- Research Article
- Open Access
Equivalent Extensions to Caristi-Kirk's Fixed Point Theorem, Ekeland's Variational Principle, and Takahashi's Minimization Theorem
© The Author(s). 2010
- Received: 26 September 2009
- Accepted: 24 November 2009
- Published: 9 December 2009
With a recent result of Suzuki (2001) we extend Caristi-Kirk's fixed point theorem, Ekeland's variational principle, and Takahashi's minimization theorem in a complete metric space by replacing the distance with a -distance. In addition, these extensions are shown to be equivalent. When the -distance is l.s.c. in its second variable, they are applicable to establish more equivalent results about the generalized weak sharp minima and error bounds, which are in turn useful for extending some existing results such as the petal theorem.
- Fixed Point Theorem
- Error Bound
- General Distance
- Multivalued Mapping
- Nondecreasing Function
Let be a complete metric space and a proper lower semicontinuous (l.s.c.) bounded below function. Caristi-Kirk fixed point theorem [1, Theorem ] states that there exists for a relation or multivalued mapping if for each with there exists such that
EVP has been shown to have many equivalent formulations such as Caristi-Kirk fixed point theorem, the drop theorem , the petal theorem [3, Theorem ], Takahashi minimization theorem [7, Theorem ], and two results about weak sharp minima and error bounds [8, Theorems and ]. Moreover, in a Banach space, it is equivalent to the Bishop-Phelps theorem (see ). EVP has played an important role in the study of nonlinear analysis, convex analysis, and optimization theory. For more applications, EVP and several equivalent results stated above have been extended by introducing more general distances. For example, Kada et al. have presented the concept of a -distance in  to extend EVP, Caristi's fixed point theorem, and Takahashi minimization theorem. Suzuki has extended these three results by replacing a -distance with a -distance in . For more extensions of these theorems, with a -distance being replaced by a -function and a -function, respectively, the reader is referred to [12, 13].
Theoretically, it is interesting to reveal the relationships among the above existing results (or their extensions). In this paper, while further extending the above theorems in a complete metric space with a -distance, we show that these extensions are equivalent. For the case where the -distance is l.s.c. in its second variable, we apply our generalizations to extend several existing results about the weak sharp minima and error bounds and then demonstrate their equivalent relationship. In particular, when the -distance reduces to the complete metric, our results turn out to be equivalent to EVP and hence to its existing equivalent formulations.
Definition 2.1 (see ).
are -distances on . Note that for each with . For more examples, we see .
Definition 2.3 (see ).
From now on, we assume that is a complete metric space and is a proper l.s.c. and bounded below function unless specified otherwise. In this section, mainly motivated by fixed point theorems (for a single-valued mapping) in [10, 11, 14–16], we present two similar results which are applicable to multivalued mapping cases. The following theorem established by Suzuki's in  plays an important role in extending existing results from a single-valued mapping to a multivalued mapping.
Theorem 3.1 (see [11, Proposition ]).
Based on Theorem 3.1, [11, Theorem ] asserts that a single-valued mapping has a fixed point in when holds for all (which generalizes [10, Theorem ] by replacing a -distance with a -distance). We show that the conclusion can be strengthened under a slightly weaker condition (in which holds on a subset of instead) for a multivalued mapping
Clearly, [8, Thoerem ] follows as a special case of Theorem 3.2 with . In addition, when and is a single-valued mapping, Theorem 3.2 contains [11, Theorem ]. The following simple example further shows that Theorem 3.2 is applicable to more cases.
Next result is immediate from Theorem 3.4.
Thus the conclusion follows from Theorem 3.4.
When and is a single-valued mapping, Theorem 3.4 reduces to [16, Theorem ] while Theorem 3.5 to [16, Theorems and ]. If also for all , then Theorem 3.5 reduces to [14, Theorem ] (when is nondecreasing) and [15, Theorem ] (when is upper semicontinuous). In the later case, it also extends [14, Theorem ].
Furthermore, we will see that the relaxation of from a single-valued mapping (as in several existing results stated before) to a multivalued one (as in Theorems 3.2–3.5) is more helpful for us to obtain more results in the next section.
As applications of Theorems 3.4 and 3.5, several generalizations of EVP will be presented in this section.
satisfies the condition in Theorem 4.1 when is a nondecreasing or u.s.c. function. So, based on Theorem 4.1 or Theorem 3.5, we obtain next result (from which [11, Theorem ] follows by taking ).
Similar to the proof of Theorem 4.1, the first part of the conclusion can be derived from Theorem 3.5.
and , we arrive at the following conclusion, from which (by taking ) we can obtain [17, Theorem ], a generalization of EVP.
We have obtained Theorem 4.5 from Theorem 4.1. Conversely, when is a -distance, Theorem 4.1 follows from Theorem 4.5 by taking for all . In this case they are equivalent results. If also holds for some and all , Theorem 4.5 is obviously applicable. In particular, when we take for certain point , the condition in Theorem 4.5 about can be deleted.
Upon taking and in Theorem 4.7 and replacing with , we obtain (ii) of [10, Theorem ], which is also an extension to EVP.
In this section we mainly apply the extensions of EVP obtained in Section 4 to establish minimization theorems which generalize [11, Theorem ] (an extension to [10, Theorem ] and [7, Theorem ]). From these results we also derive Theorem 3.2. Consequently, seven theorems established in Sections 3–5 are shown to be equivalent.
Firstly, we use Theorem 4.1 to prove the following result.
Similarly, we can use Theorem 4.2 to establish the following result.
As a conclusion in this paper, the following result states that these seven theorems are equivalent.
Theorems 3.2–3.5, 4.1-4.2, and 5.1-5.2 are all equivalent.
By Remark 5.4, it suffices to show that Theorems 5.1-5.2 both imply Theorem 3.2.
The condition in Theorem 5.2 is sufficient for to attain minimum on . In this section we show that such a condition implies more when the -distance (on ) is l.s.c. in its second variable. For convenience we introduce the following notions.
When , the above concepts, respectively, reduce to -condition of Takahashi Hamel and the condition of Takahashi Hamel in .
It is clear that for any the generalized -condition of Takahashi implies the generalized -condition of Takahashi and the generalized -condition of Hamel implies the generalized -condition of Hamel. For any the generalized -condition of Takahashi and the generalized -condition of Hamel are, respectively, weaker than that of Takahashi and of Hamel. For example, when , the function satisfies the generalized -conditions of Takahashi and Hamel for any but it does not satisfy that of Takahashi nor of Hamel. Furthermore, the generalized -condition of Hamel always implies that of Takahashi. Next result asserts that the converse is also true in a complete metric space.
Next, we suppose that satisfies the generalized condition of Takahashi. For each , the function satisfies the generalized -condition of Takahashi, so satisfies the generalized -condition of Hamel. This implies that is nonempty. For each with , if , then for some In this case we can find such that
As stated in , the -condition of Takahashi is one of sufficient conditions for an inequality system to have weak sharp minima and error bounds. With Theorem 6.2 being established, the generalized -condition of Takahashi plays a similar role for the generalized weak sharp minima and error bounds introduced below.
For a proper l.s.c. and bounded below function we say that has generalized local(global) weak sharp minima if the set of minimizers of on is nonempty and if for some and some nondecreasing function and each with there holds
The proof is immediate from Theorem 6.2.
When and , the study of generalized error bounds has received growing attention in the mathematical programming (see  and the references therein). Now, using Theorem 7.1, we present the following sufficient condition for an l.s.c. inequality system to have generalized error bounds.
When and is a -distance such that is l.s.c. on for each , we can obtain Theorem 3.1 by applying Theorem 7.4 to the function . As more applications, the following two propositions are immediate from Theorem 7.4 by taking , and , respectively, on .
Upon taking in Propositions 7.6 and 7.7, we obtain [3, Theorem ] which is equivalent to EVP in a complete metric space. In this case EVP implies Theorem 3.1.
Finally, following the statement in Theorem 5.5, on the condition that the -distance is l.s.c. on for each , Theorems 3.1–3.5, 4.1-4.2, 5.1-5.2, 6.2, and 7.1–7.4 turn out to be equivalent since we have further shown that
in Sections 6 and 7. In particular, each theorem stated above is equivalent to Theorem 4.5 (as stated in Remark 4.6) when is a -distance on , to [3, Theorem ] and EVP when (see Remark 7.8), and to the Bishop-Phelps Theorem in a Banach space when is the corresponding norm. Therefore, we can conclude our paper as below.
(i)Theorems 3.1–3.5, 4.1-4.2, 5.1-5.2, 6.2, and 7.1-7.4 are all equivalent;
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