Abstract
In this paper, we study a feasibility problem with infinitely many sets in a metric space. We present a novel algorithm and analyze its convergence. The algorithms used for the feasibility problem in the literature work for finite collections of sets and cannot be applied if the collection of sets is infinite. The main feature of these algorithms is that, for iterative steps, we need to calculate the values of all the operators belonging to our family of maps and even their sums with weighted coefficients. This is impossible if the family of maps is not finite. In the present paper, we introduce a new algorithm for solving feasibility problems with infinite families of sets and study its convergence. It turns out that our results hold for feasibility problems in a general metric space.
MSC:
47H04; 47H09; 47H10
1. Introduction
The convex feasibility problem is used to obtain a common element of a finite family of convex and closed sets or at least its approximation. This problem, investigated in [1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23], is very important in the optimization with constraints. It is also used in engineering, medical, and natural sciences.
Assume that , , where is a natural number, are closed and convex sets in a real Hilbert space endowed with an inner product and a complete norm , which is induced by the inner product. We consider the problem
under the assumption that is nonempty. It is well-known [3] that, for each and each , there exists a unique element such that
and
for each and each . For each and each set,
This convex feasibility problem can be written as the optimization problem
This is a convex minimization problem and one can try to solve it using some optimization methods. However, in the practice for solving convex feasibility problems, the following iterative method is used.
Fix an integer and denote by the collection of all maps such that for every positive integer s,
We associate with any map the following iterative algorithm:
Initialization: choose any starting point of the space X.
Iterative step: given a current iterate calculate
It is known that iterates obtained using this method weakly converge to a solution of our feasibility problem. The same result is also guaranteed by the well-known Cimmino algorithm described below:
Initialization: choose any starting point of the space X.
Iterative step: given a current iterate calculate
Recently, Y. Censor, T. Elfving, and G. T. Herman in [24] introduced dynamic string-averaging methods, which are, in some sense, a combination of the iterative algorithm and the Cimmino algorithm. In these dynamic string-averaging methods, which became very popular in the literature, a family of sets is divided into blocks and the algorithms operate in such a manner that all the blocks are processed in parallel.
In the present paper, we study a feasibility problem with a collection of sets that is not necessarily finite. Clearly, the algorithms described above cannot be applied if the collection of sets is infinite. The main feature of these algorithms is that, for iterative steps, we need to calculate the values of all the operators belonging to our family of maps and even their sums with weighted coefficients. Of course, this is impossible if the family of maps is not finite. In the present paper, we introduce a new algorithm for solving feasibility problems with infinite families of sets and study its convergence. It turns out that our results hold for feasibility problems in a general metric space.
2. Preliminaries and the First Main Result
Let be a metric space endowed with a metric . For every element and every positive number r, put
For every element and every nonempty set , define
Fix . Denote by Card the cardinality of a set E. We assume that the sum over an empty set is zero.
Assume that is a nonempty set, for each , is a nonempty, closed set and that there exists such that
In the sequel, we use the following assumption.
(A1) There exists such that, for each , each and each ,
Assume that there exists
We consider the problem
and use the following algorithm.
Let a sequence satisfy
Initialization: choose any element .
Iterative step: given a current iterate calculate such that
and calculate
The following theorem is our first main result.
Theorem 1.
Let (A1) hold,
, a natural number Q satisfy
a sequence satisfy
and let an integer satisfy
Assume that a sequence satisfies
and that, for each integer , there exists such that
and
Then,
if an integer satisfies , then
and
Proof.
Assumption (A1) and Equations (2) and (3) imply that, for each integer ,
It follows from (4), (8), and (11) that, for each integer ,
Assumption (A1) and Equations (3), (9) and (12) imply that for each integer satisfying
we have
Let n be a natural number. By (4), (5), (8), and (13),
and
Since n is any natural number, we conclude that
Since is any element of , we have
Set
In view of (14) and (15),
Assume that
By (15) and (16),
It follows from (7) and (15)–(17) that, for each ,
and
Theorem 1 is proved. □
We say that the family has a bounded regularity property (or (BRP) for short) [3] if, for each , there exists such that, for each satisfying , the inequality holds.
Clearly, (BRP) holds if the space X is finite dimensional or if there is a set in the collection such that all its bounded, closed subsets are compact.
Theorem 1 implies the following result.
Proposition 1.
Let (BRP) and (A1) hold,
and a let sequence satisfy
Then, there exists a natural number Q such that for each sequence , which satisfies
and such that for each integer there exists satisfying
and
the equations
and
hold.
Example 1.
The results of this section can be applied for the feasibility problem, where X is the Hilbert space of square-summable sequences of the real numbers , for each integer , , and for each , such that for each natural number . It is easy to see that the assumptions posed in this section as well as its results hold for this family of sets.
3. The Second Main Result
We use the notation and definitions introduced in Section 2.
We continue to assume that is a nonempty set, for each , is a nonempty, closed set and that there exists such that
In the sequel, we use the following assumption.
(A2) For each , there exists such that for each , each and each satisfying
the inequality
holds.
Assume that there exists
The following theorem is our second main result.
Theorem 2.
Let (A2) hold,
a sequence satisfy
, and let an integer satisfy
Then, there exists a natural number Q depending on such that, for each sequence, , which satisfies
and such that, for each integer , there exists satisfying
and
The inequalities
and
hold, if an integer satisfies ; then,
and
Proof.
Assumption (A2) implies that there exists such that the following property holds:
(i) For each , with each and each satisfying
we have
Fix an integer
Assume that and satisfy (22)–(24) for each integer By (A2) and Equations (19), (20) and (22), for each integer ,
and
Property (i) and Equations (19), (20), (23) and (28) imply that, for each integer satisfying
we have
Thus, the following property holds:
(ii) If is an integer and (29) holds, then (30) is true.
Let n be a natural number. Property (ii) and Equations (20), (22), (26), (29) and (30) imply that
and
Since n is any natural number, we conclude using (25) that
Since is any element of , we have
Assume that is an integer and that
It follows from (21) and (24) that, for each ,
Together with (31), this implies that
Theorem 3 is proved. □
Theorem 3 implies the following result.
Proposition 2.
Let (BRP) and (A2) hold,
and a sequence satisfy
Then, there exists a natural number Q such that, for each integer sequence , which satisfies
and such that, for each integer , there exists satisfying
and
the equations
and
hold.
4. The Third Main Result
We use the notation and definitions introduced in Section 2.
We continue to assume that is a nonempty set, for each , is a nonempty, closed set, that there exists , and that (18) and (19) hold.
In the sequel, we use the following assumption.
(A3) For each , there exists such that, for each , each and each satisfying
the inequality
holds.
The following theorem is our third main result.
Theorem 3.
Let (A3) hold,
, a sequence satisfy
and an integer satisfy
Then, there exists a natural number Q depending on such that, for each sequence , which satisfies
and such that, for each integer , there exists satisfying
and
the inequalities
and
hold; if an integer satisfies , then
and
Proof.
Assumption (A3) implies that there exists such that the following property holds:
(i) For each , with each and each satisfying
we have
Fix an integer
Assume that and satisfy (34)–(36) for each integer By (A3) and Equations (18), (19), (32), and (34), for each integer ,
and
Property (i) and Equations (19), (32) and (40) imply that, for each integer satisfying
we have
Thus, the following property holds:
(ii) If is an integer and (41) holds, then (42) is true.
Assume that is an integer and that
Then, there exists such that
By (A3), (35) and (43),
In view of (43) and (44),
By (33), (36), (45), and the inequality , for each ,
Therefore, the following property holds:
(iii) If is an integer and
then
Let n be a natural number. Property (ii) and Equations (38), (39), (41) and (42) imply that
and
Since n is any natural number, we conclude using (37) that
Property (iii) and (46) imply that
Theorem 5 is proved. □
Theorem 5 implies the following result.
Proposition 3.
Let (BRP) and (A3) hold,
and a sequence satisfy
Then, there exists a natural number Q such that for each sequence satisfying
and such that, for each integer , there exists satisfying (35) and (36), the inequality
holds.
5. Conclusions
In this paper, we study a feasibility problem with infinitely many sets in a metric space. Usually, in the literature, the feasibility problem is studied with a finite family of sets using the iterative method, the Cimmino algorithm, and the dynamic string-averaging methods, which are, in some sense, a combination of the iterative algorithm and the Cimmino algorithm. These algorithms work well for problems with finite families of sets but cannot be applied when a family of sets is infinite. The main feature of these algorithms is that, for iterative steps, we need to calculate the values of all the operators belonging to our family of maps and even their sums with weighted coefficients. Of course, this is impossible if the family of maps is not finite. In our paper, we introduce a new algorithm that can be applied for feasibility problems with infinite families of sets and analyze its convergence.
Funding
This research received no external funding.
Data Availability Statement
Not applicable.
Conflicts of Interest
The authors declare no conflict of interest.
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