Abstract
We have developed the generalized quasilinearization method (QM) for an initial value problem (IVP) of dynamic equations on time scales by using comparison theorems with a coupled lower solution (LS) and upper solution (US) of the natural type. Under some conditions, we observed that the solutions converged to the unique solution of the problem uniformly and monotonically, and the rate of convergence was investigated.
Keywords:
quasilinearization; quadratic convergence; weak convergence; extremal solutions; time scale MSC:
34A12; 34A45; 34N05
1. Introduction
The theory of calculus on time scales was studied by Stefan Hilger [1] in 1990 to connect continuous and discrete analyses. Examining dynamic equations on time scales is a research area of great interest. Many researchers are devoted to the qualitative study of ordinary differential equations on time scale.
The QM, combined with the LS and US methods, provides a clear analytical representation for the solution of the nonlinear differential equation, providing pointwise upper and lower estimates for the solution of the problem. Using these generalized concepts given by Lakshmikantham and Köksal, [2] it was developed on time scales by Kaymakçalan and Lawrence in [3]. Bohner and Peterson [4] discussed basic information, definitions and theorems about time scale. They also dealt first and second order linear equation on time scale. In recent years, studies and articles have been argued, and extraordinary results have been obtained. Some articles and books that can be found are referenced in this article.
Akın et al. [5] applied QM to the unique solution of BVP on time scales with monotone sequences of LS and US, and the authors obtained the convergence rate. Atici and Topal [6] improved the convergence of monotone sequences on time scales for nonlinear dynamical equations. They generated two sequences that, under some favorable conditions, converged to the solution of BVP.
Daneev and Sizykh [7] investigated the convergence issues of the developed algorithm method for minimizing the generalized work functional. The authors demonstrated the algorithm using numerical stabilization of a two-dimensional oscillatory linear control system.
Yang and Vatsala [8] developed a generalized QM for reaction diffusion systems where the forcing functions are the sum of convex and concave functions. They showed the solutions of the corresponding linear systems converge monotonically, uniformly and quadratically to the unique solution of the nonlinear problem.
Idiz et al. [9] examined the numerical solution of a fractional Lane–Emdan-type equation that occurs mainly in astrophysics applications. They improved a numerical approach using QM and solved some example problems to compare with other numerical techniques in the literature.
Jyoti and Singh [10] obtained a coupled iterative approach based on a QM approximation for evaluating the solutions of nonlinear two-point Dirichlet BVP.
Lakshmikantham and Vatsala [11] applied the QM to an IVP and obtained LS and US on ordinary differential equations.
Mohammadi et al. [12] proposed a method to solve some classes of singular fractional nonlinear Lane–Emdan-type equations using the QM. They compared the results produced using the method with some well-known results to demonstrate its accuracy.
Mwakilama et al. [13] offered and applied a multivariate spectral local QM to model the transport and interaction of soil. They conducted systematic analyses on the influence of model parameters on the distribution profiles to check the accuracy of the solution. The findings correspond to the theory and literature and thus demonstrate the feasibility of solving coupled nonlinear PDEs with environmental fluid dynamics applications.
In 2023, Radhika improved the generalized QM for an IVP using the LS and US of type I and obtained the solution of a periodic boundary value problem. He also obtained the generalized quasilinearization technique BVP using the method developed for IVP for Caputo fractional differential equations.
Safari et al. [14] attempted to solve variable-order fractional diffusion problems with a fourth-order derivative term. They obtained the solution of the problem in terms of the primary approximation, as well as the corresponding correction functions at each time step by using a radial basis in functions.
Verma and Urus [15] developed a monotone iterative technique with LS and US for a class of four-point nonlinear BVP. They offered Green’s function and iterative sequences for the corresponding linear problem by using the QM.
Wang and Tian [16] studied nonlinear BVP for difference equations with causal operators. By using the methosd of LS and US together with the monotone iterative technique, they showed the existence of extremal solutions.
Wang and Wu [17] dealt with convergence for a class with a given IVP involving the sum of two terms as a right-hand function on time scales. They obtained second-order convergence of approximate solutions for approximately iterative generated series by using the generalized QM.
Torkaman et al. [18] studied the numerical solution of nonlinear multi-dimensional Volterra integral equations. The authors reduced the problem to a sequence of linear Volterra integral equations by using the QM.
Tunitsky [19] obtained necessary and sufficient conditions for local quasilinearizability of elliptic Monge-Ampère systems.
Yakar [20] investigated the qualitative behavior of perturbed dynamical systems on time scales that differed in initial time compared to unperturbed dynamical systems. The author proposed the classical concept of stability with the concept of initial time difference stability on time scales. Yakar and Arslan [21] extended the generalized QM for nonlinear terminal value problems. They obtained several conditions to apply the QM to the nonlinear problem involving Riemann–Liouville fractional derivatives.
Yüzbaşı and Izadi [22] improved two numerical methods based on Bessel polynomials. In the first method, they transformed the HIV problem into a system of nonlinear equations by using the Bessel polynomial. The other method was used to transform the HIV problem into a sequence of linear equations using the QM.
Zare et al. [23] introduced a combination of QMs to approximate the solution of nonlinear Volterra integral equations. Moreover, the authors solved the obtained linear equation in each iteration using these method.
We have given two results, with the solutions showing the practical applicability of the conditions. We believe that our study will add a new perspective to the literature.
In this paper, our aim is to elucidate the LS and US of dynamic non-linear IVP,
in the particular case of , where .
2. Preliminaries
This section reviews some basic concepts and fundamental theorems on LS and US that are crucial to obtain our results in the other sections below.
Definition 1
([24]). Any nonempty closed subset T of the real numbers is called a time scale. are examples of time scales. Furthermore, is the usual derivative if is taken.
Definition 2
([11]). A function is said to be a LS of
if
and similarly, is said to be an US if
Definition 3
([11]). Let ϖ and ε be rd-continuously differentiable functions such that on Then, ϖ and ε are called natural LS and US of (1) if
and
Theorem 1
([11]). Let be the lower and upper solutions of (2), respectively, and suppose that
whenever for some Then, implies
Theorem 2
([11]). Let be the LS and US of (2), respectively, such that on and will be bounded on Ω, where . Then, there exists a solution of (2) satisfying on whenever .
Theorem 3
(Arzela Ascoli Theorem [25]). Let be a sequence of functions defined on a compact set J that is equicontinuous and uniformly bounded on J. Then, there exists a subsequence , which is uniformly convergent on J.
3. Results
3.1. The Semi Quadraticity of the Convergence
We achieved monotone sequences on the time scale that uniformly converges to the solution of (1). Auxiliary linear IVPs were constructed for this purpose. By using QM with LS and US, under some appropriate conditions, we indicated that this convergence is semi quadratic. In addition to this, we will prefer instead of in the proof, because we are studying the time scale instead of an ordinary differential equation. Let
where
Theorem 4.
Assume that the following hypotheses hold:
- (A1)
- Let be the natural LS and US of (1) such that on J.
- (A2)
- exist as continuous functions on , where , and they satisfy whenever and exist and on And, satisfies the following inequality. For
where is a Lipschitz constant. Then, there exist monotone sequences and , and these sequences converge uniformly to the unique solution of (1). The rate of convergence is semi-quadratic.
Proof of Theorem 4.
Let us take
In view of (A2), it is clear that for
Since , and because of the properties of , we note the following inequalities. For and ,
and
Now, we consider the following auxiliary IVPs
and
Now, we will show that on Let then Taking the delta derivative of both sides, we can obtain the following:
Since then yields . Similarly, we will show that Let , then Taking the delta derivative of both sides and using (4)–(6), we can write the following:
Therefore, Hence, on J. Now, it can be shown that By using the inequalities given in (7) and (4)–(6), and using the mean value theorem, the following can be obtained:
Therefore, is an LS. Similarly, using (8), we obtain the following:
Hence, is a US. Since are the LS and US of (1) with , Theorem 1 yields . Therefore, we have established the following inequalities:
To generalize this situation, let us define . It is clear that . Taking the delta derivative of both sides and using inequalities (4)–(6) and the mean value theorem, we can obtain the following:
Since then yields . Now, we will show Based on (7), the following can be written:
By using (4)–(6), we obtain the following:
When the mean value theorem is applied, and because of the properties of and , the following inequality can be obtained:
Hence, is an LS. Similarly, we can show that is a US. Using (8),
can be written. In a similar fashion, the following can be obtained:
Consequently, is a US. Since satisfies the Lipschitz condition, from Theorem 1, it follows that . Through mathematical induction, we have
for all Since the sequences and are equicontinuous and uniformly bounded by Theorem 3, it is clear that the sequences uniformly converge to the unique solution of (1) on J. Now, we will prove this convergence is semi-quadratic on J. To show this, we define the following:
It is obvious that and . If we take the delta derivative of both sides, then we have the following:
When the mean value theorem is applied several times and when using the properties of the functions,
where can be obtained. Thus,
where
When the Cauchy inequality is applied to term then we have the following:
where .
Since
is linear in , we have to use Gronwall’s inequality. Hence, it can be written as follows:
Therefore, one can see the following:
This shows us that converges to the solution of (1) semi-quadratically. Similarly, it can be easily shown that the convergence of is also semi-quadratical. This completes the proof. □
3.2. The Weak Quadraticity of the Convergence
Under different conditions, we examined whether the rate of quadraticity changes. By placing another condition on the function , the sequences converged as weakly quadratic.
Theorem 5.
Suppose that (A1) and (A2) of Theorem 4 hold. Assume further that is a continuous function. Then, there exist monotone sequences and , which converge uniformly to the unique solution of (1), and the convergence is weakly quadratic.
Proof of Theorem 5.
In view of (4) and (5), we consider the following IVPs:
Since is the LS of (1),
is the US of (1). Based on the inequalities of (4) and (5), it can be written as follows
Therefore, and are the LS and US of (9). Based on Theorem 2, there exists a unique solution of (9) called such that Similarly, we can write
and
Hence, and are the natural LS and US of (10). Thus, there exists a unique solution of (10) called such that Now, it will be shown that By using (9), we obtain the following:
Then, we obtain the following:
Similarly, by using (8) with inequalities (2) and (3), we obtain the following:
With the condition (A2), using the mean value theorem, we have the following:
Based on Theorem 1, since the condition (3) holds and are the LS and US of (1), respectively, yields Therefore, we have established the following inequalities:
If this process continues, we have the following:
Here, the elements of the monotone sequences and are the unique solutions of the following linear IVPs:
Since the sequences and are uniformly bounded and equicontinuous, based on Theorem 3 (the Arzela Ascoli theorem), these sequences converge to the unique solution (1) uniformly. Now, we shall show that the convergence of the sequences and to the unique solution of (1) is weakly quadratic on To show this, let us define the following:
When we take the delta derivative of both sides, and using the definition together with applying the mean value theorem, the following can be obtained:
where Therefore, the following can be written:
where and are positive constants. If the Cauchy inequality is applied to the term , then we have
which is linear in Now, if Gronwall’s inequality is used, then we have the following:
Therefore, the following can be written:
This shows that converges (1) weakly quadratically. Similarly, it can be easily shown that the convergence of is also weakly quadratical. This completes the proof. □
3.3. An Example
For the particular case of system (1), let us consider the dynamic initial value problem:
where and Let
for all It can be determined that
and
This implies that (LS) and (US) are of a natural type for (1). Consider the functions
and
These satisfy the following inequality:
Similarly, and can be obtained if it can be continued like this. Then, all the conditions of Theorem 5 are satisfied. Thus, we obtain the existence of monotone sequences that converge uniformly to the unique solution of (1).
4. Discussion
We have applied the QM to the given nonlinear differential equations on time scales. Under certain conditions, we have constructed monotone sequences that converge uniformly and monotonically to the unique solution of the problem. The most important advantage of this method is that each element of the monotone function sequence is the solution of linear differential equations. Also, it has been shown that the convergence is semi-quadratic in the first result and weakly quadratic in the other.
5. Conclusions
In this paper, by using the technique of upper and lower solutions, under convenient conditions, we showed that the convergence is semi-quadratic and weakly quadratic. We observed that similar results were obtained in parallel with the results given by the classical derivative. The novelty of the applied quasilinearization method is the change in the convergence speed under different conditions. While the rate of convergence is semi-quadratic in Section 3, it is weakly quadratic in Section 4. Therefore, it was seen that the rate of convergence may also change if the conditions of the functions are changed.
Author Contributions
Investigation, Ş.Ç., Y.Y. and C.Y. All authors have read and agreed to the published version of the manuscript.
Funding
This publication was supported by the Sakarya University and TUBITAK (The scientific and Technological Research Council of Türkiye).
Data Availability Statement
The data are contained within this published article.
Acknowledgments
The first author would like to thank TUBITAK (The scientific and Technological Research Council of Türkiye) for their financial support during his PhD studies.
Conflicts of Interest
The authors declare no conflicts of interest.
Abbreviations
The following abbreviations are used in this manuscript:
| QM | Quasilinearization method |
| LS | Lower solution |
| US | Upper solution |
| IVP | Initial value problem |
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