Abstract
In the present paper, we use the proximal point method with remotest set control for find an approximate common zero of a finite collection of maximal monotone maps in a real Hilbert space under the presence of computational errors. We prove that the inexact proximal point method generates an approximate solution if these errors are summable. Also, we show that if the computational errors are small enough, then the inexact proximal point method generates approximate solutions
MSC:
47H05; 47H09
1. Preliminaries and the First Main Result
The proximal point method is an important tool in solving optimization problems [1,2,3,4,5]. It is also used for solving variational inequalities with monotone operators [6,7,8,9,10,11,12,13,14,15], which is an important topic of nonlinear analysis and optimization. In the present paper, we use a proximal point method with remotest set control to find an approximate common zero of a finite collection of maximal monotone operators in a real Hilbert space under the presence of computational errors. Usually, in the study of variational inequalities, the main issue is the existence of their solutions [10,13,15]. In the present paper, we are interested in their approximate solutions. We prove that the inexact proximal point method produces an approximate solution if the computational errors are summable. Also, we show that if the computational errors are small enough, then the inexact proximal point method generates approximate solutions.
Asssume that is a real Hilbert space endowed with an inner product . This inner product induces the norm .
A set-valued mapping is called a monotone operator if and only if
It is called maximal monotone if, in addition, the graph
is not properly contained in the graph of any other monotone mapping.
Assume that is a maximal monotone mapping. The proximal point algorithm produces, for an arbitrary sequence of positive numbers and an arbitrary initial point in the Hilbert space, a sequence of iterates, and the goal is to establish the convergence of this sequence of iterates. It should be mentioned that that in a general infinite-dimensional Hilbert space this convergence is usually weak. The proximal algorithm used in order to solve the inclusion is strongly based on the fundamental result obtained by Minty [16], who proved that, for an arbitrary point and an arbitrary positive number c, there exists a unique point satisfying
where is the identity mapping ( for every point ).
The mapping
is therefore a single-valued self-mapping of (where c is an arbitrary positive number). Moreover, this mapping is nonexpansive:
and
According to [17], is called the proximal mapping associated with .
The proximal point algorithm produces, for an arbitrary sequence of positive numbers and an arbitrary initial point , a sequence of iterates such that
It is clear that the
is closed in the norm topology of the product space .
Define
Usually algorithms studied in the literature produce iterates converging weakly to a point of . In this paper, for a given positive number , we are interested in finding a point such that there is satisfying .
For each point and each nonempty set , put
For each and each set
We denote by Card the cardinality of the set . For every number , define
We apply the proximal point algorithm with remotest set control in order to generate an appropriate approximation of a point which is a common zero of a finite collection of maximal monotone maps and a common fixed point of a finite collection of quasi-nonexpansive mappings (see (8)).
Let be a finite set of maximal monotone operators and be a finite set of mappings . We suppose that the set is nonempty. (Note that one of the sets or may be empty.)
Let and let , if .
We suppose that
and that for every operator , we have
Let and let and , if .
Define
Let . For every monotone operator , define
and for every mapping , set
Define
We are interested in finding approximate solutions belonging to the set where is sufficiently small.
Set
Usually, in the literature, the convergence of some iterative processes to common fixed points is under consideration. In infinite-dimensional spaces, this convergence is usually weak. Here, instead of obtaining such convergence, we show that our iterative process generates approximate common fixed points, elements of with some small positive . For this purpose, we use the so-called proximal point method with remotest set control [18]. Such results are interesting and important because in practice only a finite number of iterations are produced.
Below is our first result on the proximal point algorithm.
Theorem 1.
Let , ,
Assume that ,
and that for each integer ,
and for each there is such that
and that
Then,
2. Auxiliary Results
It is easy to see that the following lemma holds.
Lemma 1.
Let . Then,
Lemma 2.
Assume that
the integers satisfy , , ,
and that for all integers ,
Then, for every integer , the following inequality holds:
Proof.
Let an integer . By (3)–(5), (7), (9), (13), and (19),
This implies the validity of Lemma 2. □
Lemma 3.
Let
Then,
Proof.
There are two cases:
(i) ;
(ii) There exists a mapping and a number such that .
If (i) holds, then (21) follows from (8) and (20). Assume that (ii) holds. Then, by Lemma 2,
By (ii) and (2), there are , such that
By (1), (5), (20), (22), and (23), Equation (29) holds. Lemma 3 is proved. □
3. Proof of Theorem 1
Put
In view of (14), there exists a point
By (15) and (25), we have
It follows from Lemma 3, (18), (25) and (26) that for all integers ,
Define
Let be a natural number. In view of (26), (27), and (30), we have
Since the relation above holds for every natural number , we conclude that
Let
In view of (18), (30), and (32),
By (11), (16), (24), and (33), for each ,
Assume that . By (17) and (33), there exists such that
Clearly,
and by (24) and (35),
Together with (35), this implies that
Combined with (34), this implies that
for Together with (31), this implies that
Theorem 1 is proved.
4. Approximate Solutions Under the Presence of Summable Errors
In this section, we prove an extension of Theorem 1 which shows that the inexact proximal point method with the remotest set control generates an approximate solution if perturbations are summable.
Theorem 2.
Let , , satisfy
Assume that ,
and that for each integer ,
and for each there is such that
and that
Then,
Proof.
In view of (37), there exists a point
By (38) and (42),
It follows from Lemma 2 and (41)–(43) that for all integers ,
Put
Let be an integer. Lemma 3 and (42) imply that
By (41), (44), and (46),
It follows from (44), (46), and (47) that
By (43) and (48), for each natural number ,
and in view of (36),
Since the relation above holds for every natural number , we conclude that
Define
In view of (49) and (50), we have
Let an integer satisfy
In view of (50) and (52),
By (39), (45), and (53), for each ,
Assume that . By (40) and (53), there exists such that
Clearly, in view of (2),
and by (45),
Thus,
Together with (54) and (55), this implies that
for each Thus,
and in view of (51),
Theorem 2 is proved. □
5. Approximate Solutions Under the Presence of Nonsummable Errors
We show that if the perturbations are sufficiently small, then the the inexact proximal point method produces approximate solutions.
Theorem 3.
Let , be such that
δ is a positive number satisfying
and is a natural number satisfying
Assume that
for each integer ,
and for each there is such that
and that
Then, there exists an integer such that
Moreover, if an integer and (63) holds, then
Proof.
Fix
and a point
Assume that q is a natural number such that for each integer ,
Clearly,
Assume that an integer and that
By (65) and Lemma 3,
In view of (62), (67), and (68),
It follows from (57), (62), (64), and (66) that
It follows from (57), (64), and (68)–(70) that
By (77) and the relation above,
By induction and using (71), we conclude that for all integers ,
and (71) holds each . Together with (58), (64), and (66), this implies that
and
Thus, if is an integer such that for each integer (66) holds, then
This implies that there exists an integer such that for every integer ,
Assume that is an integer and that (72) holds. In view of (62) and (72),
It follows from (57), (60), (64), and (73) that
Assume that . By (43), there exists such that
and
Thus,
Theorem 3 is proved. □
Funding
This research received no external funding.
Data Availability Statement
No new data were created or analyzed in this study.
Conflicts of Interest
The author declares no conflicts of interest.
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