1. Introduction
Fractional calculus has become an important framework in modern applied mathematics due to its capability of describing memory and hereditary properties in complex systems. Since the introduction of the Liouville–Caputo fractional derivative [
1], fractional-order models have attracted extensive attention and have been successfully applied in various areas, including memristive circuits [
2], infectious disease modeling [
3], control theory [
4,
5], and economic dynamics [
6]. Meanwhile, difference equations have played a fundamental role in the analysis and modeling of discrete-time systems, with classical contributions in stability theory [
7,
8] and systematic treatments provided in the monographs of Kocic and Ladas [
9] and Agarwal [
10]. Comprehensive introductions to fractional calculus and fractional differential equations can be found in the classical monographs by Oldham and Spanier [
11], Kilbas et al. [
12], and Podlubny [
13].
The combination of fractional calculus and discrete analysis has led to the rapid development of discrete fractional calculus. Several discrete fractional operators, including the Grünwald–Letnikov [
14], Riemann–Liouville [
15], and Liouville–Caputo [
16] formulations, have been introduced. Among them, Goodrich and Peterson [
17] provided a systematic foundation for discrete fractional calculus, particularly emphasizing forward fractional differences. Subsequently, different qualitative and numerical aspects of discrete fractional equations have been investigated. For instance, Laledj et al. [
18] studied implicit fractional
q-difference equations using fixed-point methods, while Allouch et al. [
19] and Alili et al. [
20] considered boundary value problems and fractional difference inclusion problems, respectively. Hattaf [
21] further investigated numerical schemes for nonlinear fractional models.
A Liouville–Caputo fractional difference operator can be interpreted as the composition of a fractional sum and an integer-order difference operator. Since fractional sums can be defined through variable upper-limit and variable lower-limit formulations, while integer-order differences can be constructed in forward and backward directions, four distinct Liouville–Caputo fractional difference formulations naturally arise. However, these four formulations have not yet been systematically studied within a unified theoretical framework. Early developments of discrete fractional calculus mainly focused on the variable upper-limit fractional sum combined with the forward difference operator [
17]. Cheng and Chu [
22] considered a formulation involving the variable upper-limit fractional sum and the backward difference operator, but a general framework covering all combinations of fractional sums and difference directions was not established.
To overcome this limitation, our previous work [
23] developed a unified framework for the four Liouville-Caputo fractional difference formulations with variable bounds. In that work, the four definitions were introduced, their fundamental properties were established, their equivalence with corresponding Volterra-type summation equations was proved, and explicit solution representations were obtained. These results provide the necessary analytical foundation for further investigation of the qualitative properties of the corresponding solutions. However, the existence, uniqueness, and continuous dependence of solutions, which are essential aspects of well-posedness, remain open problems.
In the continuous setting, the qualitative analysis of fractional equations, especially existence and uniqueness theory, has been extensively investigated. Various techniques have been developed for different classes of fractional differential equations. For example, Salem and Mshary [
24] studied fractional Langevin equations, Nanware and Dhaigude [
25] considered fractional equations with integral boundary conditions, Ahmad and Alsaedi [
26] investigated coupled fractional systems, and Ardjouni and Djoudi [
27] established results for nonlinear Liouville–Caputo fractional differential equations. More recently, Baroudi et al. [
28], Benzenati et al. [
29], Azzouz and Beddani [
30], Promsakon et al. [
31], Graef et al. [
32], and Ezugorie et al. [
33] extended existence, uniqueness, and stability analyses to various fractional models. Of particular relevance to the present work, Islam et al. [
34] investigated Caputo fractional differential equations by transforming the problems into equivalent Volterra integral equations and applying contraction arguments. Their work also highlighted the potential extension of such qualitative analysis to discrete fractional calculus. Motivated by these developments, this paper establishes a unified well-posedness theory for the four Liouville–Caputo fractional difference formulations introduced in [
23]. By utilizing the equivalent Volterra-type summation equations, each initial/final value problem is reformulated as a fixed-point problem in a Banach space. The Banach contraction mapping principle is then applied to derive sufficient conditions for the existence and uniqueness of solutions. Furthermore, the continuous dependence of solutions on initial/final data is established through explicit Lipschitz-type estimates obtained from a discrete fractional Gronwall-type inequality. Compared with [
23], which focused on the construction of the operators and the representation of solutions, the present work investigates their qualitative properties and completes an essential part of the unified discrete fractional calculus framework. Numerical examples are also provided to illustrate and validate the theoretical results. The remainder of this paper is organized as follows.
Section 2 introduces preliminary definitions and auxiliary results.
Section 3 presents the main theoretical results on existence, uniqueness, and continuous dependence.
Section 4 provides numerical examples, and
Section 5 concludes the paper.
3. Theoretical Proof and Extended Discussion on the Existence and Uniqueness of Solutions
In this section, we establish and rigorously prove four theorems concerning the existence and uniqueness of solutions for the four types of Liouville–Caputo fractional difference equations introduced in
Section 2, utilizing the Banach fixed point theorem combined with refined estimates and combinatorial identities.
3.1. Theoretical Proof of the Existence and Uniqueness of Solutions
This subsection focuses on the existence and uniqueness of solutions to fractional-order difference equations under four different definitions. We propose and rigorously prove four theorems, each corresponding to one of the four definition scenarios. This not only consolidates the theoretical framework of fractional-order difference equations but also provides a basis for their practical applications, thus promoting the development of research in this field.
Theorem 1 (Forward fractional difference equation with initial value)
. Let be a constant, and let with . Consider the initial value problem for a Liouville–Caputo fractional difference equation of order on the finite interval : where denotes the Liouville–Caputo fractional difference operator of order α, is a given function, and is the initial value. Assume that:- (H1)
There exists a constant such that for all ;
- (H2)
f satisfies a Lipschitz condition with respect to the second variable, i.e., there exists a constant such thatand moreover, .
Then, problem (19) possesses a unique solution on . Proof. By Lemma 2, the initial value problem (
19) is equivalent to the Volterra sum equation
(For
, the sum is empty and the equality reduces to
.) This equivalence is guaranteed by the corresponding lemma in [
23]. Hence, it suffices to prove that (
20) admits a unique solution on
.
Existence. Define a sequence
by
We first estimate the differences
. For
, using (H1) and the identity (see Lemma 6)
we obtain, for every
,
For
, by the Lipschitz condition (H2) and the previous estimate,
Assume inductively that for some
, for all
,
Then,
Consequently, for every
and all
,
Now consider the series
. Since
, we have
and therefore
Hence,
where the convergence follows from
. Thus, the series converges absolutely, and the sequence
converges pointwise to some limit
for each
.
To see that
satisfies (
20), fix
and let
in the recurrence defining
. Because the sum is finite, we may interchange the limit with the summation. The continuity of
f (implied by the Lipschitz condition) yields
which shows that
is a solution of (
20) on
.
Uniqueness. Let
X be the space of functions on
equipped with the supremum norm
. Then,
is a Banach space. Define an operator
by
Thanks to (H1),
is bounded, so
T is well defined. A solution of (
20) is exactly a fixed point of
T.
Take any
. Using (H2) and the fact that
for all
, we have
Taking the supremum over
gives
. Since
,
T is a contraction mapping.
By the Banach fixed-point theorem,
T possesses a unique fixed point in
X. Consequently, problem (
19) has a unique solution on
. □
Theorem 2 (Forward fractional difference equation with final value)
. Let be a constant, and let with . Consider the final value problem for a Liouville–Caputo fractional difference equation of order on the finite interval : where denotes the Liouville–Caputo fractional difference operator of order α, is a given function, and is the final value. Assume that:- (H1)
There exists a constant such that for all ;
- (H2)
f satisfies a Lipschitz condition with respect to the second variable, i.e., there exists a constant such thatand moreover, .
Then, problem (21) possesses a unique solution on . Proof. By Lemma 3, (
21) is equivalent to the Volterra sum equation
(For
, the sum is empty and the equality reduces to
.)
The proof is exactly parallel to that of Theorem 1, with the summation direction reversed and rising factorials replaced accordingly. Following the same iterative construction
and applying the combinatorial identity from Lemma 6, we obtain, for every
,
Since
and
, the series
converges, ensuring pointwise convergence to a solution of (
22).
For uniqueness, define
T on the Banach space of functions on
with the supremum norm by the right-hand side of (
22). For any
, using (H2),
Since
,
T is a contraction. The Banach fixed-point theorem yields a unique fixed point, which is the unique solution of (
21) on
. □
Theorem 3 (Backward fractional difference equation with initial value)
. Let be a constant, and let with . Consider the initial value problem for a Liouville–Caputo fractional difference equation of order on the finite interval : where denotes the Liouville–Caputo fractional difference operator of order α, is a given function, and is the initial value. Assume that:- (H1)
There exists a constant such that for all ;
- (H2)
f satisfies a Lipschitz condition with respect to the second variable, i.e., there exists a constant such thatand moreover, .
Then, problem (23) possesses a unique solution on . Proof. By Lemma 4, (
23) is equivalent to the Volterra sum equation
(For
, the sum is empty and the equality reduces to
.)
The proof follows exactly the same iterative construction as in Theorem 1, with the summation limits adjusted from
to
to
to
. Following the same inductive argument and applying Lemma 6, we obtain, for every
,
Since
and
, the series
converges. Thus,
converges pointwise to a solution of (
24) on
.
For uniqueness, define
T on the Banach space of functions on
by the right-hand side of (
24). For any
, using (H2),
since
. Thus,
T is a contraction, and the Banach fixed-point theorem yields the unique solution of (
23) on
. □
Theorem 4 (Backward fractional difference equation with final value)
. Let be a constant, and let with . Consider the final value problem for a Liouville–Caputo fractional difference equation of order on the finite interval : where denotes the Liouville–Caputo fractional difference operator of order α, is a given function, and is the final value. Assume that:- (H1)
There exists a constant such that for all ;
- (H2)
f satisfies a Lipschitz condition with respect to the second variable, i.e., there exists a constant such thatand moreover, .
Then, problem (25) possesses a unique solution on . Proof. By Lemma 5, (
25) is equivalent to the Volterra sum equation
(For
, the sum is empty and the equality reduces to
.)
The proof is exactly parallel to that of Theorem 1, with the summation direction reversed and the kernel
replaced by
. The shifted argument of
f in the original equation is a direct consequence of the equivalence proof in Lemma 5 (see also [
23]). Following the same iterative construction and applying Lemma 6, we obtain, for every
,
Since
and
, the series
converges, ensuring pointwise convergence to a solution of (
26) on
.
For uniqueness, define
T on the Banach space of functions on
with the supremum norm by the right-hand side of (
26). For any
, using (H2),
Since
,
T is a contraction. The Banach fixed-point theorem yields a unique fixed point, which is the unique solution of (
25) on
. □
Remark 1 (On the shifted arguments of the nonlinearity). The different arguments of the nonlinearity f appearing in Theorems 1–4 result naturally from the index shifts involved in the Caputo fractional difference definitions and their equivalent Volterra sum formulations.
For instance, in Theorem 1 (the forward initial value problem) with
, the Caputo fractional difference is defined as
Applying the fractional summation operator
to both sides and using the identity
valid for
(see [
23], Theorem 19, with
), shifts the argument of
f from
to the summation variable
i, leading to the Volterra sum equation
A similar index shift occurs in Theorem 4 (the backward final value problem), where the argument is transformed into the summation variable i. In contrast, Theorems 2 and 3 do not involve such shifts because the summation variable naturally coincides with the argument of f.
The precise form of these shifts follows directly from the equivalence results established in [
23] (Theorems 22–25), where the four Caputo fractional difference equations are converted into their corresponding Volterra sum equations for
. Thus, the shifted arguments in the original formulations ensure that the resulting Volterra equations take the standard form with
f evaluated at the unshifted summation index
i.
3.2. Continuous Dependence on Initial/Final Values
In this subsection, we establish that the unique solutions obtained in Theorems 1–4 depend continuously on the prescribed initial or final data. This property is indispensable for practical applications, since boundary data in real-world models are inevitably subject to measurement errors. Owing to the structural symmetry of the four formulations, we provide detailed proofs for the forward initial value problem (Theorem 5) and the backward final value problem (Theorem 8), while the remaining two cases (Theorems 6 and 7) follow by completely analogous arguments; we state the corresponding estimates without repeating the identical derivations.
Theorem 5 (Continuous dependence on initial value for forward type)
. Let be a constant, with , and let . Suppose that satisfies the Lipschitz condition (H2) with constant . Let and be the unique solutions of the initial value problem (19) corresponding to the initial values and , respectively. Then, there exists a constant , depending only on α, A, N, and a, such that In particular, the solution depends Lipschitz continuously on the initial datum. Proof. By Lemma 2, the solutions
y and
satisfy the Volterra sum equations
and
Subtracting the two equations and defining the error
, we obtain
Taking absolute values and applying the Lipschitz condition (H2), we have
Let
. Then, the above inequality reads
By the forward version of the discrete Gronwall-type inequality (Lemma 8 (1)), there exists a constant
, depending only on
,
A,
N, and
a, such that
Taking the maximum over
yields the desired estimate. This completes the proof. □
Theorem 6 (Continuous dependence on final value for forward type)
. Let be a constant, with , and let . Suppose that satisfies the Lipschitz condition (H2) with constant . Let and be the unique solutions of the final value problem (21) corresponding to the final values and , respectively. Then, there exists a constant , depending only on α, A, M, and b, such that Proof. By Lemma 3, the solutions satisfy the Volterra sum Equation (
22). Setting
and subtracting the corresponding equations, we obtain
Applying the backward version of Lemma 8 (2) to the above inequality yields a constant
such that
for all
. Taking the supremum over the interval completes the proof. □
Theorem 7 (Continuous dependence on initial value for backward type)
. Let be a constant, with , and let . Suppose that satisfies the Lipschitz condition (H2) with constant . Let and be the unique solutions of the initial value problem (23) corresponding to the initial values and , respectively. Then there exists a constant , depending only on α, A, N, and a, such that Proof. By Lemma 4, the solutions satisfy the Volterra sum Equation (
24). Defining
and subtracting, we have
Applying the forward version of Lemma 8 (1) yields a constant
such that
for all
. Taking the supremum over
n concludes the proof. □
Theorem 8 (Continuous dependence on final value for backward type)
. Let be a constant, with , and let . Suppose that satisfies the Lipschitz condition (H2) with constant . Let and be the unique solutions of the final value problem (25) corresponding to the final values and , respectively. Then there exists a constant , depending only on α, A, M, and b, such that In other words, the solution depends Lipschitz continuously on the final datum. Proof. By Lemma 5, the solutions satisfy the Volterra sum Equation (
26). Letting
and subtracting, we get
Taking absolute values and applying (H2), we have
Let
. Then,
By the backward version of Lemma 8 (2), there exists a constant
such that
Taking the maximum over
yields the desired estimate. The proof is complete. □
Theorems 5–8 collectively establish the continuous dependence of solutions on the boundary data for all four types of Liouville–Caputo fractional difference equations. Together with the existence and uniqueness results in Theorems 1–4, this completes the qualitative analysis of the unified discrete fractional calculus framework. These results are indispensable for sensitivity analysis, parameter identification, and numerical approximation in practical applications where boundary data are inevitably subject to measurement errors.
3.3. Discussion
The results obtained in
Section 3 establish a unified qualitative framework for the four Liouville–Caputo fractional difference formulations introduced in [
23]. Theorems 1–4 provide sufficient conditions for the existence and uniqueness of solutions for the corresponding initial and final value problems, while Theorems 5–8 establish the Lipschitz continuous dependence of solutions on prescribed boundary data. These results extend the operator framework developed in [
23] from solution representations to the well-posedness analysis of the associated fractional difference equations.
All results in this paper are established on finite discrete intervals, namely or . This restriction is closely related to the discrete fractional Gronwall-type inequality used in the continuous dependence analysis, since the corresponding estimates generally depend on the length of the interval. Extending the results to infinite domains would require additional uniform estimates and remains an interesting direction for future research.
The assumptions on the nonlinear term f, including boundedness (H1) and global Lipschitz continuity with in (H2), are sufficient conditions that allow the application of the Banach contraction mapping principle. They are adopted to establish a unified theory for the four formulations and may be weakened in future investigations. In particular, local Lipschitz conditions, monotonicity assumptions, or other growth conditions may lead to broader existence and uniqueness results.
Although the present work focuses on the case , the extension to higher-order fractional differences is an important open problem. For , the decomposition with suggests that multiple initial or final conditions are required, and a corresponding well-posedness theory needs to be developed. Other possible directions include stability, periodicity, asymptotic behavior, and numerical approximation theories for variable-bound discrete fractional systems.
4. Examples
Example 1 (Forward initial value problem)
. Consider the initial value problem for the forward fractional difference equation of order , where N is a fixed positive integer, is the initial value, and α is the fractional order. By Lemma 2 (see also [23], Theorem 24), problem (27) is equivalent to the Volterra sum equation Numerical simulations. Using the iterative scheme derived from (
28), we compute the numerical solution on
. First, we fix the initial value
and vary the fractional order
to examine the effect of the fractional order on the solution. The numerical results are displayed in
Figure 1 and summarized in
Table 3. It is observed that for higher fractional orders, the solution grows rapidly in the early stage but eventually tends to a steady value; for lower orders, the solution grows more slowly and consistently stays below the higher-order solutions throughout the entire interval. This is consistent with the memory effect of fractional calculus: lower-order fractional differences possess stronger memory, resulting in smoother and slower evolution of the solution.
Second, we fix the fractional order
and vary the initial value
to investigate the sensitivity of the solution with respect to perturbations in the initial data. The numerical results are displayed in
Figure 2 and summarized in
Table 4. It is clearly observed that the solution depends continuously on the initial value: small perturbations in
lead to small, uniformly controlled deviations in the solution.
Verification of continuous dependence on initial data (Theorem 5). To verify the Lipschitz continuous dependence of the solution on the initial data as established in Theorem 5, we fix the reference solution with
and compute the maximum deviation
for each perturbation
. The results are summarized in
Table 5. For
, the ratios
are
,
, and
, respectively. These values remain bounded as
, indicating that the solution depends Lipschitz continuously on the initial data, in agreement with Theorem 5. Moreover, by Lemma 8, the theoretical Lipschitz constant is explicitly given by
, which is finite, thereby ensuring the existence of a uniform bound. This confirms that the numerical results are consistent with the theoretical prediction.
The ratio remains bounded (between 50 and 64) for all perturbations, indicating that the solution depends Lipschitz continuously on the initial data as predicted by Theorem 5.
Verification of the assumptions of Theorem 1. Now consider the specific initial value problem
where
for
and
. The function
f satisfies the Lipschitz condition with constant
, since
Moreover,
, so the boundedness condition holds with
. To verify the condition of Theorem 1, we note that for
,
, and all
, we have
and consequently
trivially satisfies the required inequality. Therefore, by Theorem 1, problem (
29) possesses a unique solution on
.
Remark 2 (on numerical simulations for other examples)
. The other three types of fractional difference equations, namely the forward final value problem, backward initial value problem, and backward final value problem, can be treated similarly by using the iterative schemes derived from their corresponding Volterra-type summation equations (Theorems 2–4). Numerical validations for the four formulations have already been discussed in [23]. Since the main purpose of the present work is to establish the qualitative properties, including existence, uniqueness, and continuous dependence on initial/final data, Example 1 is presented as a representative numerical illustration of the continuous dependence result (Theorem 5). Similar simulations can be performed for the remaining cases to verify the corresponding theoretical results. Example 2. Consider the final value problem of the forward fractional difference equationwhere for and . The function f satisfies the Lipschitz condition with constant , because the mapping is Lipschitz with constant 1 (its derivative is bounded by 1). Indeed,Clearly, , so the boundedness condition holds with . For Theorem 2, with , , and any , we havewhich implies that the contraction condition is automatically satisfied. Thus, by Theorem 2, problem (30) admits a unique solution on . Example 3. Consider the initial value problem of the backward fractional difference equationwhere for and . The function f satisfies the Lipschitz condition with constant , because (the derivative of arctan is bounded by 1). Thus,Moreover, , so the boundedness condition holds with . For Theorem 3, with , , and any , we havehence guarantees the contraction property. Consequently, by Theorem 3, problem (31) has a unique solution on . Example 4. Consider the final value problem of the backward fractional difference equationwhere for and . As argued in Example 2, the function is Lipschitz with constant 1, soi.e., the Lipschitz constant is . Clearly , so boundedness holds with . For Theorem 4, with , , and any , we havewhich together with satisfies the conditions of Theorem 4. Hence, problem (32) possesses a unique solution on . These four examples cover all four types of Liouville–Caputo fractional difference equations discussed in
Section 3 and demonstrate the applicability of the corresponding existence and uniqueness theorems. In each case, the chosen nonlinearities satisfy the required Lipschitz and boundedness conditions, and the inequalities involving the fractional factorials are automatically fulfilled due to the elementary estimate
for all
and
.
Example 5 (Economics: fractional inventory adjustment with memory)
. Consider the initial value problem of the forward fractional difference equation where denotes the inventory level (in thousand units) at week n. Define . It is clear that for all Q, so condition (H1) holds with . Moreover, the function f is Lipschitz with constant because the derivative of is bounded by 2. Hence, . For Theorem 1 (with , ), one verifies that for every the inequality holds (the smallest right-hand side is about at , still larger than ), and . Thus, all assumptions of Theorem 1 are satisfied, guaranteeing a unique solution for . Example 6 (Economics: inflation deviation with terminal condition)
. Consider the final value problem of the forward fractional difference equation of order , describing the evolution of inflation deviation (in percentage points) over with terminal condition at : where denotes the deviation of inflation from a target value (e.g., 2%). The nonlinear function models a central bank’s bounded adjustment rule. Clearly (condition (H1)), and f is Lipschitz with constant because the derivative of is bounded by 2. Hence, . For Theorem 2, we set , (). For each , one easily checks that holds (the smallest right-hand side is about at , still larger than ). Also, . Therefore, all conditions of Theorem 2 are satisfied, and the problem possesses a unique solution for . This means that given a known future inflation deviation (e.g., a policy target), the past path is uniquely determined. Such backward analysis is valuable for policy evaluation and calibration.