Abstract
In this paper, the new subclass of a linear differential operator’s associated with multivalent analytical function has been introduced. Further, the coefficient inequalities, extreme points for the extremal function, sharpness of the growth and distortion bounds, partial sums, starlikeness, and convexity of the subclass is investigated.
Keywords: Multivalent functions, Convolution, Derivative operators
Subject terms: Materials science, Mathematics and computing
Introduction
Assume that is the unit circle and that is an analytical function, as exhibited by the power series.
1 |
The sequence of coefficients in (1) is the basis for the function , which maps onto a sub-domain of a Riemann surface.
An attribute of geometry of is described by the statement that the univalent function is in . By definition, has the property of being univalent in if
2 |
Briefly, is said to be univalent in if it does not take any value more than once for in .
The image of creates a simple domain in the w-plane, provided is univalent.
The multivalent function is a logical consequence of the idea of the univalent function. Assume that . It is said that has p roots in and that the function denotes p-valent in Meanwhile, the constraints
3 |
for a certain pair, ensure that . To put it simply, is p-valent in assuming that some value but no value exceeds p times.
Typically, 1907 the work of Koebe20 was considered the earliest stages of the concept of univalent functions. In 1933 by Montel21 and in 1938 by Biernacki7 were given two credible evaluations of the research on univalent and multivalent functions. After that, the volume of information grew rapidly, as usual, making it challenging for researchers to ascertain the current situation. Books from Schaeffer and Spencer26, Jenkins19, and others explore specialized parts of the topic in great detail. The writings by Hayman18 and Goluzin15 provided a thorough overview, and it contained enough unresolved issues for a while. The study of fragments by Bernardi6 and Hayman17 offered additional direction in the field.
The differential and integral operators of normalized analytic functions have recently gained a lot of popularity. Numerous articles covered the operators and generalizations made by various authors. In 1975, Ruscheweyh24 introduced the differential operator and it is generalized by Salagean25 in 1985. For a long time, these two operators were utilized to investigate various subclasses of univalent function by researchers. In the year of 2004 Al-Oboudi’s2 generalized of the Salagean operator, followed by Shaqsi and Darus3,4 generalized the Ruscheweyh and Salagean differential operators in 2008. Following that, several authors began to develop new operators based on the Salagean and Ruscheweyh in their own distinctive style. For example, see5,8–10,12,13,16,22,23,27–31. By the help of this survey, in this current work, certain properties of subclass of new linear differential operator of multivalent functions have been investigated.
Let be called a class of multivalent analytic functions
4 |
belongs to
For , Aghalary et al.1 studied the following multiplier transformation operator
5 |
For a new differential operator has defined by
Similarly,
6 |
Remark 1.1
For in (6), the multiplier transformations are obtained. It was stated by Aghalary et al.1.
For in (6), the operator is obtained. It was presented by Cho and Srivastava11.
For in (6), the differential oprator was introduced by Uralegaddi et al.32.
The operator is stated by Salagean25 for in (6).
For in (6), the multiplier transformation is obtained; it was introduced by Flett14.
The class
Definition 2.1
Let denote the subclass of in which
7 |
Definition 2.2
Let represents a subclass belonging to
then
8 |
where , and all
Estimate the coefficient inequality
The concepts of univalent and multivalent functions are crucial while studying complex analysis. They are usually defined on the complex plane. It is customary in this context to estimate the coefficients of these functions, more precisely, their inequalities. We will gain insight into the branching structure of multivalent functions by estimating their coefficients. The coefficient inequalities provide information about how branch points behave over the complex plane of the function. In both cases, understanding the coefficients and their inequalities in univalent and multivalent functions are essential for various applications in complex analysis, particularly in the fields of conformal mapping, complex geometry, and Riemann surfaces. The coefficient estimation provides valuable information about the behavior of functions and its geometric properties, helping mathematicians and scientists work with them effectively in various contexts.
Theorem 2.1
Let , then
9 |
Proof
Let
By the condition of the class,
There exist a schwarz function , with and such that
This implies that
We know that
Then
The last expression is bounded by 1, if
Which implies that,
where Hence the equation (9) is hold.
Corollary 2.1
Let then
10 |
and the equality is concluded for the function is given by
11 |
Extreme points
Extremal points are analyses in the framework of multivalent functions in order to comprehend branch cuts, singularities, and branching behavior. It is essential to comprehending the function of complex structure and Riemann surface.
Theorem 2.2
Let
where
Then only when it is in the form
where and
Proof
Let assume that
Then
Thus,
which demonstrates
Conversely,
Consider this
While
Let
Thus,
Growth and distortion theorems
Growth and distortion theorems are useful tools in the study of univalent and multivalent functions because they help to characterize and comprehend the behavior of these functions and how they relate to the geometry of the complex plane. According to the growth theorem, a complex-valued function is inherently constant if it is entire and bounded. The geometry of curves and regions in the complex plane is influenced by analytic functions, as revealed by the distortion theorem. It sets limits on the maximum amount of stretching or distortion that can happen when a function transfers a region or curve from one domain to another. By using these theorems, mathematicians and researchers can study the behavior of complex analytic functions and how it impacts the sizes and shapes of curves and regions in the complex plane.
Theorem 2.3
If ,then
provided The result called as sharp for
Proof
By making use of the inequality (9) for together with
then
12 |
By using (12) for the function , since
and similarly,
Theorem 2.4
If ,then
provided Clearly, the outcome is sharp for
Proof
By using the inequality (9) for , then
By using (12), then
For the function , then
and similarly,
Convexity and starlikeness
The coefficient inequalities of power series functions are frequently caused by starlikeness and convexity. Starlike functions fulfill the well-known Bieberbach conjecture, which gives restriction on the coefficients of starlike function. The geometric shapes can be preserved by mapping functions that are starlike or convex. The starlikeness and convexity of multivalent functions maintain specific structures, these qualities are crucial.
Theorem 2.5
Let then the subclass claimed as convex .
Proof
Let
contains
it is necessary to show that
while
which implies that
Thus
Hence called convex.
Theorem 2.6
If , then according to order f is p-valently convex in the disc , where
The bound for is sharp for each ,with the form (11) serving as the extreme function.
Proof
If and f is claimed orderly convex of then it is required to prove that
13 |
Now, the equation (13) gives
14 |
15 |
In the view of (13), it follows that (15) is true if
16 |
Setting in (16), the result follows. The sharpness can be verified.
Theorem 2.7
If , then according to order f is p-valently starlike in the disc , where
The bound for is sharp for each , with the form (11) serving as the extreme function.
Proof
If and f is claimed orderly starlike of then it is required to demonstrate that
17 |
Now, the equation (17) gives
18 |
From (17) and (18), the following equation obtain
19 |
In the view of (17), it follows that (19) is true if
20 |
Setting in (20), the result follows. The sharpness can be verified.
Partial sums
The concept of partial sums is one that is commonly used in the study of infinite series. On the other hand, partial sums are useful in complicated analysis and can be used in many other mathematical situations, including function analysis. This section looks into the relationship between form (4) and its series of partial sums.
and
when the coefficients are small enough to satisfy the analogous condition
It can be written as
where
Then .
Theorem 2.8
If , satisfying (7),then
Proof
Clearly ,
Utilising (4), to get
Let
Through basic computations, there is
which gives,
Hence will give the sharp result.
Theorem 2.9
If and satisfies (7). Then
Proof
Clearly .
Let
Through basic computations, there is
Hence, the result
is sharp for all n.
Theorem 2.10
If , satisfying (7), then
Proof
Clearly .
Let
Through basic computations, there is
which gives,
Hence the result is sharp.
Theorem 2.11
If , satisfying (7), then
Proof
Clearly .
Let
Through basic computations, there is
which gives,
Hence the result is sharp.
Graphical representation for the function
Functions that operate on Complex numbers are referred to as Complex functions. An extension of the complex functions that accepts a complex number as input and returns a complex number is output. Input has two dimensions of information and output another two, giving us a total of four dimensions to fit into our graph. It is challenging to draw the graph for complex functions. Even though the Complex functions are often used for mapping and transformation, such as conformal mapping in complex analysis. The phase and absolute value diagrams help visualize how these mappings and transformations alter the complex plane, preserving angles or shapes, which is a fundamental property of conformal mappings. The conformal mappings find applications in engineering and physics, where complex numbers describe electrical circuits, waves, and quantum mechanics, among other things. Understanding the phase and magnitude of complex functions is essential for solving problems in these domains.
Phase and absolute value diagrams, also known as Argand diagrams or complex plane diagrams, are useful tools for visualizing and analyzing complex functions, whether they are univalent or multivalent. These diagrams help us understand the behavior of complex functions in terms of their magnitude (absolute value) and phase (argument) at various points in the complex plane. The phase diagram can help identify singularities (such as poles and branch points) as they typically manifest as discontinuities or infinite slopes in the diagram. The absolute value diagram can show the behavior of the function near these points, indicating if it approaches infinity or remains bounded.
In this section, the phase and absolute values of the function from (11) have been examined (Figs. 1, 2, 3, 4 and 5) with various parameters and the following graphs (Figs. 1, 2, 3, 4 and 5) are drawn by using MATLAB. The phase and absolute values for the figures provide a geometric and intuitive way to understand the behavior of complex functions. They are particularly useful when dealing with univalent and multivalent functions, as they help identify key features, singularities, and transformations in the complex plane, making complex analysis more accessible and insightful.
Conclusions
In this article, the coefficient inequality, extreme points, growth and distortion, starlikeness and convexity, and partial sums for a new subclass by using the linear operator have been examined. Furthermore, the graphs of extremal functions are analyzed in terms of how it has been expressed while replacing the suitable values of the parameters. This work motivates the researchers to extend the results of this article into some new subclasses of meromorphic functions and q-calculus.
Acknowledgements
The study was funded by Researchers Supporting Project number (RSPD2024R749), King Saud University, Riyadh, Saudi Arabia.
Author contributions
All the equally are equally contributed.
Data availibility
No datasets were generated or analysed during the current study.
Competing interests
The authors declare no competing interests.
Footnotes
Publisher's note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
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