree and let {C_β}_β∈Γ be a family of nonempty, closed and convex subsets of M such that ⋂ β ∈ Γ f C β ≠ ∅ , where Γ_f is any finite subset of Γ, then ⋂ β ∈ Γ C β ≠ ∅ and is convex.

This is known as compactness of C ( M ) according to Penot's formulation [16]. Note the slight difference between the statements of the two theorems. Indeed the intersection of two convex sets is convex while the intersection of two hyperconvex sets may not be hyperconvex.

Next we discuss the nearest point projections in metric trees. Let C be a nonempty, closed and convex subset of a complete metric tree M. For any x ∈ M, denote

In a Hilbert space, the metric projections on closed and convex subsets are nonexpansive. In uniformly convex spaces, the metric projections are uniformly Lipschitzian. In fact, they are nonexpansive if and only if the space is Hilbert. In what follows we will show that the metric projections in metric trees are nonexpansive. This result is not true in hyperconvex metric spaces.

Lemma 2.1. [23,24]If C is a nonempty, closed and convex subset of a complete metric tree M, then for any x ∈ M there exists a unique c_x ∈ C such that dist(x,C) = d(x,c_x), which means that P_C is single valued. Moreover if c ∈ C we have

and

or P_C(x) = P_C(y), for any x, y ∈ M. In particular, P_C is nonexpansive.

Next we prove another property of the mapping P_C.

Proposition 2.1. If C is a nonempty, closed and convex subset of a complete metric tree M, then for any x ∈ M we have

In other words, P_C is a sunny nonexpansive mapping [25,26].

Proof. Let x ∈ M and y ∈ [x,P_C(x)]. Since M is a metric tree, there exists w ∈ M such that [y, P_C(x)] ∩ [y, P_C(y)] = [y, w]. We have w ∈ [P_C(x), P_C(y)]. Since C is convex, we get that w ∈ C. Also the definition of w implies

The properties of P_C will force P_C(y) = w which will imply P_C(y) ∈ [y, P_C(x)]. Since y ∈ [x, P_C(x)] we get

In particular we get d(x, P_C(y)) ≤ d(x, P_C(x)) which implies P_C(y) = P_C(x).

In 1965 Amemiya and Ando [7] proved the astonishing result.

Theorem 3.1. [7]Let H be a Hilbert space and {P₁,..., P_N} be a finite family of orthogonal linear projections defined on H. Let r : ℕ → {1,..., N} be a map that assumes every value infinitely often. The sequence (x_n) defined by

converges weakly in H.

Today, 46 years later, it is still not known whether (x_n) converges strongly, even for N = 3. There is doubt expressed in the literature as to whether this sequence does converge strongly (cf. [[27], Example 4]) for an interesting example of possible relevance. In general, strong convergence may be obtained when some kind of compactness is assumed. Next, we show that in the case of metric trees, we have strong convergence without any compactness assumption. The Amemiya and Ando's theorem was preceded by von Neumann [28] for alternating products of two projections (with strong convergence as the conclusion).

Theorem 3.2. Let C₁, ..., C_N be a finite family of nonempty, closed and convex subsets of a complete metric tree M such that C = ⋂ 1 ≤ i ≤ N C i ≠ ∅ . Let r : ℕ → {1, ..., N} be a map that assumes every value infinitely often. The sequence (x_n) defined by

converges strongly in M. Moreover we have

Fix

Proof. c ∈ C. Using Lemma 2.1 we have

for any n ≥ 0. In particular we have

for any n ≥ 0 and h ≥ 0. Since P C r ( n ) ( c ) = c , we get d(x_n+1,c) ≤ d(x_n, c), for any n ≥ 0. In other words, the sequence (d(x_n,c)) is decreasing. Hence lim n → ∞ d ( x n , c ) exists. Therefore the sequence (x_n) is Cauchy. Since M is complete, there exists c ∈ M such that lim n → ∞ x n = c . For any i ∈ {1,..., N}, there exists a subsequence of (x_n) which belongs to C_i. Since C_i is closed, we conclude that c ∈ C_i, for any i ∈ {1,..., N}. Hence c ∈ C. Next we show that c = P_C(x₀). For any n ≥ 0, we get

If we let n → ∞, we get

The definition of c₀ implies

which implies d(c, c₀) = 0, or c = c₀.

Remark 3.1. In [14]the authors made heavy use of the property that in smooth reflexive Banach spaces X, if E is a closed subspace of X, then there is at most one nonexpansive retraction of X onto E [26]. In the case of metric trees, we have a similar result. Indeed, let C be a nonempty, closed and convex subset of a metric tree M. Then P_C is a sunny nonexpansive retract of M onto C. Let Q : M → C be another sunny mapping. Let x ∈ M. There exists w ∈ M such that [x, P_C(x)] ∩ [x, Q(x)] = [x, w]. Since C is convex, then w ∈ C. Also since d(x,w) + d(w, P_C(x)) = d(x, P_C(x)), the definition of P_C(x) will force P_C(x) = w. Hence P_C(x) ∈ [x, Q(x)]. Since Q is sunny, we must have Q(P_C(x)) = Q(x), which implies P_C(x) = Q(x). In other words, P_C is the only sunny retract from M onto C.

In the next section we investigate the behavior of the random product of mappings other than the nearest point projections.

As the authors did in [14], one inspires itself from the Amemiya and Ando's work in Hilbert spaces to extend it to other underlying spaces. In particular the authors in [14] introduced the concepts of (W) and (S) properties. Since the (W) property is strongly linked to the weak-topology, we are not able to extend such property to metric trees.

Definition 4.1. Let M be a metric space. Let T : M → M be a nonexpansive map with a nonempty fixed point set Fix(T). We will say that T satisfies the property (S) if and only if for any c ∈ Fix(T) and any sequence (v_n) such that lim n → ∞ d ( v n , c ) - d ( T ( v n ) , c ) = 0 , we have lim n → ∞ d ( v n , T ( v n ) ) = 0 .

Remark 4.1. Note that if T₁,...,T_N are nonexpansive mappings with a common fixed point and satisfy the property (S), then we have

Indeed let c₀ ∈ M be a common fixed point of T₁, ...,T_N. Let us only prove that F i x ( T 1 ∘ ⋯ ∘ T N ) ⊂ ⋂ 1 ≤ i ≤ N F i x ( T i ) . Let c ∈ Fix(T₁ ○ ⋯ ○ T_N). Then we have T₁ ○ ⋯ ○ T_N(c) = c.

Since each mapping is nonexpansive, we get

Since T_N satisfies the property (S), we get T_N(c) = c. Similarly one will show that T_i (c) = c, for i = 1,...,N.

Another property discovered by Caristi [29] (see also [30]) and extensively used to obtain some beautiful results extending Banach contraction principle is the following definition.

Definition 4.2. Let M be a metric space. Let T : M → M be a mapping. We will say that T satisfies the property (C)-λ if and only if there exists a map λ : M → [0, ∞) such that

for any x ∈ M.

It is easy to check that if T satisfies the (C)-λ property, then any orbit (Tⁿ(x)) is a Cauchy sequence for any x ∈ M. In particular if M is complete and T is continuous, then P ( x ) = lim n → ∞ T n ( x ) is a retraction from M into Fix(T) which is nonempty.

Example 4.1. Let M = [0,2] is a metric tree being an interval of the metric tree ℝ. Define the mapping T : [0,2] → [0,1] by

Note that T is a nonexpansive retraction and Fix(T) = [0,1]. In particular (Tⁿ(x)) is convergent and its limit is T(x). But the nearest point projection on [0,1] is the map

which is different from T. Moreover one can easily show that

for any x ∈ [0,2], where λ(t) = t. Therefore T satisfies the (C)-λ property.

In the next result, we show how Theorem 3.2 extends to the family of mappings satisfying the (C)-λ property.

Theorem 4.1. Let M be a complete metric space. Let T₁,...,T_N be a finite family of pairwise distinct self-mappings of some nonempty and closed subset D of M. Suppose further that each map T_i, i = 1,..., N, is continuous and satisfies the (C)-λ property, with the same function λ. Let r be a random mapping for {1,...,N}, i.e., a surjective mapping from ℕ onto {1,...,N} that takes each value in{1,...,N} infinitely often. Then the random sequence (x_n) defined by x₀ ∈ D arbitrary, and

is convergent. Its limit is a common fixed point of the mappings T₁,...,T_N.

Proof. Let x ∈ D. Our assumptions on the mappings T_i imply

for any n ≥ 0. In particular we have

for any n ≥ 0 and h ≥ 0. On the other hand, we have λ(x_n+1) ≤ λ(x_n), for any n ≥ 0. Therefore the positive sequence (λ(x_n)) is convergent. Clearly this will imply that the sequence (x_n) is Cauchy. Since M is complete, there exists c ∈ M such that lim n → ∞ x n = c ∈ D since D is closed. For any i ∈ {1,..., N}, there exists a subsequence of (x_ϕ(n)) such that x_ϕ(n)+1 = T_i(x_ϕ(n)). Hence T_i (c) = c, for any i = 1,..., N.

Note that the limit defines a retraction on the common fixed point set of the mappings T₁,...,T_N. But this retraction may not be equal to the nearest point projection even in the case of a metric tree as the Example 4.1 shows.

The next result investigates the extension of some of the results discovered in [14]. Before we do this, we need to discuss the weak-topology in the nonlinear setting of metric spaces. Indeed, let (x_n) be a bounded sequence in the metric tree M. Define the real-valued function

where is a nontrivial ultrafilter [31]. We have the following theorem which will play a central role in our work.

Theorem 4.2. Let M be a complete metric tree. Let (x_n) be a bounded sequence in M. Then for any nontrivial ultrafilter , there exists a unique z U ∈ M such that

for any x ∈ M.

Proof. Let

For any ε > 0, consider the set

Using the properties of metric trees, we know that C_ε is a nonempty, bounded and convex subset of M. Since φ U is continuous, then it is also closed. Using the compactness of C ( M ) , then

Now, we will show that this intersection is reduced to one point. Indeed, let us Fix z U ∈ M such that φ U ( z U ) = r . Let x be any point in M. Using the properties of metric trees, for any n ≥ 1, there exists w n ∈ [ x , z U ] such that [ x n , x ] ∩ [ x n , z U ] = [ x n , w n ] . Since [ x , z U ] is compact, then there exists w ∈ [ x , z U ] such that lim U d ( w n , w ) = 0 . Since d ( x n , w n ) + d ( w n , z U ) = d ( x n , z U ) for any n ≥ 1 then we have

Hence

that is, φ U ( w ) + d ( w , z U ) = φ U ( z U ) . Obviously this will imply d ( w , z U ) = 0 or lim U d ( w n , z U ) = 0 . Also since d(x_n, w_n) + d(w_n,x) = d(x_n,x) for any n ≥ 1, then we have

Hence

that is, φ U ( z U ) + d ( z U , x ) = φ U ( x ) . This latest identity, also known as Uniform Opial condition, will easily show that z U is unique.

Definition 4.3. Let M be a metric tree and (x_n) be a bounded sequence in M. For any nontrivial ultrafilter , the unique point z U found in Theorem 4.2 is called the weak-limit of (x_n) along . We will say that (x_n) is weakly convergent if and only if z U = z V , for any nontrivial ultrafilters and .

It is because of the absence of a dual space that we used Opial behavior to try to catch the weak-limit of a bounded sequence. In the next result we show some close similarities between the classical weak-limit point in Banach spaces and the one introduced above.

Proposition 4.1. Let M be a complete metric tree and (x_n) be a bounded sequence in M. For any nontrivial ultrafilter , then

Fix

Proof. i ≥ 1. Set P_n the nearest point projection on conv ¯ ( x i ) i ≥ n . Since is nontrivial then

where we used the nonexpansiveness of P_n. This obviously implies

which implies P n ( z U ) = z U or z U ∈ con ¯ ( x i ) i ≥ n for any n ≥ 1. So z U ∈ Ω ( x n ) .

Next we discuss the behavior of mapping which satisfies the property (S).

Theorem 4.3. Let M be a complete metric tree. Let T : C → C be a nonexpansive mapping which satisfies the property (S), where C is a nonempty, bounded, closed, and convex subset of M. Then the sequence (Tⁿ(x)) converges weakly to a fixed point of T.

Proof. Let and be any nontrivial ultrafilters. Let z U and z V be the minimum point of φ U ( z ) = lim U d ( T n ( x ) , z ) and φ V ( z ) = lim V d ( T n ( x ) , z ) , respectively. Proposition 4.1 implies that z U and z V are in Ω(Tⁿ(x)) ⊂ C. Next we will prove that z U and z V are fixed point of T. It is enough to prove that T ( z U ) = z U . Since C is hyperconvex and bounded, we know that T has a nonempty fixed point set (see [32,33]). Let c ∈ Fix(T). The sequence (d(Tⁿ(x),c)) is a decreasing sequence of positive numbers. Since T satisfies the property (S), we deduce that lim n → ∞ d ( T n ( x ) , T n + 1 ( x ) ) = 0 . Hence

which implies φ U ( T ( z ) ) ≤ φ U ( z ) , for any z ∈ C. The properties of z U will force the identity T ( z U ) = z U , i.e., z U ∈ Fix ( T ) . Note that the sequence ( d ( T n ( x ) , z U ) ) is decreasing which implies

Hence

because z_V ∈ Fix(T). Since

which implies φ U ( z V ) ≤ φ U ( z U ) . The properties of z U imply z V = z U . This completes the proof of Theorem 4.3.

The authors declare that they have no competing interests.

All authors participated in the design of this work and performed equally. All authors read and approved the final manuscript.

The authors gratefully acknowledge the financial support provided by the University of Tabuk through the project of international cooperation with the University of Texas at El Paso. The authors thank the referee for pointing out some oversights and calling attention to some related literature.

1. Deutsch, F: The method of alternating orthogonal projections. In: Singh SP (ed.) Approximation Theory Spline Features and Applications, pp. 105–121. Kluwer Academic, Amsterdam (1992) Proc. Conf., Hotel Villa del Mare, Maratea, Italy, 28 April 1991-9 May 1991

2. Censor, Y: Parallel application of block-iterative methods in medical imaging and radiation therapy. Math Program. 42, 307–325 (1988)

3. Censor, Y, Herman, GT: On some optimization techniques in image reconstruction from projections. Appl Numer Math. 3, 365–391 (1987)

4. Sezan, MI: An overview of convex projections theory and its applications to image recovery problems. Ultramicroscopy. 40, 55–67 (1992)

5. Bauschke, HH, Borwein, JM: On projection algorithms for solving convex feasibility problems. Siam Rev. 38(3), 367–426 (1996)

6. Combettes, PL: Hilbertian convex feasibility problem: convergence of projection methods. Appl Math Optim. 35, 311–330 (1997)

7. Amemiya, I, Ando, T: Convergence of random products of contractions in Hilbert space. Acta Sci Math (Szeged). 26, 239–244 (1965)

8. Bruck, RE: Random products of contractions in metric and Banach spaces. J Math Anal Appl. 88, 319–332 (1982)

9. Dye, J, Reich, S: Unrestricted iterations of nonexpansive mappings in Hilbert space. Nonlinear Anal. 18, 199–207 (1992)

10. Aharoni, R, Censor, Y: Block-iterative projection methods for parallel computation of solutions to convex feasibility problems. Linear Algebra Appl. 120, 165–175 (1989)

11. Flam, SD, Zowe, J: Relaxed outer projections, weighted averages and convex feasibility. BIT. 30, 289–300 (1990)

12. Tseng, P: On the convergence of the products of firmly nonexpansive mappings. SIAM J Optim. 2, 425–434 (1992)

13. Eisner, L, Koltracht, I, Neumann, M: Convergence of sequential and asynchronous nonlinear para-contractions. Numer Math. 62, 305–319 (1992)

14. Dye, JM, Khamsi, MA, Reich, S: Random products of contractions in Banach spaces. Trans Am Math Soc. 325(1), 87–99 (1991)

15. Kirk, WA: A fixed point theorem for mappings which do not increase distances. Am Math Month. 72, 1004–1006 (1965)

16. Penot, JP: Fixed point theorems without convexity. Bull Soc Math France. 60, 129–152 (1979)

17. Tits, J: A Theorem of Lie-Kolchin for Trees. Contributions to Algebra: a Collection of Papers Dedicated to Ellis Kolchin. Academic Press, New York (1977)

18. Dress, AWM: Trees, tight extensions of metric spaces, and the chomological dimension of certain groups: a note on combinatorial properties of metric spaces. Adv Math. 53, 321–402 (1984)

19. Dress, AWM, Moulton, V, Terhalle, W: T-theory, an overview. Eur J Combin. 17, 161–175 (1996)

20. Buneman, P: A note on the metric properties of trees. J Combin Theory Ser B. 17, 48–50 (1974)

21. Kirk, WA: Hyperconvexity of ℝ-trees. Fund Math. 156, 67–72 (1998)

22. Baillon, JB: Nonexpansive mappings and hyperconvex spaces. Contem Math. 72, 11–19 (1988)

23. Aksoy, AG, Khamsi, MA: Fixed points of uniformly Lipschitzian mappings in metric trees. Scientiae Mathematicae Japonicae. 65, 31–41 (2007)

24. Aksoy, AG, Khamsi, MA: A selection theorem in metric trees. Proc Am Math Soc. 134, 2957–2966 (2006)

25. Goebel, K, Reich, S: Uniform Convexity, Hyperbolic Geometry, and Nonexpansive Mappings. Marcel Dekker New York (1984)

26. Reich, S: Product formulas, nonlinear semigroups, and accretive operators. J Funct Anal. 36(2), 147–168 (1980)

27. Dye, J, Reich, S: On the unrestricted iteration of projections in Hilbert space. J Math Anal Appl. 156, 101–119 (1991)

28. von Neumann, J: Functional operators Vol II. Ann Math Stud, Princeton University Press (1950) This is a reprint of mimeographed lecture notes first distributed in 1933

29. Caristi, J: Fixed point theorems for mappings satisfying inwardness conditions. Trans Am Math Soc. 215, 241–251 (1976)

30. Ekeland, I: Sur les problemes variationnels. Comptes Rendus Acad Sci Paris. 275, 1057–1059 (1972)

31. Aksoy, AG, Khamsi, MA: Nonstandard Methods in Fixed Point Theory.p. 139. Springer-Verlag, New York/Berlin (1990)

32. Sine, R: Hyperconvexity and nonexpansive multifunctions. Trans Am Math Soc. 315, 755–767 (1989)

33. Soardi, P: Existence of fixed points of nonexpansive mappings in certain Banach lattices. Proc Am Math Soc. 73, 25–29 (1979)