Sharpness Estimation of Hankel Determinants and Logarithmic Coefficients for a Family of Analytic Functions Related to a Lung-Shaped Domain
Abstract
1. Introduction, Definitions and Notations
- Limacon-Shaped Domain: Functions subordinate to images of the function ; see [34].
2. The Lung-Shaped Domain and Auxiliary Tools
- Schwarz function and subordination representation. Since , there exists a Schwarz function , analytic in with and for all , such thatWriting the power-series expansion of r asand substituting into (4), a term-by-term expansion of in powers of yields explicit expressions for the Taylor coefficients of in terms of .
- Carathéodory family and the function . A key bridge between the subordination condition (4) and classical coefficient methods is provided by the Carathéodory family , consisting of all analytic functions with and for , admitting the Taylor expansionThe connection to the Schwarz function r is established via the standard Möbius-type relationwhich allows one to translate bounds on into the well-developed coefficient theory for .
- (i)
- Express and expand both sides as power series in , equating coefficients to obtain in terms of .
- (ii)
- Pass from the Schwarz coefficients to the Carathéodory coefficients via (7), exploiting the classical bounds and the Carathéodory parametrization recalled in Lemma 2 below.
- (iii)
- Apply sharp estimates for and related functionals (Lemmas 1 and 2) to obtain the stated bounds on , and verify sharpness by exhibiting extremal functions.
3. Set of Lemmas
- The principal aims of this investigation are:
- (I)
- Initial Taylor–Maclaurin Coefficient Estimation. To establish sharp bounds for the coefficients , , , and for functions . These coefficients are the most fundamental geometric descriptors of the mapping , encoding information about its local distortion and the shape of the image domain; their estimation is the natural starting point for any systematic study of a new function family.
- (II)
- Logarithmic Coefficient Bounds. To determine sharp bounds for the logarithmic coefficients , and . These coefficients control the distortion of the function and play a central role in the Milin–Lebedev inequalities, which in turn underlie de Branges’ celebrated proof of the Bieberbach conjecture.
- (III)
- Moduli Differences in Logarithmic Coefficients. To establish a sharp bound for the difference . Such difference estimates refine the individual bounds obtained in (ii) by capturing the relative growth of successive logarithmic coefficients, a question that has attracted considerable attention in the recent literature on coefficient problems for subfamilies of univalent functions.
- (IV)
- Hankel Determinant Analysis. To derive sharp estimates for the Hankel determinants , , and . These determinants encode deeper structural information about the Taylor coefficients than individual bounds can provide: recovers the classical Fekete–Szegö functional, while and measure higher-order coefficient interactions and have direct connections to the theory of Padé approximants and the study of bounded-characteristic functions.
- (V)
- Zalcman Functional Bounds. To obtain a sharp bound for the Zalcman functional . The Zalcman conjecture, which asserts that for every univalent function, has stimulated a rich body of research; establishing its sharp form for the family contributes to this programme and complements the Hankel determinant estimates in (iv).
- (VI)
- Sharpness Verification. To demonstrate that every bound obtained in (i)–(v) is sharp by constructing explicit extremal functions. Sharpness is the hallmark of a complete coefficient result: it confirms that the estimates cannot be improved and identifies the functions at which equality is attained, thereby giving a precise geometric characterization of the extremal configurations within .
4. Initial Coefficient Bounds
- I.
- Interior points of cuboidWe attempt to determine a maximum of within the interior of the cuboid. Let .Considering yields . The point is critical within the interval if the inequality holds.After simple calculations, we get that does not hold for . Consequently, it may be deduced that the function does not contain critical points throughout the interior of the cuboid .Assume to be a critical point of located within the interior of the cuboid defined by the intervals . Given that , it may be mentioned thatConsequently,By assuming , which indicates . Although, , it ensues thatEstablishing yields , which represent a local maximum of . As a result,Therefore,which agree with the behavior of shown in Figure 2.
- II.
- On the six faces of the cuboidSubsequently, we aim to determine the maximum possible value of the function across all six sides of the cuboid .
- (i)
- Regarding the face , is transformed intoFollowing thatConsequently, has no optimal points in the region .
- (ii)
- Regarding the face , is turned to
- (iii)
- Regarding the face , is converted toFollowing thatConsequently, has no any optimal points within the region .
- (iv)
- Regarding the face , is evolved toBy setting , we identify the critical point , at which reaches its maximum value; specifically,
- (v)
- Regarding the face , is transformed toBased on optimization, we deduce that the system of equations and has a distinct solution within the region , referred to as . Given that,Consequently, has no extreme points.
- (vi)
- Regarding the face , is converted toThrough a series of complicated steps, we conclude that the system of equations and has no possible solutions within the area .
- III.
- On the twelve edges of the cuboidFinally, we have to establish the maximum of along the twelve edges of the cuboid .
- (i)
- On and , turns toIt appears that for , indicating that is a decreasing function, with its maximum value established as follows:
- (ii)
- On (similarly and & and ), we have
- (iii)
- On and , transforms intoIt appears that for , indicating that is a decreasing function, with its maximum value established as follows:
- (iv)
- On and (similarly for and ), converts toProviding yields a critical point at . However, , indicating that reaches its maximum value at as follows:
- (v)
- On and , evolves intoIt is worth noting that for , indicating that is a decreasing function, with its maximum value derived to be as follows:
- (vi)
- On and , transforms intoProviding generates a critical point at . However, , implying that approaches its maximum value at as follows:
5. The Logarithmic Coefficients Estimate
- (I)
- Interior points of cuboid:We shall evaluate the maximum value of within the interior of cuboid . Suppose that .By putting , we obtainWherever is a critical point within , therefore , which is acceptable as long asIt is evident from the straightforward computations that (35) is valid merely for . Consequently, we may assert that the function possesses a critical point within the cuboid . Hence,Let us considerwhere is recorded in Appendix A.Assuming that , we conclude that . Consequently,whereDifferentiating with respect to and equating to zero, we possess a critical point at , which represents a local maximum. Therefore,Thus, gets its maximum within the interior of the cuboid and bewhich is compatible with the behavior of the function , as shown in Figure 3.
- (II)
- On the six faces of cuboid:Subsequent to the previous discussion, we will look at the maximum value of the function across the six faces of the cuboid . This analysis aims to identify the optimal solutions within the defined parameters of the cuboidal structure.
- (i)
- Regarding the face , turns intoSince for every , then will be a decreasing function and has no optimal point over the region .
- (ii)
- Regarding the face , becomes
- (iii)
- Regarding the face , converts toLet us analyze , which leads to , a critical point representing a local maximum; hence,Given that for all , we may deduce that has no optimum points in the area .
- (iv)
- Regarding the face , transforms intoConsidering , which leads to , is a critical point and exhibits a local maximum; hence,
- (v)
- Regarding the face , turns intoBy setting , , solve the system within the region . The optimization reveals that the function has a critical point at . However,Therefore, does not possess a maximum within .
- (vi)
- Regarding the face , converts intoBy setting , , solve the system within the region . Upon using the numerical optimization, the function has a critical point at approximately . However,Therefore, attains its maximum within as follows:which agree with the behavior of , illustrated in Figure 4.
- III.
- On the twelve edges of cuboid:Afterwards, an evaluation will be performed to ascertain the maximum values of the function along the twelve edges of the cuboidal region, denoted as .
- (i)
- On , we haveSince for all , then is a decreasing function. Thus,
- (ii)
- On and , we obtainSince for all , then is an increasing function. Therefore, the function attains it maximum at . Hence,
- (iii)
- On and , we getGiven that , for every , it follows that is a decreasing function. Consequently, the function reaches its maximum at as follows:
- (iv)
- On and , we have
- (v)
- On , we obtain
- (vi)
- On and (similarly for and ), we getLet us consider , which leads to , a critical point that also represents a local maximum. Thus,
- (vii)
- On and , we haveSince, for all , then is a decreasing function. Therefore, the function attains it maximum at as follows:
- (viii)
- On and , we obtainLet us consider , which leads to , a critical point that also represents a local maximum. Hence,
6. Second-Order Hankel Determinants and
7. Third-Order Hankel Determinant
- I.
- Interior points of cuboid:In this step, we are going to evaluate the maximum value of inside of the cuboid . Let .Setting providesWhile is a critical point within ; hence, , which is acceptable only ifandTo detect the critical point, it is necessary to evaluate a solution that fulfills (43) and (44). Let us discussSince for , indicating that is an increasing function. Therefore,Simple calculations show that (43) is invalid for . Consequently, it may be inferred that the function does not have critical points inside the cuboid .
- II.
- On the six faces of the cuboid:We are now going to evaluate the maximum value of the function throughout the six faces of the cuboid .
- (i)
- Regarding the face , transforms intoHence,Consequently, has no optimal points in the region .
- (ii)
- Regarding the face , becomes
- (iii)
- Regarding the face , evolves intoAssuming that , which providesIf is a critical point, it must concurrently fulfill the subsequent inequalities:It is obvious that ; however, fundamental calculations indicate that this condition is not valid for any . Consequently, does not possess optimal points in the region .
- (iv)
- Regarding the face , transforms intoHence,This indicates that an optimal solution does not exist for .
- (v)
- Regarding the face , converts into recorded at Appendix B. By providing the system and , which seems to have no solutions within the region . Consequently, has no optimal solution.
- (vi)
- Regarding the face , evolves into recorded at Appendix B. By establishing the system and , which appears to have no solutions within the region . Consequently, does not possess any optimal solution.
- III.
- On the twelve edges of the cuboid:At last, it is necessary to evaluate the maximum values of along the twelve edges of the cuboid .
- (i)
- On , converts intoIt is evident that for , indicating that is an increasing function, approaching its maximum at . As a result,
- (ii)
- On and , evolves intoBy setting , a critical point is identified at , displaying a local minimum. The maximum value is derived from the following:
- (iii)
- On , reduces toIt is noted that for , indicating that is an increasing function; thus, the maximum occurs at as follows:
- (iv)
- On and (similarly ); becomesIt is evident that for . Consequently, is an increasing function, thus the maximum is attained at exactly as follows:
- (v)
- On and , gets
- (vi)
- On ,
- (vii)
- On , transforms intoIt is evident that , providing a critical point , which refers to a local maximum. That is, reaches its maximum as illustrated:
- (viii)
- On and , turns toConsider , which provides a critical point , which represents a local maximum. That is, reaches its maximum as illustrated:
8. Zalcman Functional Bounds
9. Moduli Differences in Logarithmic Coefficients
10. Discussion
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
Appendix A. Expressions in Theorem 2
Appendix B. Expressions in Theorem 5
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Mamon, M.A.; Alyusof, S.; Alyusof, R.; El-Qadeem, A.H. Sharpness Estimation of Hankel Determinants and Logarithmic Coefficients for a Family of Analytic Functions Related to a Lung-Shaped Domain. Mathematics 2026, 14, 1240. https://doi.org/10.3390/math14081240
Mamon MA, Alyusof S, Alyusof R, El-Qadeem AH. Sharpness Estimation of Hankel Determinants and Logarithmic Coefficients for a Family of Analytic Functions Related to a Lung-Shaped Domain. Mathematics. 2026; 14(8):1240. https://doi.org/10.3390/math14081240
Chicago/Turabian StyleMamon, Mohamed A., Shams Alyusof, Rabab Alyusof, and Alaa H. El-Qadeem. 2026. "Sharpness Estimation of Hankel Determinants and Logarithmic Coefficients for a Family of Analytic Functions Related to a Lung-Shaped Domain" Mathematics 14, no. 8: 1240. https://doi.org/10.3390/math14081240
APA StyleMamon, M. A., Alyusof, S., Alyusof, R., & El-Qadeem, A. H. (2026). Sharpness Estimation of Hankel Determinants and Logarithmic Coefficients for a Family of Analytic Functions Related to a Lung-Shaped Domain. Mathematics, 14(8), 1240. https://doi.org/10.3390/math14081240

