Abstract
This paper establishes new fixed-point theorems in the framework of complete p-normed spaces, where . By extending the classical Banach, Schauder, and Krasnosel’skii fixed-point theorems, we derive several results for the sum of contraction and compact operators acting on s-convex subsets. The analysis is further generalized to multivalued upper semi-continuous operators by employing Kuratowski and Hausdorff measures of noncompactness. These results lead to new Darbo–Sadovskii-type fixed-point theorems and global versions of Krasnosel’skii’s theorem for multifunctions in p-normed spaces. The theoretical findings are then applied to demonstrate the existence of solutions for nonlinear integral equations formulated in p-normed settings. A section on numerical applications is also provided to illustrate the effectiveness and applicability of the proposed results.
Keywords:
p-normed space; fixed-point theorem; contraction operator; multivalued operator; measure of noncompactness; Darbo–Sadovski theorem; Krasnosel’skii theorem; nonlinear integral equation; s-convexity; existence results; numerical applications MSC:
65M70; 47H10; 54H25; 46A16
1. Introduction
The theory of fixed points (s) forms a central and rich component of modern mathematics, drawing on ideas from analysis, topology, and geometry. Over the past five decades, methods have become indispensable in the study of nonlinear phenomena and now play a key role across numerous branches of mathematical sciences. They provide effective tools for establishing the solvability of integral equations, systems of linear equations, the existence of periodic trajectories in dynamical systems, economic equilibrium models, and solutions of ordinary and partial differential equations.
theory for multivalued operators represents an essential direction within set-valued analysis. Many classical results for single-valued mappings such as those due to Banach and Schauder have been successfully extended to multivalued operators in Banach spaces; see the monographs by Gorniewicz and co-authors [1,2]. More recently, Boriceanu [3] and Petrusel [4] established multivalued variants of Krasnosel’skii’s theorem. In 2011, Xiao and Zhu [5] have studied the existence of s for different types of operators defined on s-convex sets in p-normed spaces with , and extended some well known results to s-convex sets. In 2012, Alghamdi et al. [6] have shown Krasnosel’skii’s type of theorems and Sadovskii-type theorem for s-convex sets in p-normed spaces with , . In 2018, Xiao and Lu [7], by using a concept of measure of noncompactness, have obtained some theorems for single-valued and multivalued operators defined on s-convex sets in p-norm spaces with , . The class of p-normed spaces is considered an important generalization of the class of usual normed spaces as cited in many results on the study of these topics [8]. Several authors have further generalized the classical Krasnosel’skii theorem in various directions [9,10,11,12,13]. The purpose of this work is to develop new multivalued extensions of the Krasnosel’skii theorem in p-normed spaces.
A wide range of problems in applied sciences reduce, after mathematical formulation, to investigating the solvability of nonlinear differential equations or inclusions of the form
and
where F is a multivalued operator and is a closed, convex subset of a Banach space . Many integral equations and inclusions can be recast in the form of (1) or (2); see, for instance, [14]. In 1958, Krasnosel’skii [15] proved that (1) admits a solution in , provided that and satisfy the following:
- (C1)
- for all ;
- (C2)
- is continuous on and is compact in ;
- (C3)
- is a -contraction.
This theorem links the Banach contraction principle with Schauder’s theorem. The problem of locating s for the sum of two operators has drawn considerable attention because of its extensive applications in nonlinear analysis. Numerous refinements and extensions of the Krasnosel’skii theorem have been proposed by relaxing or modifying the above assumptions; see, for example [9,10,11,12,13,16,17,18,19,20,21,22].
In this work, we establish what seems to be the first systematic framework for fixed-point theorems of the Krasnosel’skii type for multivalued operators in full p-normed spaces, where . Our goal is to apply the traditional “compact + contraction” approach to the operator sum , where G might be contraction, nonexpansive, expansive, or condensing, and F is an upper semi-continuous multivalued mapping with s-convex values. Many of the traditional methods from Banach space theory are no longer applicable because the analysis is conducted in a non-locally convex environment. In order to overcome these challenges, we establish selection results appropriate for this context, modify measurements of noncompactness to the structure of p-normed spaces, and present new approximation techniques. These elements enable us to build a unified theory for operator equations in spaces like for . Additionally, we provide explicit applications to nonlinear integral equations, which are complemented by numerical findings that show how applicable the theoretical framework is in real-world situations.
2. Preliminaries
In this section, we introduce the notation and recall of several basic concepts used throughout the paper. Let be a vector space over , endowed with the zero element , where denotes either or .
A non-negative functional , with , is called a p-norm if it satisfies the following:
- (P1)
- if and only if ;
- (P2)
- for all and ;
- (P3)
- for all .
When , the pair reduces to a classical normed space. For any subset , we denote its interior, closure, and boundary by , , and , respectively. The sphere of radius centered at is written as
The set represents the natural numbers, and for any and , the open ball centered at with radius is denoted by .
Let . A set is said to be s-convex if
A point satisfying , where , , and , is called an s-convex combination of . A set is s-convex precisely when it contains every such s-convex combination of its elements. In particular, each ball is s-convex for .
Proposition 1
([23]). Let . Let be s-convex. Then, for any linear operator .
The difference between the shapes of convex and s-convex hulls is given in the following example.
Example 1
([23,24]). Let , with being linearly independent in and . By Proposition 1, suppose that (the canonical basis). This set, , can be viewed as the image of
under the operator given by , where .
Figure 1 illustrates two different shapes of when it is equal to (blue shaded area) and when it is equal to (white area).
Figure 1.
Illustration of the difference between the convex and s-convex sets.
Lemma 1
([25]). Suppose that x is a point of a p-normed space , then the s-convex hull and the closure of the s-convex hull of , where , , are given by
and
Remark 1
([5]).
- 1.
- If , one obtains the usual definition of convex sets.
- 2.
- In a p-normed space, there is a significant difference between a convex set and a s-convex set. A s-convex set is not translation-invariant in the case . If is s-convex and , then is not s-convex in general.
- 3.
- If is a closed s-convex set , then .
Lemma 2
([25,26]). Let be a p-normed space with and let . Then,
- (a)
- is s-convex for any .
- (b)
- If is s-convex and , then is s-convex.
- (c)
- If and are s-convex subsets of , then is s-convex.
- (d)
- If is a family of s-convex sets, then is s-convex.
- (e)
- If and , then , where denotes the convex hull of .
- (f)
- If is a closed s-convex set and , then is also a closed r-convex set.
Let be an s-convex subset of containing the origin. The mapping defined by
is called the Minkowski s-functional associated with . For any , we write
If is a closed s-convex set with , then it necessarily contains the origin ; see [5,7].
We recall the following notions:
- -
- Distance from a point to a set :
- -
- The excess functional , defined byIn general, .
- -
- Pompeiu–Hausdorff metric: , given byAn equivalent expression isThe pair forms a metric space.
We frequently identify a point with the singleton . Thus, we may write , , , and .
It is known that
and
We now recall some additional definitions used throughout the paper.
Let and be metric spaces, and let be a multivalued operator. For any , we write
A single-valued mapping is called a selection of F, denoted , if for all .
The operator is called compact (or quasicompact) if, for every bounded (respectively, relatively compact) subset , the set is relatively compact. When Y is a p-normed space, the operator F is said to be s-convex-valued if is an s-convex set for every .
The operator F is upper semi-continuous on if, for each , the set is nonempty, and for every nonempty open set containing , ∃, an open neighborhood of such that . Equivalently, the set
is open for every open . A dual characterization is that, for each nonempty closed set , the set
is closed in .
Definition 1
([27]). A multivalued operator F is sequentially upper semi-continuous at if, for every sequence converging to and every sequence with for all n, we have
When both and Y are metric spaces, upper semi-continuity is equivalent to sequential upper semi-continuity.
If is a nonempty set and is a multivalued operator, a point is called a of F if . The corresponding set is
The graph of F is defined by
A multivalued operator F is said to be closed if its graph is a closed subset of .
Lemma 3
([27]). If a multivalued operator F is sequentially upper semi-continuous, then it is closed.
Under an additional compactness assumption on the space Y, closedness implies sequential upper semi-continuity.
Theorem 1.
([27]). If the space Y is compact, then a multivalued operator F is sequentially upper semi-continuous if and only if it is closed.
A classical fact concerning multivalued mappings with closed graphs is the following.
Lemma 4.
Let be a space and be a multivalued operator for which is closed in . Then, for every .
Proof.
Fix and let be a sequence such that in . Then,
and in . Since is closed, it follows that , hence . Thus, is closed in , so . ☐
Definition 2
([28,29]). A multivalued operator is called
- (1)
- σ-Lipschitz if ∃ such that
- (2)
- a contraction if it is σ-Lipschitz with .
The next results will be used frequently.
Lemma 5
([5]). Let be a complete p-normed space with and let be a totally bounded subset of . Then, is compact for some .
Proposition 2
([30], Proposition 3, p. 42). Let be compact and let be upper semi-continuous. Then, is compact.
Theorem 2.
([31,32]). Let and Y be metric spaces and let be a multivalued operator. If F is upper semi-continuous, then F has a closed graph. Conversely, if F is quasicompact and has a closed graph, then F is upper semi-continuous.
Theorem 3.
([5], Kakutani-type). Let be a complete p-normed space and let be a compact s-convex subset of with and . If is upper semi-continuous, then ∃ such that .
Theorem 4.
([33], Nadler’s theorem). If is a complete metric space, then every contraction multivalued operator has a .
Theorem 5.
([5], Schauder-type). Let be a compact s-convex subset of a complete p-normed space with and . If is continuous, then has a .
Theorem 6.
([6], Schauder-type). Let be a closed s-convex subset of a complete p-normed space with and . If is completely continuous, then f admits a .
3. Main Results
In this section, we present and establish our main fixed-point theorems.
3.1. Krasnosel’skii-Type Fixed-Point Theorem for Upper Semi-Continuous Multivalued Operators in p-Normed Spaces
Theorem 7.
Let be a complete p-normed space with , and let be a nonempty compact s-convex subset of . Assume that is an upper semi-continuous multivalued operator and that satisfies the following:
- ()
- for some ;
- ()
- If with , then .
Then, ∃ such that .
Proof.
From (), the inequality guarantees that the Neumann series
converges in the operator p-norm. Hence, is invertible in . Using the factorization
where S is a continuous linear operator, we obtain
Thus, is well defined and continuous.
Define
Since is continuous and , it follows that .
To verify upper semi-continuity, let be closed. Then,
Since is continuous, is closed. Because F is upper semi-continuous, is closed, so is closed. Hence, is upper semi-continuous.
Next, for any and , choose such that
Thus, , and by (), we obtain . Hence, .
Let with and . Choose such that
Since F is upper semi-continuous, there is a subsequence converging to some . Then,
so . Thus, has a closed graph.
By Theorem 1, an upper semi-continuous operator with closed values has a closed graph; hence, is upper semi-continuous. Applying Theorem 3, admits a , and therefore,
☐
Theorem 8.
Let be a complete p-normed space with , and let be a nonempty closed bounded s-convex subset of . Assume that is upper semi-continuous and satisfies ()–(),
- ()
- is relatively compact, and for every ,
Then, ∃ such that .
Proof.
Let , which is compact and s-convex. As in Theorem 7, define . If , then for some and , which implies
Hence, , and by (), we obtain . Thus, , and Theorem 7 gives a of and, in turn, of . ☐
Theorem 9.
Let be a complete p-normed space with , and let be a nonempty compact s-convex subset of . Assume
- If and with , then .
Letsatisfy (), and supposeis upper semi-continuous. Then, ∃ such that .
Proof.
Choose a sequence with and . By Theorem 7, for each n ∃ such that
Since is compact, a subsequence converges to some . Define
Because G is continuous, F is upper semi-continuous, and is compact, has a closed graph. Therefore,
☐
Theorem 10.
Let be a complete p-normed space with . Let be a nonempty closed s-convex subset, , and let be open with . Assume that is upper semi-continuous and satisfies () and the condition
- ()
- If with , then .
Then, at least one of the following holds:
- (1)
- The equation has a solution for ;
- (2)
- ∃ and some such that
Proof.
Assume that (2) fails and that has no on . Define
Then, (take ). Using compactness and the closed-graph property of F, one shows that B is compact. Since , Urysohn’s lemma gives a continuous function such that
Define
By compactness of and continuity of , the set
is compact (Lemma 5). Hence, is upper semi-continuous. Applying Theorem 3 yields with , which produces the desired alternative. ☐
3.2. Approximation Techniques in Krasnosel’skii Theory
For a wide class of multifunctions with compact (and not necessarily convex) values, various authors have developed constructive schemes for obtaining approximate continuous selections. Notable contributions include the work of Cellina [34], Górniewicz, Granas, Kryszewski [35], as well as Górniewicz and Lassonde [36]. These approximation results form a foundation for the modern development of multivalued index theory.
Unlike the techniques employed in these classical approaches, the present section relies on a different type of approximation, tailored to Krasnosel’skii-type results in p-normed spaces. The following continuous approximation theorem serves as a key tool in our analysis.
Theorem 11
where denotes the open unit ball of and the sum is understood in the Minkowski sense.
(Approximate Lipschitz Selection [37]). Let be a p-normed space and a complete p-normed space, where . Suppose is an upper semi-continuous multivalued operator. Then, for every ∃, a locally Lipschitz function satisfies the following:
- 1.
- for all .
- 2.
- The graph of is ε-close to the graph of F in the product space, i.e.,
Proof.
Since F is upper semi-continuous, for each fixed and every ∃ such that
where denotes the open –ball centered at , and is the open unit ball in Y. We may assume without loss of generality that .
The collection is an open covering of . Since is paracompact, ∃ a locally finite refinement together with a locally Lipschitz partition of unity subordinate to it.
For each i, choose so that and select . Define
The sum is well defined by local finiteness, and is locally Lipschitz as a finite combination of locally Lipschitz functions.
Since is an s-convex combination of points , we have
Fix and set , which is finite. For each , let and choose an index with . Then, , so
Thus, lies within of , and consequently,
This yields
☐
Theorem 12.
Let be a complete p-normed space with , and let be a compact s-convex subset of . Suppose is an upper semi-continuous multivalued operator, and is single-valued. Assume
- ()
- G is a contraction;
- ()
- .
Then, the inclusion
admits at least one solution.
Proof.
By Theorem 11, for every ∃, a continuous map
such that
Using () and the s-convexity of ,
For any fixed , define
Since G is a contraction, the Banach theorem ensures the existence of a unique satisfying
equivalently,
Because is a homeomorphism onto its image, is continuous.
Define
It follows that satisfies the hypotheses of Theorem 5, so ∃ with
Choose a sequence and pick such that
Passing to the limit and using the closedness of the graph of F yields
Thus,
☐
Following a comparable line of reasoning, we now prove the next result.
Theorem 13.
Let be a complete p-normed space, and let be a closed, bounded, s-convex subset. Suppose is an upper semi-continuous multivalued operator and is a single-valued operator satisfying condition . Additionally, assume that
- ()
- is compact and .
Then, the operator possesses at least one .
Proof.
Set , which is compact and s-convex. As in the proof of Theorem 12, define . For any , by the definition of , ∃ such that
or equivalently,
Condition ensures that , so . Consequently, by Theorem 12, the operator has a . ☐
Theorem 14.
Assume that is a complete p-normed space with , and let be a nonempty, compact, s-convex subset of , where . Suppose that condition holds, G is a nonexpansive operator, and is an upper semi-continuous multivalued operator. Then, the operator admits at least one .
Proof.
Let satisfy , and define . Then, is a contraction because
By Theorem 12, ∃ such that
Since is compact, the sequence admits a subsequence converging to some . Define by
Given that G is continuous, F is upper semi-continuous, and is compact, the map has a closed graph. Therefore,
which shows that z is a of . ☐
Theorem 15.
Let be a complete p-normed space with . Suppose is an upper semi-continuous multivalued operator and satisfies condition . Then, either of the following is unbounded:
- (1)
- The inclusion has a solution for .
- (2)
- The set .
Proof.
Assume that alternative (2) does not hold, that is,
is bounded. Then, ∃ such that
Define the open ball
Thus, is an open, bounded, p-convex set containing the origin .
Since F is compact, is relatively compact in , and hence is compact.
Let be an -approximate selection of F such that
Now, consider the set
Because , Urysohn’s lemma provides a continuous function satisfying
For each , define the operator by
Owing to the compactness of F and the continuity of , the set
is compact by Lemma 5. Hence, is a continuous, compact operator. Applying Schauder’s theorem (Theorem 5), we obtain a point such that . Finally, repeating the argument used in the proof of Theorem 12, we conclude that has at least one . ☐
3.3. A Krasnosel’skii-Type Theorem in the Expansive Setting
In this section, we establish a multivalued version of a Krasnosel’skii-type theorem in the context of expansive operators within p-normed spaces. The results extend those in [38], which were confined to Banach spaces .
Definition 3.
Let be a metric space and . An operator is said to be expansive if ∃ a constant such that
We now recall several auxiliary results that will be instrumental in the sequel.
Theorem 16
([39]). Let be a complete metric space, and let be closed. If is expansive and satisfies , then ∃ is a unique such that .
Lemma 6
([38]). Assume is a complete metric space and that is expansive for some . If is closed and , then G has a unique in .
Lemma 7
([39]). Let be a p-normed linear space, , and suppose is expansive with constant . Then, the operator is invertible, and its inverse satisfies
We are now prepared to present the main results of this section.
Theorem 17.
Let be a complete p-normed space, and let be a compact, s-convex subset. Suppose is an upper semi-continuous multivalued operator, and is a single-valued operator satisfying the following:
- G is continuous and expansive;
- For every , it holds that
Then, the inclusion admits at least one solution in .
Proof.
By Theorem 11, for each ∃ a continuous selection such that
Fix and define the operator by
Since G is expansive, so is . Hence, by Theorem 16, ∃ a unique such that
Because G is expansive with constant , we have for any ,
It follows that
Since is continuous, the mapping is also continuous. Define
Then, is continuous. By Schauder’s theorem (Theorem 5), ∃ such that
Finally, by the same limiting argument used in the proof of Theorem 12, one concludes that the operator possesses at least one in . ☐
Using a similar approach, we obtain the following result.
Theorem 18.
Let be a complete p-normed space and be a compact, s-convex subset. Assume is upper semi-continuous and is single-valued, satisfying conditions and . Then, the inclusion
admits at least one solution in.
Proof.
Since G is expansive, the inverse exists and is a contraction. Moreover, the continuity of G implies that is closed.
For a given , let be an -approximate continuous selection of F, so that
Fix and define by
Since is a contraction, is a contraction as well. By the Banach contraction principle, ∃ a unique such that
Let . Then,
where is the expansivity constant of G, so that has Lipschitz constant . Rearranging yields
Thus, the mapping is continuous from into itself. By Theorem 5, ∃ such that
Passing to the limit as and using the argument from Theorem 12, we obtain a point satisfying
which completes the proof. ☐
3.4. A Study of Krasnosel’skii’s Theorem Using Measures of Noncompactness
Definition 4
([40], p. 20). Let be a complete p-normed space with , and let denote the family of bounded subsets of . For any , the Kuratowski and Pompeiu–Hausdorff measures of noncompactness are defined, respectively, by
In a complete p-normed space , a set is called a -net of if
Consequently, an equivalent formulation of the Pompeiu–Hausdorff measure in this setting is
For notational convenience, we let denote a generic measure of noncompactness (either or ).
Proposition 3
([7]). Let be a complete p-normed space. Then, any measure of noncompactness satisfies the following properties:
- (1)
- Regularity: if and only if is relatively compact.
- (2)
- Invariance under closure: .
- (3)
- Semi-additivity: .
- (4)
- Monotonicity: If , then .
- (5)
- Semi-homogeneity: for all .
- (6)
- Algebraic semi-additivity: .
- (7)
- Invariance under translations: for any .
- (8)
- Lipschitzianity: , where if and if .
- (9)
- Invariance under p-convex hull: .
Definition 5
([7]). Let (Kuratowski) and (Pompeiu–Hausdorff) be measures of noncompactness in .
- (i)
- A multivalued operator is a σ–set-contraction (with respect to or ) if it is bounded and continuous, and ∃ such thatfor every bounded subset In particular, if σ = 1 F is called 1-set-contractive.
- (ii)
- A bounded continuous operator is ψ-condensing iffor every bounded with
Remark 2
([40,41]).
- 1.
- Every compact operator is a 0-set-contraction. Moreover, any Lipschitz operator with constant is a σ-set-contraction.
- 2.
- Every σ-set-contraction with is ψ-condensing.
- 3.
- Every ψ-condensing operator is 1-set-contractive, though the converse does not hold in general (see [42]).
Definition 6
([13,20,43]). An operator is called ϕ-expansive if ∃ a function such that
where ϕ satisfies the following:
- (i)
- ;
- (ii)
- for all ;
- (iii)
- ϕ is either continuous or non-decreasing.
Lemma 8.
([13,43]). Let be a nonempty, bounded, closed subset of a complete p-normed space , . If is ϕ-expansive, then f is injective and its inverse is uniformly continuous.
Theorem 19
(Sadovski-type, [7]). Let be a bounded, closed, s-convex subset of a complete p-normed space , where and . If is continuous and ψ-condensing (with respect to or ), then f has at least one in , and the set of all such s is compact.
Theorem 20.
Assume that is a closed, bounded, s-convex subset of a complete p-normed space . Let be an upper semi-continuous multivalued operator, and be a continuous single-valued operator satisfying the following:
- ()
- F is compact;
- ()
- G is a σ-set-contraction with (with respect to or );
- ()
- I − G is ϕ-expansive;
- ()
- .
Then, the inclusion admits at least one solution in .
Proof .
Fix . By Theorem 11, ∃ a continuous selection such that
Using and the s-convexity of , we obtain
Fix and define the operator
For any bounded , we have
since G is ψ-condensing by Remark 2 and . As G is continuous and ψ-condensing, Theorem 19 yields a point such that
By Lemma 8 and , the operator is injective and is uniformly continuous. Hence,
Define by
The continuity of and uniform continuity of imply that is continuous. Moreover, since and F is compact, the image is relatively compact. Thus, by Schauder’s theorem (Theorem 5), ∃ such that
Finally, by the standard limiting argument used in Theorem 12, the operator admits at least one in . ☐
Theorem 21.
Let be a closed, s-convex subset of a complete p-normed space with . Suppose is upper semi-continuous, and is continuous. In addition to –, assume
- ()
- is bounded.
Then, either of the following is unbounded:
- (1)
- The inclusion has a solution for .
- (2)
- The set .
Consequently, the operator admits at least one solution.
Proof .
Assume that (2) fails, that is, the set
is bounded. Then, ∃ such that
Define the open ball
Then, is open, bounded, p-convex, and contains θ.
Since F is compact, is relatively compact, so is compact. By Theorem 11, ∃ an ϵ-approximate selection of F satisfying
Set
Since , Urysohn’s lemma provides a continuous function such that
For each , define the operator by
By the compactness of F and the uniform continuity of (from and Lemma 8), the set
is compact (by Lemma 5). Thus, is continuous. Moreover, since is relatively compact, for any bounded with . Hence, is ψ-condensing by Remark 2.
Applying Theorem 19, ∃ such that . Finally, using the same limiting procedure as in Theorem 12, we conclude that possesses at least one . ☐
4. Application of Nonlinear Integral Equation of Hammerstein Type
For the applicability of the obtained Krasnosel’skii-type results in p-normed spaces, we consider a nonlinear integral equation of Hammerstein type
where is a continuous kernel and is a nonlinear function satisfying a Lipschitz condition. Equation (4) can be reformulated as a problem , where
Under the assumptions of Theorem 7, one can guarantee the existence of at least one of .
5. Numerical Application
To illustrate the applicability of the obtained Krasnosel’skii-type results in p-normed spaces, we present a numerical example based on a Chebyshev collocation discretization. Chebyshev-based spectral techniques have been proven effective for high-accuracy solutions of nonlinear and nanoscale boundary value problems [44], and they provide a convenient framework to verify the existence of s established in the theoretical sections of this work. The approach also complements recent extensions of theory in generalized metric structures [45].
The integral equation under consideration is of classical Hammerstein type:
which can be equivalently expressed in the abstract fixed-point form by setting and . This decomposition aligns with the Krasnosel’skii fixed-point framework where one operator is compact (here, F) and the other is a contraction or identity-type map (here, trivially ). Thus, our numerical example directly realizes a canonical Hammerstein equation within the theoretical setting of the paper.
5.1. Chebyshev Collocation Approximation
For a numerical solution of (4), we expand in terms of shifted Chebyshev polynomials of the first kind:
where are the classical Chebyshev polynomials on and are unknown coefficients.
By collocating (4) at the shifted Chebyshev–Gauss nodes
we obtain a nonlinear algebraic system
where are the corresponding quadrature weights associated with the shifted Chebyshev nodes.
5.2. Illustrative Example
Consider the kernel and nonlinearity
which appear in nonlinear diffusion-type models. We take collocation points and solve (5) using a Newton–Kantorovich iterative scheme. The resulting approximation is compared with a reference solution obtained by fine discretization.
The residual error is defined as
Table 1 shows the decay of with increasing N, confirming the spectral accuracy of the Chebyshev collocation method.
Table 1.
Maximum residual error for different values of N.
5.3. Real-World Application: Spatial Population Dynamics with Density-Dependent Feedback
Hammerstein integral equations are widely used in mathematical ecology to model steady-state distributions of biological populations where growth is limited by local resource availability and individuals disperse across space. A prototypical model for a single species in a one-dimensional habitat is given by
where
- 1.
- denotes the equilibrium population density at location t;
- 2.
- is the spatially heterogeneous intrinsic growth rate (e.g., due to variation in sunlight, soil quality, or water);
- 3.
- is a competition coefficient that models how per-capita reproduction declines with local density (Holling type II or Michaelis–Menten saturation);
- 4.
- is a dispersal kernel describing the probability that an individual born at location s settles at location t.
We adopt the ecologically realistic Hammerstein model (6) with kernel () modeling limited dispersal, sinusoidal growth rate representing two favorable habitat zones, and competition parameter . This yields a continuous, bounded nonlinearity and a compact integral operator F, satisfying the conditions of Krasnosel’skii-type fixed-point theorems in p-normed spaces and guaranteeing a non-negative steady-state solution. Using the Chebyshev collocation method from Section 5.1 with and a damped Newton–Kantorovich solver (tolerance ), we obtain a highly accurate numerical solution () that exhibits density peaks aligned with favorable habitats (see Figure 2). This demonstrates the relevance of our theoretical framework to real-world problems in spatial ecology, conservation, and habitat modeling [46].
Figure 2.
Equilibrium population density for the ecological Hammerstein model (6) with , , and . The solution was computed via fixed-point iteration on a fine grid and illustrates habitat-driven clustering.
The resulting steady-state population density is shown in Figure 2, which clearly reflects the influence of the spatially varying growth rate : higher densities emerge near and , where attains its maxima. The smoothness of the solution is a direct consequence of the diffusive-like dispersal encoded in the kernel .
5.4. Discussion
The rapid convergence of demonstrates the efficiency of Chebyshev collocation in approximating the solution of nonlinear integral equations consistent with the Krasnosel’skii framework. This experiment validates the theoretical results obtained in Section 3 and highlights the feasibility of using spectral methods for computational verification in p-normed settings.
6. Abbreviations
For clarity of presentation, the abbreviations and notation are compiled in Table 2.
Table 2.
Abbreviations with definitions and descriptions for families of subsets of .
7. Conclusions
This work develops fixed-point theory by developing a unified framework for Krasnosel’skii-type theorems in complete p-normed spaces with . We generalize classical results to the setting of multivalued operators and introduce several new tools, including an approximation theorem for upper semi-continuous mappings and measures of noncompactness adapted to the geometry of p-normed spaces. These ingredients allow us to address the difficulties that arise in non-locally convex environments, where standard Banach space techniques are no longer effective. The resulting theory suits a broad range of operator behaviors, from contractions to expansive mappings, and is supported by applications to Hammerstein integral equations with numerical verification based on the spectral collocation method. Taken together, these contributions fill gaps in the existing literature and provide a solid analytical framework for studying nonlinear problems in function spaces where classical convex methods fail, thereby opening further points for research in non-convex analysis and its applications.
8. Comparative Analysis of Fixed-Point Theory Developments
For convenience, the principal advancements relevant to fixed-point theory are organized and compared in Table 3, which outlines the core features and distinctions among existing approaches.
Table 3.
Comparative Analysis of Fixed-Point Theory Developments.
Author Contributions
Methodology, G.A.; formal analysis, G.A.; writing—original draft preparation, G.A.; writing—review and editing, M.G.; numerical application and discussion, Y.H.Y. All authors have read and agreed to the published version of the manuscript.
Funding
The project was funded by KAU Endowment (WAQF) at King Abdulaziz University, Jeddah, Saudi Arabia. The authors, therefore, acknowledge with thanks WAQF and the Deanship of Scientific Research (DSR) for technical and financial support.
Data Availability Statement
No new data were created or analyzed in this study.
Acknowledgments
The authors are grateful to the anonymous reviewers for their thoughtful and constructive comments provided across both rounds of revision. Their careful evaluations and insightful suggestions have contributed substantially to strengthening the quality and clarity of this manuscript.
Conflicts of Interest
The authors declare no conflicts of interest.
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