1. Introduction
The concept of metric space has been extensively studied in the literature, among other reasons, due to its usefulness in many fields of Science as Physics, Biology, Computer Science, … Indeed, it is an essential tool to quantify the proximity between objects in a real problem. Nevertheless, sometimes the nature of the problem under consideration requires a way of quantify such a proximity for which the concept of metric is too restrictive. This fact has motivated the introduction of different generalizations of the concept of metric by means of deleting or relaxing some of axioms which define it. Among others, we can find the quasi-metrics, in which the symmetry is not demanded, or the partial metrics, for those the self-distance is not necessary zero. These last ones were introduced by Matthews in [
1] where, in addition, he showed a duality relationship between them and a subclass of quasi-metrics, the so-called weighted quasi-metrics.
Coming back to the restrictiveness of the notion of metric space to be used in many real problems, sometimes the considered problem involves some uncertainty, which makes it more appropriate to provide a way of measuring the proximity between objects framed in the fuzzy setting. In this direction, George and Veeramani introduced, in [
2], a notion of fuzzy metric by slightly modifying a previous one given by Kramosil and Michalek in [
3]. This concept has been extensively studied by different authors, both from the theoretical point of view (see, for instance, Ref. [
4,
5,
6,
7,
8,
9,
10,
11,
12] and references therein) and by its applicability to engineering problems (see, for instance, Ref. [
13,
14,
15,
16,
17]). Different fuzzy concepts, based on the notion of fuzzy metric due to George and Veeramani, have appeared in the literature (see, for instance, Ref. [
9,
18,
19,
20]). In this direction, here we adopt the concept of fuzzy quasi-metric (see Definition 5) appeared in [
18], according to a modern concept of quasi-metric (see [
21]). Additionally, we adopt the concept of fuzzy partial metric (see Definition 6), defined by means of the residuum operator of a continuous 
t-norm, appeared in [
9], which, also, is according to the notion of partial metric.
The aim of this paper is to retrieve to the fuzzy setting the duality relationship between quasi-metrics and partial metrics defined on a non-empty set 
 that was established by Matthews in the classical case. To this end, we introduce a subclass of fuzzy quasi-metrics, the so-called fuzzy weighted quasi-metrics (see Definition 7). Subsequently, we provide a way to construct a fuzzy quasi-metric 
 on 
, from a given fuzzy partial metric space 
 (see Theorem 2). Furthermore, as in the classical case, we show that 
 (see Proposition 1), and also that 
 is weightable (see Theorem 4). On the other hand, to obtain the converse, we construct a fuzzy partial metric 
 on 
, from a given fuzzy weighted quasi-metric space 
 (see Theorem 3). Besides, we show that 
 (see Proposition 4). In both cases, we demand on the 
t-norm ∗ to be Archimedean. The consistency of our constructions is detailed in Remarks 2 and 3. Several examples are provided for illustrating the theory. It is worth to mentioning that a part of the content of the paper is included in the PhD dissertation of the third author [
22].
The reminder of the paper is organized as follows. 
Section 2 compiles the basics used throughout the paper. Subsequently, 
Section 3 is devoted to obtain a fuzzy quasi-metric deduced from a fuzzy partial one in such a way that the topology is preserved and, in 
Section 4 is approached the conversely case. In 
Section 5 a brief discussion is provided. Finally, 
Section 6 exposes the conclusions of the present work and some lines of research to continue it.
  2. Preliminaries
We begin recalling the notion of quasi-metric space that we manage throughout this paper (see [
18,
21]).
Definition 1. A quasi-metric space is a pair  where  is a non-empty set, and  is a mapping such that, for each , the following conditions are satisfied:
- (Q1) 
  if and only if  for every .
- (Q2) 
 .
As usual, we also say that q is a quasi-metric on .
 In a similar way that a metric, given a quasi-metric space , then q induces a  topology  on , which has as a base the family of open balls , where , for each .
We continue recalling the notion of partial metric space introduced by Matthews in [
1].
Definition 2. A partial metric space is a pair  where  is a non-empty set, and  is a mapping such that, for each , the following conditions are satisfied:
- (P1) 
  if and only if .
- (P2) 
 .
- (P3) 
 .
- (P4) 
 .
Again, we also say that p is a partial metric on .
 Besides, Matthews showed in [
1] that a partial metric 
p on a non-empty set 
 induces a 
 topology 
 on 
 which has as a base the family of open balls 
, where 
, for each 
.
In addition, Matthews showed a duality relationship between partial metrics and quasi-metrics. Such a relationship is given by the fact that, from each partial metric 
p on a non-empty set 
 we can construct a quasi-metric 
 on 
 defining 
, for each 
. In order to obtain a similar construction in the converse case, Matthews introduced, in [
1], the following notion of weighted quasi-metric space.
Definition 3. A weighted quasi-metric space is a tern , where q is a quasi-metric on  and w is a function defined on  satisfying, for each , the following conditions:
- (w1) 
 ;
- (w2) 
 .
 Subsequently, Matthews established a way to construct a partial metric from a given weighted quasi-metric space , defining a partial metric  on  given by , for each .
Moreover, Matthews showed that both constructions preserve the topology. Indeed, given a partial metric space  then, . Conversely, given a weighted quasi-metric space  then, .
Now, we recall the notion of fuzzy metric space given by George and Veeramani in [
2].
Definition 4. A fuzzy metric space is an ordered triple  such that  is a (non-empty) set, ∗ is a continuous t-norm (see [23]) and M is a fuzzy set on  satisfying, for all  and , the following conditions: - (GV1) 
 - (GV2) 
  if and only if 
- (GV3) 
 - (GV4) 
 - (GV5) 
 The assignment , given by  for each , is a continuous function.
As usual, we will say that , or simply M, if confusion does not arise, is a fuzzy metric on .
 George an Veeramani showed in [
2] that every fuzzy metric 
M on 
 defines a topology 
 on 
X, which has as a base the family of open balls 
, where 
 for all 
, 
 and 
.
In the next, we recall two significant examples of fuzzy metrics given in [
2].
Example 1. Let  be a metric space and let  the function on  defined by Then,  is a fuzzy metric space, where  denotes the minimum t-norm (i.e., , for each ).  is called the standard fuzzy metric induced by d. The topology  coincides with the topology  on  deduced from d.
 Example 2. Let  be a metric space and let  the function on  defined by Afterwards,  is a fuzzy metric space and  will be called the exponential fuzzy metric induced by d. Again, the topology  coincides with the topology  on  deduced from d.
 Gregori and Romaguera in [
18] introduced two concepts of fuzzy quasi-metric. Here, we deal with the following concept which is according to Definition 1.
Definition 5. A fuzzy quasi-metric space is a tern , such that  is a non-empty set, ∗ is a continuous t-norm and Q is a fuzzy set on  satisfying, for all  and , the following conditions:
- (QGV1) 
 ;
- (QGV2) 
  if and only if ;
- (QGV3) 
 ;
- (QGV4) 
 The assignment , given by  for each , is a continuous function.
In such a case, , or simply Q, is called a fuzzy quasi-metric on .
 Gregori and Romaguera proved in [
18] that every fuzzy quasi-metric 
Q on 
 generates a 
 topology 
 on 
 that has as a base the family of open sets of the form 
, where 
 for all 
, 
 and 
.
Now, we recall the concept of fuzzy partial metric space introduced by Gregori et al. in [
9].
Definition 6. Afuzzy partial metric spaceis an ordered triple , such that  is a (non-empty) set, ∗ is a continuous t-norm and P is a fuzzy set on  satisfying, for all  and , the following conditions:
- (FPGV1) 
 - (FPGV2) 
  if and only if 
- (FPGV3) 
 - (FPGV4) 
 - (FPGV5) 
 The assignment , given by  for each , is a continuous function.
 Similarly to the previous cases, Gregori et al. proved in [
9] that that every fuzzy partial metric 
P on 
 generates a 
 topology 
 on 
 which has as a base the family of open sets of the form 
, where 
 for all 
, 
 and 
.
In the previous definition, 
 denotes the residuum operator of the continuous 
t-norm ∗ (see, for instance, [
24] in order to find a deeper treatment on it), which can be obtained by next formula:
To finish this section, we recall some aspects on continuous t-norms and their residuum operator, which will be useful later.
First, recall that an additive generator 
 of a 
t-norm ∗ is a strictly decreasing function which is right-continuous at 0, satisfying 
, and such that for 
 we have
      
      and also
      
      where 
 denotes the pseudo-inverse of the function 
 (see [
24]).
This concept allows for characterizing a family of continuous t-norms, the so-called Archimedeans (i.e., those continuous t-norms ∗ such that  for each ) as shows the next theorem.
Theorem 1. A binary operator ∗ in  is a continuous Archimedean t-norm if and only if there exists a continuous additive generator  of ∗.
Moreover, an additive generator 
 of a continuous Archimedean 
tnorm ∗ allows us to obtain a simpler formula of the ∗-residuum, as follows:
          
Note that the pseudo-inverse of a continuous additive generator 
 is given by
          
By Formula (
6), we conclude that, for each continuous Archimedean t-norm ∗, it is held 
 for each 
.
        
 Remark 1. Attending to Formula (6), it is obvious that given a continuous Archimedean t-norm ∗, its ∗-residuum is continuous on . Nevertheless, the such an affirmation is not true, in general. Indeed, the residuum operator of the non-Archimedean continuous t-norm  is given byand one can easily observe that  is not continuous on .  Corollary 1. Let ∗ be a continuous Archimedean t-norm, and let  be its continuous additive generator. Then, for every , we have that .
   3. From Fuzzy Partial Metrics to Fuzzy Quasi-Metrics
In this section, we provide a way of constructing a fuzzy quasi-metric from a fuzzy partial metric. To obtain such an aim, we are based on the classical techniques given by Matthews in [
1].
We begin this section introducing two examples of fuzzy quasi-metric spaces. They generalize, in some sense, the exponential and standard fuzzy metric spaces deduced from a classic metric (see 
Section 2). Both examples will be useful later.
Example 3. Let  be a quasi-metric space.
- (i) 
 We define the fuzzy set  on , as follows After a tedious computation, one can prove that  is a fuzzy quasi-metric space. It will be called the exponential fuzzy quasi-metric space deduced from q.
- (ii) 
 We define the fuzzy set  on  as Afterwards,  is a fuzzy quasi-metric space (see [18]), where  denotes the usual product t-norm (i.e.,  for each ). It is left to the reader to show that  is also a fuzzy quasi-metric space. 
Observe that both  and  are also fuzzy quasi-metric spaces for each continuous t-norm ∗, since  for each t-norm ∗.
 Now, we show the next theorem.
Theorem 2. Let  be a fuzzy partial metric space, where ∗ is a continuous Archimedean t-norm. Then,  is a fuzzy quasi-metric space, where  is the fuzzy set on  given by:for each   Proof.  We will see that  fulfills Definition 5.
            
- (QGV1) 
 As , then . Hence, .
                
- (QGV2) 
  implies that  and  for each . Hence,  and . Therefore,  and . On the other hand, if  for some , we have that . Hence, as  and , we have that  for some , and so .
                
- (QGV3) 
 It is straightforward due to axiom (PGV4).
                
- (QGV4) 
 By axiom (FPGV5) we have that  and  are continuous functions on  Furthermore, since , on account of Remark 1 we conclude that  is a continuous function due to it is a composition of continuous functions.
Hence,  is a fuzzy quasi-metric space. □
 We cannot remove the condition of being ∗ Archimedean in the previous theorem, as the next example shows.
Example 4. Let . We define the fuzzy set P on  as In [9], the authors proved that  is a fuzzy partial metric space. Nevertheless, if we define the fuzzy set  on  by , for each  and , then  does not satisfy axiom . Indeed, on account of Example 4.2 of [9], we have that Obviously,  is not a continuous function.
 We illustrate the construction presented in Theorem 2 applying it to some particular cases of fuzzy partial metric space.
Example 5. Let  be a partial metric space. First, recall that, following the Matthews’ construction we have that  is a quasi-metric on , where  for each .
- (i) 
 By Proposition 3.3 in [9],  is a fuzzy partial metric space, where , for each . Since  is a continuous Archimedean t-norm then, by Theorem 2, we have that  is a fuzzy quasi-metric space, where  is given byfor each . It will be called the exponential fuzzy partial metric deduced from p. Recall that an additive generator of  is  given by , for . So, on account of Formula (6) we have, for each , that Then, for each , we obtain Thus, , for each .
- (ii) 
 By Proposition 3.4 in [9],  is a fuzzy partial metric space, where , for each , and  denotes the Hamacher product t-norm, which is given by the following expression:for each . It will be called the standard fuzzy partial metric deduced from p. In [24], it was pointed out that the function  is an additive generator of  and so, on account of Formula (7), the function  is its pseudo-inverse. Attending to these observations and taking into account Formula (6), the expression of the -residuum is given by Because  is a continuous Archimedean t-norm then, by Theorem 2, we have that  is a fuzzy quasi-metric space, where  is given byfor each . On account of Formula (18) we have, for each , that Thus, , for each .
 Example 6. Let  and consider the partial metric  defined on , where  for each . We define the fuzzy set  on  given by It is left to the reader to show that  is a fuzzy partial metric space, where  denotes the Lukasievicz t-norm, which is given by .
Recall that an additive generator of  is  given by  for each . Accordingly, on account of Formula (6), the residuum operator of  is given by Taking into account that  is a continuous Archimedean t-norm then, by Theorem 2 we conclude that  is a fuzzy quasi-metric space, where  is given byfor each  and . By Formula (23) we have, for each  and , that Thus, , for each  and .
 Remark 2. Observe, in the previous examples, that we obtain the same fuzzy quasi-metric, both if we construct the exponential (or standard) fuzzy quasi-metric deduced from  and if we construct the fuzzy quasi-metric from the exponential (or standard) fuzzy partial metric deduced from p using Theorem 2. This fact shows, in some sense, the consistence of the construction provided in Theorem 2 when comparing with the classical one.
 To finish this section, we will show that the topology induced by a fuzzy partial metric coincides with the topology induced by the fuzzy quasi-metric constructed from it by means of Theorem 2.
Proposition 1. Let  be a fuzzy partial metric, where ∗ is a continuous Archimedean t-norm. Afterwards, , where  is the fuzzy quasi-metric on  constructed from P given in Theorem 2.
 Proof.  Let 
 be a fuzzy partial metric, where ∗ is a continuous Archimedean t-norm. Taking into account Remark 4.1 in [
9], we have that, for each 
, 
 and 
, the open balls are defined, as follows:
              
It ensures that 
 if and only if 
. Indeed,
              
Hence, . □
   4. From Fuzzy Quasi-Metrics to Fuzzy Partial Metrics
In this section, we tackle the conversely of the construction provided in 
Section 3, i.e., we establish a way to construct a fuzzy partial metric from a fuzzy quasi-metric. To achieve such a goal, we begin introducing a notion of fuzzy weighted quasi-metric adapting the classical notion of weighted quasi-metric to our fuzzy context. Besides, some axioms have been added in order to maintain the “essence” of the George and Veeramani’s fuzzification.
Definition 7. We will say that  is a fuzzy weighted quasi-metric space, provided that  is a fuzzy quasi-metric space and W is a fuzzy set on , satisfying, for each , , the following properties:
- (WGV0) 
 ;
- (WGV1) 
 .
- (WGV2) 
 The assignment , given by  for each , is a continuous function.
In such a case, the fuzzy set W will be called the fuzzy weight function associated to the fuzzy quasi-metric space .
 Moreover, we will say that a fuzzy quasi-metric space  is weightable if there exist a weight function  satisfying axioms (WGV0)–(WGV2).
          
After introducing the previous concept we provide, in the next two propositions, examples of fuzzy weighted quasi-metric spaces.
Proposition 2. Let  be a weighted quasi-metric space. Then,  is a fuzzy weighted quasi-metric space, whereand  is the Hamacher product t-norm.  Proof.  On account of Example 3 (ii), we deduce that  is a fuzzy quasi-metric space. Accordingly, we just need to show that  satisfies, for each  and , axiom (WGV1), since, by definition of , it is not hard to check that (WGV0) and (WGV2) are held,
Let 
 and 
. On the one hand,
              
Because  is a weighted quasi-metric space, then  and so .  □
 Following similar arguments to the ones used in the preceding proof, one can show the next proposition.
Proposition 3. Let  be a weighted quasi-metric space. Subsequently,  is a fuzzy weighted quasi-metric space, whereand  is the usual product t-norm.  On account of Definition 7, one can observe that W is defined on  according to the George and Veeramani’s context. The following theorem states a way to obtain a fuzzy partial metric from a fuzzy weighted quasi-metric.
Theorem 3. Let  be a fuzzy weighted quasi-metric space, where ∗ is a continuous Archimedean t-norm. afterwards,  is a fuzzy partial metric space, where  is the fuzzy set on , given by:  Proof.  We will show that every axiom of Definition 6 is satisfied, for each  and .
            
- (PGV1) 
 Let  and . On the one hand, since W is a fuzzy weight function, axiom (WGV0) ensures that . On the other hand, . Thus, 
- (PGV2) 
 Obviously,  implies .
Now, suppose that 
 for some 
, 
. Afterwards, on the one hand,
                  
Besides, because W is a fuzzy weight function, axiom (WGV1) ensures that . So, .
Because ∗ is an Archimedean t-norm and,  and , then . Thus, axiom (QGV2) implies .
- (PGV3) 
 Let 
. Because W is a fuzzy weight function, by axiom 
(WGV1), we have that
                  
- (PGV4) 
 Let  and . We will see that the following holds:
To show it, we claim that , for each  and .
Fix 
 and 
. First, since ∗ is a continuous Archimedean t-norm, there exists an additive generator 
 of ∗. Subsequently, using equality (
6) and taking into account that 
 for each 
, we have that
                  
Observe that  since . Indeed, if we suppose that  then , a contradiction.
Therefore, .
- (PGV5) 
 The function  is continuous because of the continuity of both  and , and the continuity of the t-norm ∗.
Hence,  is a fuzzy partial metric space. □
 In the next example we will show that the assumption on the t-norm, which has to be Archimedean, cannot be removed in Theorem 3. For that purpose, we introduce the following lemma.
Lemma 1. Let  be a fuzzy metric space, where ∗ is a continuous integral t-norm (i.e.,  if and only if ). Then, for every (fixed) ,  is a fuzzy weighted quasi-metric space, where  and .
 Proof.  Let  be a fuzzy metric space, where ∗ is a continuous integral t-norm, and let . Obviously, every  is a fuzzy quasi-metric space. Accordingly, we need to prove that  is a fuzzy weight function.
            
- (WGV0) 
 Suppose that  for some  and . Because ∗ is integral, our assumption implies that  or , which is a contradiction. Hence, .
- (WGV1) 
 Let  and . By axiom (GV3), we have that , so .
- (WGV2) 
 Obviously, for each  the assignment  is a continuous function on , since  for each .
 □
 Now, the previous lemma allows for us to introduce the announced (counter) example.
Example 7. Let  be the metric space, where  and  is the usual metric of  restricted to .
Consider the stantard fuzzy metric  deduced from , where  is the minimum t-norm (see [25]) and Then, since  is an integral t-norm  is a fuzzy weighted quasi-metric space by Lemma 1, where  for each . Let ,  and . We have that If we define , then  does not fulfill axiom (PGV2). Indeed, as it has been shown,  but .
 In the following example, we introduce two fuzzy partial metrics using Proposition 2 and 3 and Theorem 3.
Example 8. Let  be a weighted quasi-metric space. Following the Matthews’ construction, we have that  is a partial metric on , where  for each .
- (i) 
 By Proposition 2,  is a fuzzy weighted quasi-metric space, whereand  is the Hamacher product t-norm. Since  is a continuous Archimedean t-norm then, by Theorem 3, we have that  is a fuzzy partial metric space, where  is given byfor each . Then, for each , we have that Thus, , for each .
- (ii) 
 By Proposition 3,  is a fuzzy weighted quasi-metric space, whereand  is the usual product t-norm. Since  is a continuous Archimedean t-norm then, by Theorem 2, we have that  is a fuzzy partial metric space, where  is given byfor each . Then, for each , we have that Thus, , for each .
 Remark 3. Again, the previous example shows the consistence of the construction provided in Theorem 3 comparing with the classical one. Indeed, we obtain the same fuzzy partial metric, both if we construct the exponential (or standard) fuzzy partial metric deduced from  and if we construct the fuzzy partial metric from the exponential (or standard) fuzzy quasi-metric deduced from q while using Theorem 3.
 Moreover, the next proposition shows that the topology induced by a fuzzy weighted quasi-metric coincides with the topology induced by the fuzzy partial metric constructed from it applying Theorem 3.
Proposition 4. Let  be a fuzzy weighted quasi-metric space, where ∗ is a continuous Archimedean t-norm. Then, , where  is the fuzzy partial metric on  constructed from Q given in Theorem 3.
 Proof.  Let 
 be a fuzzy quasi-metric space, where ∗ is a continuous Archimedean t-norm. On the one hand, for each 
, 
 and 
, we have that
              
On the other hand, by Proposition 1 and Remark 4.1 in [
9] we have that
              
			  for each 
, 
 and 
.
Moreover, in the demonstration of Theorem 3, . Thus, it is obvious that, for each ,  and ,  if and only if . Hence, . □
 To finish this section, we tackle a question related with the construction given in Theorem 2. In such a theorem, we provide a way of obtaining a fuzzy quasi-metric from a fuzzy partial one. It is based on the results given by Matthews in [
1] for the classical case. Taking into account that, in the construction of Matthews, the obtained quasi-metric from a partial one turns out to be weightable, we wonder if it is so in the fuzzy context. The next theorem affirmatively answers such a question.
Theorem 4. Let  be a fuzzy partial metric space, where ∗ is a continuous Archimedean t-norm. Then,  is a fuzzy weighted quasi-metric space, whereand  Proof.  Let  be a fuzzy partial metric space, where ∗ is a continuous Archimedean t-norm. Theorem 2 ensures that  is a fuzzy quasi-metric space. So, we just need to show that  satisfies, for each  and , axioms (WGV0)–(WGV2).
First, observe that ∗ is a continuous Archimedean t-norm, so there exists a continuous additive generator  of ∗ Now, fix  and
			  :
			  
- (WGV0) 
  By definition of additive generator and taking into account Formula (6), since
                             
 by axiom 
(PGV1), we have that
			  
Hence,
                        
- (WGV1) 
 As it was exposed above,
						 
			  Analogously,
			   By axiom (PGV3), we have that  Therefore, .
- (WGV2) 
 By axiom (PGV5), we deduce that the assignment  is a continuous function. Thus, because  for each  then, the assignment  is a continuous function.
Hence,  is a fuzzy weighted quasi-metric space. □