1. Background
In this section, we introduce some basic notations, a new matrix Mittag–Leffler function as well as Babenko’s approach with an illustrative example solving a fractional differential equation.
Let
(
) and
. It follows from [
1] for
that:
In particular, for
, we have:
Let
. Then, from [
2]
where the operator
is the partial Riemann–Liouville fractional integral of order
with respect to
x with initial point zero [
3]:
and
is the partial Liouville–Caputo fractional derivative of order
with respect to
x is defined as the Banach space with the norm given as:
In this paper, we study the uniqueness of the solutions to the following equation with the boundary condition for
and
in the space
for constants
:
where
for all
,
, and
is a function satisfying certain conditions to be given. Equation (
5), with its boundary condition, is new and, to the best of our knowledge, has never previously been investigated. This research has many potential applications since uniqueness is an important topic in many scientific areas, such as control theory, and the method used clearly opens up new directions for studying other types of equations with initial or boundary problems.
A function
Z is a solution of the problem (
5) if it satisfies the equation over
and its boundary condition
.
Let
for all
, and
Definition 1. A matrix Mittag–Leffler function is defined by the following series:where for andClearly, the above series converges since there is a positive number δ such thatIn particular,which is the multivariate Mittag–Leffler function given in [4], with Moreover,which is the well known two-parameter Mittag–Leffler function, with Remark 1. In 2018, Garrappa and Popolizio [5] also defined the matrix Mittag–Leffler function to study fractional calculus. They called it the Mittag–Leffler (ML) function with matrix arguments, which is based on the spectrum of the matrix with the Jordan canonical form, and is totally different from Definition 1. Babenko’s approach [
6] is an efficient tool for dealing with integral and differential equations with initial conditions or boundary value problems [
1]. The method itself is similar to the Laplace transform while working on differential and integral equations with constant coefficients, but it can be applied to equations with continuous and bounded variable coefficients. To show this method in detail, we will derive the solution to the following equation with the initial conditions in the space
for constant
:
where
and
From (
2), one has
Applying
to the first equation in (
12), we have
Treating the operator
as a variable, we informally derive using Babenko’s approach
using
This infers that
which claims that the series solution
is in
.
We can also readily show from a different point of view: is an entire function of t so it is continuous. Hence, is continuous.
Fractional nonlinear PDEs have been used to describe many different physical systems, ranging from optical fibres to dynamical processes in various scientific fields [
7,
8]. There are many interesting studies on the uniqueness and existence of solutions, based on the theory of fixed points, for fractional nonlinear integro-differential equations with initial value or boundary condition problems, as well as for integral equations [
9]. Very recently, Li [
1] investigated the uniqueness of solutions for the following equation with the initial conditions for any positive integer
m:
in the Banach space
where
,
, and
The remainder of this paper is structured as follows.
Section 2 studies the uniqueness of the solutions to the problem (
5) using the matrix Mittag–Leffler function given above, Babenko’s approach and Banach’s contractive principle.
Section 3 presents an example to demonstrate the applications of the main results based on the value of a matrix Mittag–Leffler function evaluated using our Python code. In
Section 4, we provide a summary of the work.
3. Example
Example 1. Consider the following equation:
where
Then, the problem (
35) has a unique solution in the space
.
Proof. Clearly,
and
for all
, noting that
. Therefore,
. We evaluate the following
D given in Theorem 2 using our Python code to get
Using Theorem 2, the problem (
35) has a unique solution in the space
. □
The following is our Python code used to evaluate D given in Theorem 2:
import math
from sympy import gamma
def partition(n, m):
if m == 1:
yield (n,)
else:
for i in range(n+1):
for j in partition(n-i, m-1):
yield (i,) + j
def ME(M, z): #Matrix Mittag-Leffler function
m = len(M)
n = len(M[0])
zl = len(z)
result = 0
for l in range(0, 40): #approximate upper bound
for l_partition in partition(l, zl):
if all(map(lambda x: x >= 0, l_partition)):
combination = 1
for i in range(zl):
combination *= math.factorial(l_partition[i])
combination = math.factorial(l) / combination
gamproduct = 1
for i in range(m):
gaminput = sum([M[i][j] * l_partition[j]
for j in range(zl)]) + M[i][zl]
gamproduct *= gamma(gaminput)
numerator = 1
for i in range(zl):
numerator *= z[i] ** l_partition[i]
result += (numerator / gamproduct) * combination
return~result
#Matrices
MQ15 = [[1.5, 1.5, 1.5, 2.5], [1.1, 1.2, 1.3, 1],
[1.2, 1.3, 1.4, 1], [1.3, 1.4, 1.5, 1], [1.4, 1.5, 1.6, 1]]
MQ1 = [[1.5, 1.5, 1.5, 2], [1.1, 1.2, 1.3, 1], [1.2, 1.3, 1.4, 1],
[1.3, 1.4, 1.5, 1], [1.4, 1.5, 1.6, 1]]
M1 = [[1.5, 1.5, 1.5, 2], [1.1, 1.2, 1.3, 2.1], [1.2, 1.3, 1.4, 2.2],
[1.3, 1.4, 1.5, 2.3], [1.4, 1.5, 1.6, 2.4]]
M2 = [[1.5, 1.5, 1.5, 2], [1.1, 1.2, 1.3, 2.2], [1.2, 1.3, 1.4, 2.3],
[1.3, 1.4, 1.5, 2.4], [1.4, 1.5, 1.6, 2.5]]
M3 = [[1.5, 1.5, 1.5, 2], [1.1, 1.2, 1.3, 2.3], [1.2, 1.3, 1.4, 2.4],
[1.3, 1.4, 1.5, 2.5], [1.4, 1.5, 1.6, 2.6]]
z = [0.5, 0.25, 0.125]
#Calculation in example
result = 0.38*(ME(MQ15, z) + (1/gamma(2.5))*ME(MQ1, z)) + (1/gamma(2.5))
*(0.5*ME(M1, z) + 0.25*ME(M2, z) + 0.125*ME(M3, z))
print(result)
Remark 2. The Python language is quite powerful in computing values of the multivariate Mittag–Leffler function or the newly introduced matrix multivariate Mittag–Leffler function. Indeed, these functions often appear in various fields and play an important role in studying integral or differential equations with initial or boundary conditions, as well as in finding approximate solutions.