1. Introduction
The chaotic behavior of various complex systems, both natural and human made, has been mitigated using various methods for centuries. One of these methods works mostly in management problems and starts by using singular hypotheses, pursuing multi-stage decisions is dynamic programming. These decisions could be optimized later, leading to a model with wide applicability. However, this implies that a more complex problem has to be reduced to simpler, elementary cases, such that each case belongs to a known model with its own optimal solution. Models or recipes for quickly reaching the solution of a dynamic programming problem do not exist. We do, however, find similar assumptions and reasoning connected to the solution of a dynamic programming problem.
Richard Bellman [
1] worked for the first time with this method of optimization, called by him “dynamic programming”. It helps optimizing systems based on phases or sequences, using their mathematical representations. Systems of this type are frequently encountered in economic studies or in the development of advanced programs, such as those concerning cosmic navigation [
2]. When encountering the need of the regularization of stocks, management of equipment, mining prospecting, and investment, as well as macroeconomic problems, such as the national planning, the sequential structure of the problems paves the way for the use of well-chosen methods to perform optimization calculations and clarify problems in introducing precise concepts such as decision criteria and appropriate policies.
Given the uniqueness of almost every dynamic programming problem, a multitude of methods and algorithms from various branches of mathematics was used to solve those problems. Of these, the Banach fixed-point principle stands out as being a remarkable tool for elegantly providing at least the solution’s unicity of a dynamic programming problem [
3].
Fixed-point theory and functional analysis is being used in more and more applications from various fields such as economy, physics and astronomy, game theory, and many others.
Fuzzy metric spaces were the general set up for the first fixed-point results. One of the pioneers in this field was M. Grabiec [
4], who defined the fuzzy complete metric space (denoted G-complete) and extended this framework to reach the Banach contraction theorem. It is also worth mentioning the work of J.X. Fang [
5], who extended the results pool in this direction, and A. George and P. Veeramani [
6], who gave a modified definition of a Cauchy sequence, yielding a new notion of completitude in fuzzy spaces endowed with a metric (G-V completeness). Since all fuzzy metric spaces that are G-complete are also G-V complete, it makes sense to work on theorems referring to fixed-point properties in these fuzzy metric spaces which are G-V complete.
As important results of our work, some classic fixed-point theorems will be proved in the setting of G-V complete fuzzy normed spaces, and the application of dynamic programming will use this new type of Banach’s contraction principle on G-V complete fuzzy normed spaces.
In our paper, the general framework is set to fuzzy normed linear spaces as they are defined in [
7], and, where completeness is required, we will use the G-V setup.
The results obtained are established in a different context than in other papers that deal with fuzzy fixed-point theory. More precisely, we work with the A. George and P. Veeramani definitions of completeness, while other authors consider the definition given by M. Grabiec [
8,
9,
10]. Another important difference is the way fuzzy normed spaces are defined. We utilize the one given by S. Nadaban and I. Dzitac in 2014, which induces a Kramosil–Michálek type metric [
11]. In other articles [
5,
12,
13], different definitions of fuzzy normed spaces are given, in which the fuzzy norm generates a fuzzy metric of the type of George and Veeramani.
The structure of the work is: after the preliminary section, in
Section 3 we generalize, extend, and obtain new fixed-point theorems similar to those that can be found in [
14,
15,
16,
17,
18,
19,
20,
21,
22,
23,
24]. The first result obtained is Banach’s Contraction Principle in FNLSs of Nădăban-Dzitac type, using completeness in the George–Veeramani sense. An important original result is Theorem 2, which presents in the context of Nădăban–Dzitac type FNLSs, a result obtained by Jungck. More precisely, it offers a version of Jungck’s theorem in this FNLSs, which is induced by a fuzzy metric that is more general than the one given in the case of the fuzzy metric of Grabiec type (see Theorem 5.4.12 [
12]). In Theorem 5 the property of continuous dependence of fixed-points on a parameter is presented. In
Section 4, we present new ways of dealing with dynamic programming using the obtained fixed-point theorems.
Section 5 is dedicated to the conclusions.
2. Preliminaries
For completion purposes, we will present here how the above mentioned notions are defined.
For , we denote .
Definition 1 ([
7]).
Let be a vector space over (where is or ). A fuzzy norm is a function such that:(F1) , for all ;
(F2) , for all ] iff ;
(F3) , for all , all , and all ;
(F4) , for any , and any ;
(F5) For any is left continuous and .
The triple is called a fuzzy normed linear space (shortly FNLS).
In [
7], it is shown that
is a topological metrizable vector space with the topology given by
if there existsatisfying, with
A sequence is convergent to , denoted by or , if , for all .
Throughout this paper, a definition of Cauchy sequence, inspired by A. George and P. Veeramani [
6], is considered.
Definition 2 ([
6]).
A sequence in is called a G-V Cauchy sequence if A fuzzy normed space is G-V complete if any G-V Cauchy sequence in is convergent to a point from . A G-V complete fuzzy normed space is a fuzzy Banach space.
It is known (see [
25]) that a function
is continuous in the fuzzy sense on
, if
S is continuous between
and
.
Definition 3 ([
26]).
A function is a fuzzy contraction if One can observe that a fuzzy contraction is continuous in the fuzzy sense.
3. Some Fixed-Point Results in Fuzzy Normed Spaces
Following the introduction of fuzzy metric spaces and fuzzy normed spaces, several mathematicians have developed fixed-point theories in such a context. This work consist of generalizations, extensions, or even new fixed-point theorems such as those which can be found in [
14,
15,
16,
17,
18,
19,
20,
21,
22,
23,
24]. As we have already said, Grabiec [
4] defined the complete fuzzy metric space and extended the Banach contraction theorem in fuzzy metric spaces. Our first result is obtaining Banach’s contraction principle in FNLSs using G-V completeness, providing a new straightforward proof, using only the fuzzy norm’s definition. We remark that the existence part from the next theorem is proved in a more general context (see Lemma 5.1.3 [
12]).
Theorem 1. Given , a fuzzy Banach space, and a fuzzy contraction, S has a unique fixed-point and .
Proof. Let
. We will prove that
is a G-V Cauchy sequence, even if this proof is given in a more general context in [
12]. By induction, we obtain that
Let
. Observe that
Hence, by using
, it follows
As
, we have that
. Thus
Hence
is a G-V Cauchy sequence. Due to the fact that
is a fuzzy Banach space, there exists
such that
. One can observe that
For proving the uniqueness, we assume that there exists
,
with
. As
, there exists
such that
. Then, for any
, we have
Thus , contradicting our assumption. □
The next theorem generalizes the Banach’s contraction principle (for
) but also represents an extension in the context of FNLSs of a result obtained by G. Jungck [
27].
Theorem 2. Consider , a fuzzy Banach space where implies , for all . Let be two commuting operators satisfying:
- 1.
;
- 2.
;
- 3.
U or V is fuzzy continuous.
Then, U and V have a unique common fixed point.
Proof. Let . As , there exists . For , as , there exists , such that . We obtain, in this way, a sequence with the property .
- Step 1.
We prove that is a G-V Cauchy sequence.
By inductive reasoning, we obtain
Let
. We have that:
As , we have that . Thus So, is G-V Cauchy sequence in (a complete space). Therefore, with the property and .
- Step 2.
We note that . Indeed, if U is fuzzy continuous, as , we obtain that . If V is fuzzy continuous, as , we obtain that . Now, we have that
Thus
. As
we obtain that
.
- Step 3.
We will show that .
Since
U and
V commute, passing to the limit for
, we obtain
By induction, we obtain that
Letting , we obtain . Thus .
- Step 4.
We will show that .
As
, it results there exists
. We have that
Taking into account that
U and
V commute, for
, it results in
Thus . Finally, . Therefore .
- Step 5.
For uniqueness, consider . We have that
Applying the inductive method, Thus . □
Corollary 1. If is a fuzzy Banach space satisfying implies , and two commuting operators satisfying:
- 1.
;
- 2.
;
- 3.
U or V is continuous,
then both U and V have a unique common fixed-point.
Proof. It is obvious that commutes with V and . By previous theorem, it exists with . We have that . Thus, is a fixed-point for . In the same time . Thus, is a fixed point for V. As z is unique, we have , so . □
The next theorem is an application of fuzzy Banach’s contraction principle.
Theorem 3. Given , a fuzzy Banach space, where for any , is strictly increasing, and is fuzzy continuous with the property that then U owns a unique fixed-point.
Proof. For
,
, and
, it follows:
As , for , it results that . Using the same arguments such as in the proof of fuzzy Banach’s contraction principle, we deduce that U has a fixed point.
Now, suppose that
are two fixed-points for
U. Then, it follows
for some
and every
, whence
, for all
. Therefore,
, that completes the proof. □
Theorem 4. If is a fuzzy normed space such that is a continuous function for all and is fuzzy continuous such that it satisfies:
- 1.
,
- 2.
The closure of is compact and invariant to h,
then h has a unique fixed point . Moreover, if for any , the sequence converges, then its limit is ν, where represents the n-th iterate of h.
Proof. Fix
. From the hypothesis, the continuous function
attains its maximum at some
. If
, we have
which is impossible. Thus
is a fixed-point for
h.
If
are fixed-points of
h, then
which is impossible. Thus,
. Therefore,
is the unique fixed point of
h and also it is a fixed point for
, for all
.
Consider and . Since h is fuzzy continuous, it results in , whence v is a fixed point for h. However, is the unique fixed point of h hence . □
The following example proves the existence of a fuzzy continuous function on a fuzzy Banach space , which verifies the hypothesis in Theorem 4, and this fuzzy continuous function has the property that is not surjective, contrary to the case where h is a fuzzy contraction.
Example 1. In the space , we consider Let be defined by Then ρ is fuzzy continuous on , the closure of is a compact set invariant to ρ and , for all , and all .
Indeed, it is easy to see that
is fuzzy continuous on
, the closure of
is
and
. Now, let
and
. We have
Since
, it results that
. If
and
, then
As
, it follows that
. Finally, for
and
, we have
Since , we deduce , whence .
In the following theorem, the property of the continuous dependence of fixed points with respect to a parameter is presented.
Theorem 5. Let be a fuzzy Banach space, where is strictly increasing for any , and let ψ be a Hausdorff topological space. If has the properties:
- (1)
the mapping , is continuous for all ,
- (2)
there exists such that for any , any and any ,
then, for all , the mapping has a unique fixed point , and the function is continuous.
Proof. Since, for each
is a fuzzy contraction, it follows from Theorem 1 that
has a unique fixed point
. Consider
. We have
Now, taking into account the hypothesis about the fuzzy norm F, we deduce that , for any and any . We prove that is continuous. Let . As is continuous at , it follows that, for any and for every , there exists a neighborhood V of with the property , for each , whence, for any and for every , there exists a neighborhood V of , such that , for each . Therefore, is continuous at , whence is continuous on . □
Theorem 6. Let be a fuzzy Banach space, where, for any , is strictly increasing. If is fuzzy continuous, such that then ϕ has a fixed-point.
Proof. Suppose that
. Let
and
. For
, we have:
From and the hypothesis on F, it results
, for .
Thus, we deduce .
Similarly, for , we obtain .
Such as in the proof of Theorem 3, it results that has a fixed point. □
A similar result to the one given in Theorem 3 is pointed out.
Theorem 7. If, in a fuzzy Banach space, , is strictly increasing, for any , and if is fuzzy continuous such that , then ϕ has a unique fixed point.
Proof. Consider
and
. For
, we have
Since
, for
, it results that
Similar to the proof of Theorem 3, we deduce that
has a fixed point
. Now, if we assume that
and
, it follows that
Thus, Passing the limit for , it results that Therefore, . □
4. An Application of Fuzzy Banach Fixed-Point Principle to Dynamic Programming
There are a great number of processes that exist when ordering large amounts of goods in order to satisfy an unknown demand. Consider a process where the storage of a single type of goods is requested. After an order is placed and fulfilled, a request for that good is produced. This request is possibly satisfied, but, if demand is bigger than the goods in stock, a penalty cost is applied. Here, there are several constants and positive functions involved, such as: k-the minimum order cost to elevate the stock level, -the order cost of z units of goods for avoiding an excess z of the order amount in relation to stock, also known as “penalty cost”. Our purpose is to determine an order strategy for minimizing the overall probable cost by the means of solving a differential equation.
In dynamic programming, we come across functional equations of the form:
with
u being the state variable,
v the decision variable, and
h the optimum function.
In the study of these equations, we can use fixed-point theorems. As an example, we will investigate the following equation, known as the “equation the optimal distribution of supplies”:
where
Here, are given functions, , and h is the unknown function.
We will search for the existence of the solution to this equation in a subset of (the linear space of continuous functions on . For this purpose, we denote (the subspace of bounded functions from ).
We consider the Cebîşev norm:
and there the fuzzy norm
Then, forms a fuzzy normed space.
Now, we define the application
using:
In order for to be an invariant subset for A, we will make the following assumptions on the hypothesis of the problem:
and ;
, ;
with these conditions, from
it results that
is correctly defined. It remains to verify the contraction condition for
A. For that, consider the chain of relationships:
for all
,
. The case where the fuzzy norm
F is taken for
is trivial.
Hence, for
, we have
Now, if the operator A becomes a fuzzy contraction, then, from the Banach fuzzy fixed-point principle, the Equation has only one solution in that can be obtained by the successive approximation method, starting from any element from .