1 | 2 | 3 | 4 | 5 | 6 | 7 | ||
1 | 1 | 1 | ||||||
2 | 0 | 1 | 1 | |||||
3 | 0 | 1 | 1 | 2 | ||||
4 | 0 | 1 | 2 | 2 | 5 | |||
5 | 0 | 2 | 4 | 5 | 5 | 16 | ||
6 | 0 | 5 | 10 | 14 | 16 | 16 | 61 | |
7 | 0 | 16 | 32 | 46 | 56 | 61 | 61 | 271 |
Diabetes is one of the four major types of noncommunicable diseases (cardiovascular disease, diabetes, cancer and chronic respiratory diseases). It is chronic condition that occurs when the body does not produce enough insulin therefore results in raised blood sugar levels. Insulin is a hormone that regulates the blood sugar when food consumption. If the proper treatment is not received organs of the body like kidneys, nervous system and eyes can deteriorate. Therefore, it is better to predict diabetes as early as possible because lead to serious damage to many of the body's systems. In this paper, we modify extragradient method with an inertial extrapolation step and viscosity-type method to solve equilibrium problems of pseudomonotone bifunction operator in real Hilbert spaces. Strong convergence result is obtained under the assumption that the bifunction satisfies the Lipchitz-type condition. Moreover, we show choosing stepsize parameter in many ways, this shows that our algorithm is flexible using. Finally, we apply our algorithm to solve the diabetes mellitus classification in machine learning and show the algorithm's efficiency by comparing with existing algorithms.
Citation: Suthep Suantai, Watcharaporn Yajai, Pronpat Peeyada, Watcharaporn Cholamjiak, Petcharaporn Chachvarat. A modified inertial viscosity extragradient type method for equilibrium problems application to classification of diabetes mellitus: Machine learning methods[J]. AIMS Mathematics, 2023, 8(1): 1102-1126. doi: 10.3934/math.2023055
[1] | Heesung Shin, Jiang Zeng . More bijections for Entringer and Arnold families. Electronic Research Archive, 2021, 29(2): 2167-2185. doi: 10.3934/era.2020111 |
[2] | Bin Han . Some multivariate polynomials for doubled permutations. Electronic Research Archive, 2021, 29(2): 1925-1944. doi: 10.3934/era.2020098 |
[3] | Shishuo Fu, Zhicong Lin, Yaling Wang . Refined Wilf-equivalences by Comtet statistics. Electronic Research Archive, 2021, 29(5): 2877-2913. doi: 10.3934/era.2021018 |
[4] | Jiafan Zhang . On the distribution of primitive roots and Lehmer numbers. Electronic Research Archive, 2023, 31(11): 6913-6927. doi: 10.3934/era.2023350 |
[5] | Dmitry Krachun, Zhi-Wei Sun . On sums of four pentagonal numbers with coefficients. Electronic Research Archive, 2020, 28(1): 559-566. doi: 10.3934/era.2020029 |
[6] | Massimo Grossi . On the number of critical points of solutions of semilinear elliptic equations. Electronic Research Archive, 2021, 29(6): 4215-4228. doi: 10.3934/era.2021080 |
[7] | Ji-Cai Liu . Proof of Sun's conjectural supercongruence involving Catalan numbers. Electronic Research Archive, 2020, 28(2): 1023-1030. doi: 10.3934/era.2020054 |
[8] | Hongjian Li, Kaili Yang, Pingzhi Yuan . The asymptotic behavior of the reciprocal sum of generalized Fibonacci numbers. Electronic Research Archive, 2025, 33(1): 409-432. doi: 10.3934/era.2025020 |
[9] | Hanpeng Gao, Yunlong Zhou, Yuanfeng Zhang . Sincere wide τ-tilting modules. Electronic Research Archive, 2025, 33(4): 2275-2284. doi: 10.3934/era.2025099 |
[10] | Taboka Prince Chalebgwa, Sidney A. Morris . Number theoretic subsets of the real line of full or null measure. Electronic Research Archive, 2025, 33(2): 1037-1044. doi: 10.3934/era.2025046 |
Diabetes is one of the four major types of noncommunicable diseases (cardiovascular disease, diabetes, cancer and chronic respiratory diseases). It is chronic condition that occurs when the body does not produce enough insulin therefore results in raised blood sugar levels. Insulin is a hormone that regulates the blood sugar when food consumption. If the proper treatment is not received organs of the body like kidneys, nervous system and eyes can deteriorate. Therefore, it is better to predict diabetes as early as possible because lead to serious damage to many of the body's systems. In this paper, we modify extragradient method with an inertial extrapolation step and viscosity-type method to solve equilibrium problems of pseudomonotone bifunction operator in real Hilbert spaces. Strong convergence result is obtained under the assumption that the bifunction satisfies the Lipchitz-type condition. Moreover, we show choosing stepsize parameter in many ways, this shows that our algorithm is flexible using. Finally, we apply our algorithm to solve the diabetes mellitus classification in machine learning and show the algorithm's efficiency by comparing with existing algorithms.
The Euler numbers
1+∑n≥1Enxnn!=tanx+secx. |
This is the sequence A000111 in [20]. In 1877 Seidel [19] defined the triangular array
En,k=En,k−1+En−1,n+1−k(n≥k≥2) | (1) |
with
E1,1E2,1→E2,2E3,3←E3,2←E3,1E4,1→E4,2→E4,3→E4,4⋯=10→11←1←00→1→2→2⋯ | (2) |
The first few values of
1 | 2 | 3 | 4 | 5 | 6 | 7 | ||
1 | 1 | 1 | ||||||
2 | 0 | 1 | 1 | |||||
3 | 0 | 1 | 1 | 2 | ||||
4 | 0 | 1 | 2 | 2 | 5 | |||
5 | 0 | 2 | 4 | 5 | 5 | 16 | ||
6 | 0 | 5 | 10 | 14 | 16 | 16 | 61 | |
7 | 0 | 16 | 32 | 46 | 56 | 61 | 61 | 271 |
André [1] showed in 1879 that the Euler number
DU4={2143,3142,3241,4132,4231}. |
In 1933 Kempener [14] used the boustrophedon algorithm (2) to enumerate alternating permutations without refering to Euler numbers. Since Entringer [7] first found the combinatorial interpretation of Kempener's table
Theorem 1 (Entringer). The number of the (down-up) alternating permutations of
DUn,k:={σ∈DUn:σ1=k}. |
According to Foata-Schützenberger [9] a sequence of sets
The Springer numbers
1+∑n≥1Snxnn!=1cosx−sinx. |
Arnold [2,p.11] showed in 1992 that
1ˉ23,1ˉ32,1ˉ3ˉ2,213,2ˉ13,2ˉ31,2ˉ3ˉ1,312,3ˉ12,3ˉ21,3ˉ2ˉ1, |
where we write
S1,−1S2,2←S2,1S3,−3→S3,−2→S3,−1S4,4←S4,3←S4,2←S4,1⋯⇕12←10→2→316←16←14←11⋯S1,1S2,−1←S2,−2S3,1→S3,2→S3,3S4,−1←S4,−2←S4,−3←S4,−4⋯⇕11←03→4→411←8←4←0⋯ |
where
Sn,k={Sn,k−1+Sn−1,−k+1if n≥k>1,Sn,−1if n>k=1,Sn,k−1+Sn−1,−kif −1≥k>−n. | (3) |
Theorem 2 (Arnold). For all integers
Sn,k:={σ∈Sn:σ1=k}. |
Moreover, for all integers
Sn,k=#{σ∈DUn(B):σ1=k}. |
Similarly, the numbers
-6 | -5 | -4 | -3 | -2 | -1 | 1 | 2 | 3 | 4 | 5 | 6 | ||
1 | 1 | 1 | |||||||||||
2 | 0 | 1 | 1 | 2 | |||||||||
3 | 0 | 2 | 3 | 3 | 4 | 4 | |||||||
4 | 0 | 4 | 8 | 11 | 11 | 14 | 16 | 16 | |||||
5 | 0 | 16 | 32 | 46 | 57 | 57 | 68 | 76 | 80 | 80 | |||
6 | 0 | 80 | 160 | 236 | 304 | 361 | 361 | 418 | 464 | 496 | 512 | 512 |
This paper is organized as follows. In Section 2, we shall give the necessary definitions and present our main results. The proof of our theorems will be given in Sections 3-4. In Section 5, we shall give more insightful description of two important bijections. More precisely, Chuang et al.'s constructed a
Let
For each vertex
Definition 3. Given an increasing 1-2 tree
Let
Tn,k={T∈Tn:Leaf(T)=k}. |
Donaghey [5] (see also [3]) proved bijectively that the Euler number
Theorem 4 (Gelineau-Shin-Zeng). There is an explicit bijection
Leaf(ψ(σ))=First(σ) |
for all
Let
Hetyei [12,Definition 4] defined recursively André permutation of second kind if it is empty or satisfies the following:
(ⅰ)
(ⅱ)
(ⅲ) For all
It is known that the above definition for André permutation of second kind is simply equivalent to the following definition. Let
Definition 5. A permutation
For example, the permutation
τ[1]=1,τ[2]=12,τ[3]=312,τ[4]=3124,τ[5]=31245. |
Foata and Schützenberger [10] proved that the Euler number
A4={1234,1423,3124,3412,4123}. |
Remark. Foata and Schützenberger in [10] introduced augmented André permutation is a permutation
σj−1=max{σj−1,σj,σk−1,σk}andσk=min{σj−1,σj,σk−1,σk}, |
there exists
Definition 6. A permutation
By definition, an André permutations is always a Simsun permutation, but the reverse is not true. For example, the permutation
τ[1]=1,τ[2]=21,τ[3]=213,τ[4]=2134,τ[5]=25134. |
Let
RS3={123,132,213,231,312}. |
As for
An,k:={σ∈An:σn=k},RSn,k:={σ∈RSn:σn=k}. |
Some examples are shown in Table 3.
Foata and Han [8,Theorem 1 (ⅲ)] proved that
Theorem 7. For positive integer
Leaf(T)=Last(ω(T)) | (4) |
for all
Whereas one can easily show that the cardinality
Stanley [22,Conjecture 3.1] conjectured a refinement of Purtill's result [18,Theorem 6.1] about the
Theorem 8 (Hetyei).
For all
#An,k=#RSn−1,k−1. | (5) |
In the next theorem, we give a bijective proof of the conjecture of Stanley by constructing an explicit bijection.
Theorem 9. For positive integer
Last(σ)−1=Last(φ(σ)) | (6) |
for all
Given a permutation
σ[1]=ˉ4,σ[2]=ˉ4ˉ1,σ[3]=2ˉ4ˉ1,σ[4]=2ˉ4ˉ13,σ[5]=2ˉ4ˉ135. |
Some examples of
Definition 10. A type
For example, all type
Our second aim is to show that these two refinements are new Arnold families. Recall that the sequence
Sn,k:={σ∈DUn(B):σ1=k}. |
Theorem 11. For all
ψB:Sn,k→T(B)n,k, | (7) |
ωB:T(B)n,k→A(B)n,k. | (8) |
Thus, for all
Sn,k=#A(B)n,k=#T(B)n,k. | (9) |
In particular, the two sequences
Hetyei[12,Definition 8] defined another class of signed André permutations.
Definition 12 (Hetyei). A signed André permutation is a pair
We write
Conjecture 13. For all
Sn,k=#A(H)n+1,n+2−k. |
Since the last entry of any permutation in the family
Definition 14. A permutation
Let
Theorem 15. For positive integer
Last(σ)−1=Last(φ(B)(σ)) | (10) |
for all
Remark. Ehrenborg and Readdy [6,Section 7] gave a different definition of signed Simsun permutation as follows: A signed permutation
12,21,ˉ12,2ˉ1,1ˉ2,ˉ21,ˉ1ˉ2,ˉ2ˉ1 |
are Simsun permutations, we note that it is not an Arnold family.
First of all, we prove Theorem 7, in order to show that
Given
For example, if the tree
12,21,ˉ12,2ˉ1,1ˉ2,ˉ21,ˉ1ˉ2,ˉ2ˉ1 |
then
Given
πi={σi−1ifi∉{i1,…,iℓ},σik−1ifi=ik−1fork=2,…,ℓ. | (11) |
We show that
σa=πa+1,σa+1=πa+1+1,…,σc−1=πc−1+1, and σc≤πc+1. |
Hence a triple
Consider the running example
Remark. Considering the bijection
ψ(τ)=T∈T9,7,ω(T)=σ=684512937∈A9,7,φ(σ)=π=57341286∈RS8,6, |
where
One can extend the above mapping
Remark. This bijection preserves the
ababaaba=cddcd. |
For the cd-index of a Simsun permutation
aababaab=cddcd. |
Given a
ψB(σ)=π−1(ψ(πσ)) |
through the unique order-preserving map
For example, in the case of
π=(ˉ8ˉ4ˉ3ˉ125679123456789). |
So we have
π=(ˉ8ˉ4ˉ3ˉ125679123456789). |
In Subsection 3.1, we define the bijection
ωB(T)=π−1(ω(π(T))) |
through the unique order-preserving map
For example, in the case of
ωB(T)=π−1(ω(π(T))) |
we obtain
We summarize four interpretations for Entringer numbers
![]() |
||||
![]() |
||||
![]() |
||||
![]() |
||||
![]() |
![]() |
|||||
![]() |
|||||
![]() |
|||||
![]() |
|||||
![]() |
|||||
![]() |
|||||
![]() |
|||||
![]() |
|||||
![]() |
|||||
![]() |
|||||
![]() |
In 2012, Chuang et al. [4] construct a bijection
Algorithm A.
(A1) If
(A2) Otherwise, the word
ρ(T)=ω⋅ρ(T′), |
where the subword
(a) If the root of
(b) If the root of
As deleted only
Remark. Originally, in [4], the increasing 1-2 trees on
Theorem 16. The bijection
Proof. Suppose that we let
The root of
To record the left-child
It is clear that all vertices in the minimal path in a tree become the right-to-left minimums in a permutation under
The bijection
Given an increasing 1-2 tree
Algorithm B. Gelineau et al. described the bijection
|DUn|=|Tn|=1, |
we can define trivially
(B1) If
π′j={πj+2,if πj+2<k−1,πj+2−2,if πj+2>k. |
We get
\pi'_{j} = \begin{cases} \pi_{j+2}, &\text{if $\pi_{j+2} < k-1$,}\\ \pi_{j+2}-2, &\text{if $\pi_{j+2} > k$.} \end{cases} |
We get the tree
(B2) If
(a) If
\pi'_{j} = \begin{cases} \pi_{j+2}, &\text{if $\pi_{j+2} < k-1$,}\\ \pi_{j+2}-2, &\text{if $\pi_{j+2} > k$.} \end{cases} |
(b) If
\pi'_{j} = \begin{cases} \pi_{j+2}, &\text{if $\pi_{j+2} < k-1$,}\\ \pi_{j+2}-2, &\text{if $\pi_{j+2} > k$.} \end{cases} |
Algorithm C. We define another bijection
If
For
(C1)
\begin{align*} v_1 < u_1 < v_2 < u_2 < \dots < v_{j-1} < u_{j-1} < v_j \end{align*} |
Decomposing by the maximal path from
● Graft
● Flip the tree at vertex
● Transplant the trees
● Graft
We can illustrate the above transformation by
\begin{align*} v_1 < u_1 < v_2 < u_2 < \dots < v_{j-1} < u_{j-1} < v_j \end{align*} |
(C2) If
● Graft
● Transplant the trees
● Graft
We can illustrate this transformation by the following
\begin{align*} v_1 < u_1 < v_2 < u_2 < \dots < v_{j-1} < u_{j-1} < v_j \end{align*} |
We note that the vertex
Example. We run the new algorithm to the examples
\begin{align*} d_5(\sigma) & = (3),& d_4(\sigma) & = (6,2),& d_3(\sigma) & = (9,1),& d_2(\sigma) & = (8,5),& d_1(\sigma) & = (7,4). \end{align*} |
By Algorithm C, we get five trees sequentially
\begin{align*} d_5(\sigma) & = (3),& d_4(\sigma) & = (6,2),& d_3(\sigma) & = (9,1),& d_2(\sigma) & = (8,5),& d_1(\sigma) & = (7,4). \end{align*} |
with
\begin{align*} a^{(4)}& = 3, & a^{(3)}& = 2, & a^{(2)}& = 9, & a^{(1)}& = 5,\\ b^{(4)}& = 3, & b^{(3)}& = 2, & b^{(2)}&\text{ does not exist}, & b^{(1)}& = 5. \end{align*} |
Thus, the increasing 1-2 tree
Theorem 17. The two bijections
Proof. It is clear that (C2) is equivalent to (B1). Since the rule (B2a) just exchange two labels, but does not change the tree-structure, it is enough to show that (C1) is produced recursively from (B1) and (B2b).
Assume that
\begin{align*} a^{(4)}& = 3, & a^{(3)}& = 2, & a^{(2)}& = 9, & a^{(1)}& = 5,\\ b^{(4)}& = 3, & b^{(3)}& = 2, & b^{(2)}&\text{ does not exist}, & b^{(1)}& = 5. \end{align*} |
Due to
\begin{align*} a^{(4)}& = 3, & a^{(3)}& = 2, & a^{(2)}& = 9, & a^{(1)}& = 5,\\ b^{(4)}& = 3, & b^{(3)}& = 2, & b^{(2)}&\text{ does not exist}, & b^{(1)}& = 5. \end{align*} |
Since
\begin{align*} a^{(4)}& = 3, & a^{(3)}& = 2, & a^{(2)}& = 9, & a^{(1)}& = 5,\\ b^{(4)}& = 3, & b^{(3)}& = 2, & b^{(2)}&\text{ does not exist}, & b^{(1)}& = 5. \end{align*} |
Since (C2a) is produced from the rule (B1) and (B2b), then Algorithm C follows Algorithm B.
The first author's work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIP) (No. 2017R1C1B2008269).
[1] | H. H. Bauschke, P. L. Combettes, Convex analysis and monotone operator theory in Hilbert spaces, Springer, New York, 2011. https://doi.org/10.1007/978-3-319-48311-5 |
[2] | E. Blum, W. Oettli, From optimization and variational inequalities to equilibrium problems, Math. Student., 63 (1994), 123–145. |
[3] |
M. R. Bozkurt, N. Yurtay, Z. Yilmaz, C. Setkaya, Comparison of different methodologies for determining diabetes, Turk. J. Electr. Eng. Co., 22 (2014), 1044–1055. https://doi.org/10.3906/elk-1209-82 doi: 10.3906/elk-1209-82
![]() |
[4] |
S. Brahim-Belhouari, A. Bermak, Gaussian process for nonstationary time series prediction, Comput. Stat. Data Anal., 47 (2014), 705–712. https://doi.org/10.1016/j.csda.2004.02.006 doi: 10.1016/j.csda.2004.02.006
![]() |
[5] |
S. P. Chatrati, G. Hossain, A. Goyal, A. Bhan, S. Bhattacharya, D. Gaurav, et al., Smart home health monitoring system for predicting type 2 diabetes and hypertension, J. King Saud Univ.-Com., 34 (2020), 862–870. https://doi.org/10.1016/j.jksuci.2020.01.010 doi: 10.1016/j.jksuci.2020.01.010
![]() |
[6] |
D. K. Choubey, M. Kumar, V. Shukla, S. Tripathi, V. K. Dhandhania, Comparative analysis of classification methods with PCA and LDA for diabetes, Curr. Diabetes Rev., 16 (2020), 833–850. https://doi.org/10.2174/1573399816666200123124008 doi: 10.2174/1573399816666200123124008
![]() |
[7] |
D. Deng, N. Kasabov, On-line pattern analysis by evolving self-organizing maps, Neurocomputing, 51 (2003), 87–103. https://doi.org/10.1016/S0925-2312(02)00599-4 doi: 10.1016/S0925-2312(02)00599-4
![]() |
[8] |
D. V. Hieu, Halpern subgradient extragradient method extended to equilibrium problems, RACSAM Rev. R. Acad. A, 111 (2017), 823–840. https://doi.org/10.1007/s13398-016-0328-9 doi: 10.1007/s13398-016-0328-9
![]() |
[9] |
G. B. Huang, Q. Y. Zhu, C. K. Siew, Extreme learning machine: Theory and applications, Neurocomputing, 70 (2006), 489–501. https://doi.org/10.1016/j.neucom.2005.12.126 doi: 10.1016/j.neucom.2005.12.126
![]() |
[10] | G. M. Korpelevich, The extragradient method for finding saddle points and other problems, Matecon, 12 (1976), 747–756. Available from: https://cs.uwaterloo.ca/y328yu/classics/extragrad.pdf. |
[11] |
R. Kraikaew, S. Saejung, Strong convergence of the Halpern subgradient extragradient method for solving variational inequalities in Hilbert spaces, J. Optim. Theory Appl., 163 (2014), 399–412. https://doi.org/10.1007/s10957-013-0494-2 doi: 10.1007/s10957-013-0494-2
![]() |
[12] | V. A. Kumari, R. Chitra, Classification of diabetes disease using support vector machine, Int. J. Eng. Res. Appl., 3 (2013), 1797–1801. |
[13] | L. Li, Diagnosis of diabetes using a weight-adjusted voting approach, IEEE Int. Conf. Bioinform. Bioeng., 2014,320–324. https://doi.org/10.1109/BIBE.2014.27 |
[14] |
K. Muangchoo, A new strongly convergent algorithm to solve pseudomonotone equilibrium problems in a real Hilbert space, J. Math. Comput. Sci., 24 (2022), 308–322. http://dx.doi.org/10.22436/jmcs.024.04.03 doi: 10.22436/jmcs.024.04.03
![]() |
[15] |
L. D. Muu, W. Oettli, Convergence of an adaptive penalty scheme for finding constrained equilibria, Nonlinear Anal.-Theor., 18 (1992), 1159–1166. http://dx.doi.org/10.1016/0041-5553(86)90159-X doi: 10.1016/0041-5553(86)90159-X
![]() |
[16] |
B. T. Polyak, Some methods of speeding up the convergence of iteration methods, USSR Comput. Math. Math. Phys., 4 (1964), 1–17. https://doi.org/10.1016/0041-5553(64)90137-5 doi: 10.1016/0041-5553(64)90137-5
![]() |
[17] | J. R. Quinlan, C4.5: Programs for machine learning, Elsevier, 2014. |
[18] | R. T. Rockafellar, Convex analysis, Princeton University Press, 1970. |
[19] |
Y. Shehu, O. S. Iyiola, Weak convergence for variational inequalities with inertial-type method, Appl. Anal., 101 (2022), 192–216. https://doi.org/10.1080/00036811.2020.1736287 doi: 10.1080/00036811.2020.1736287
![]() |
[20] | S. Sahan, K. Polat, H. Kodaz, S. Gunes, The medical applications of attribute weighted artificial immune system (AWAIS): Diagnosis of heart and diabetes diseas, International Conference on Artificial Immune Systems, Springer, 3627 (2005), 456–468. https://doi.org/10.1007/11536444_35 |
[21] | R. Saxena, S. K. Sharma, M. Gupta, G. C. Sampada, A novel approach for feature selection and classification of diabetes mellitus: Machine learning methods, Comput. Intell. Neurosci., 2022 (2022). https://doi.org/10.1155/2022/3820360 |
[22] | Y. Shehu, C. Izuchukwu, J. C. Yao, X. Qin, Strongly convergent inertial extragradient type methods for equilibrium problems, Appl. Anal., 2021, 1–29. https://doi.org/10.1080/00036811.2021.2021187 |
[23] | World Health Organization, Global action plan for the prevention and control of NCDs 2013–2020, World Health Organization, 2013. Available from: https://apps.who.int/iris/bitstream/handle/10665/94384/9789241506236%20_eng.pdf?sequence=1. |
[24] | J. W. Smith, J. E. Everhart, W. C. Dickson, W. C. Knowler, R. S. Johannes, Using the Adap learning algorithm to forecast the onset of diabetes mellitus, Proc. Annu. Symp. Comput. Appl. Med. Care, 9 (1988), 261–265. Available from: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2245318/. |
[25] |
D. Q. Tran, M. L. Dung, V. H. Hguyen, Extragradient algorithms extended to equilibrium problems, Optimization, 57 (2008), 749–776. https://doi.org/10.1080/02331930601122876 doi: 10.1080/02331930601122876
![]() |
[26] |
R. Tibshirani, Regression shrinkage and selection via the lasso, J. Roy. Stat. Soc. B, 58 (1996), 267–288. https://doi.org/10.1111/j.2517-6161.1996.tb02080.x doi: 10.1111/j.2517-6161.1996.tb02080.x
![]() |
[27] | T. Thomas, N. Pradhan, V. S. Dhaka, Comparative analysis to predict breast cancer using machine learning algorithms: A survey, IEEE Int. Conf. Invent. Comput. Technol., 2020,192–196. https://doi.org/10.1109ICICT48043.2020.9112464 |
[28] |
H. K. Xu, Iterative algorithms for nonlinear operators, J. London Math. Soc., 66 (2002), 240–256. https://doi.org/10.1112/S0024610702003332 doi: 10.1112/S0024610702003332
![]() |
[29] | M. O. Osilike, S. C. Aniagbosor, B. G. Akuchu, Fixed points of asymptotically demicontractive mappings in arbitrary Banach spaces, Panamerican Math. J., 12 (2002), 77–88. |
1. | Shishuo Fu, Jiaxi Lu, Yuanzhe Ding, A skeleton model to enumerate standard puzzle sequences, 2021, 30, 2688-1594, 179, 10.3934/era.2022010 | |
2. | Kanasottu Anil Naik, Rayappa David Amar Raj, Chepuri Venkateswara Rao, Thanikanti Sudhakar Babu, Generalized cryptographic image processing approaches using integer-series transformation for solar power optimization under partial shading, 2022, 272, 01968904, 116376, 10.1016/j.enconman.2022.116376 | |
3. | Sen-Peng Eu, Tung-Shan Fu, Springer Numbers and Arnold Families Revisited, 2024, 10, 2199-6792, 125, 10.1007/s40598-023-00230-9 |
1 | 2 | 3 | 4 | 5 | 6 | 7 | ||
1 | 1 | 1 | ||||||
2 | 0 | 1 | 1 | |||||
3 | 0 | 1 | 1 | 2 | ||||
4 | 0 | 1 | 2 | 2 | 5 | |||
5 | 0 | 2 | 4 | 5 | 5 | 16 | ||
6 | 0 | 5 | 10 | 14 | 16 | 16 | 61 | |
7 | 0 | 16 | 32 | 46 | 56 | 61 | 61 | 271 |
-6 | -5 | -4 | -3 | -2 | -1 | 1 | 2 | 3 | 4 | 5 | 6 | ||
1 | 1 | 1 | |||||||||||
2 | 0 | 1 | 1 | 2 | |||||||||
3 | 0 | 2 | 3 | 3 | 4 | 4 | |||||||
4 | 0 | 4 | 8 | 11 | 11 | 14 | 16 | 16 | |||||
5 | 0 | 16 | 32 | 46 | 57 | 57 | 68 | 76 | 80 | 80 | |||
6 | 0 | 80 | 160 | 236 | 304 | 361 | 361 | 418 | 464 | 496 | 512 | 512 |
![]() |
||||
![]() |
||||
![]() |
||||
![]() |
||||
![]() |
![]() |
|||||
![]() |
|||||
![]() |
|||||
![]() |
|||||
![]() |
|||||
![]() |
|||||
![]() |
|||||
![]() |
|||||
![]() |
|||||
![]() |
|||||
![]() |
1 | 2 | 3 | 4 | 5 | 6 | 7 | ||
1 | 1 | 1 | ||||||
2 | 0 | 1 | 1 | |||||
3 | 0 | 1 | 1 | 2 | ||||
4 | 0 | 1 | 2 | 2 | 5 | |||
5 | 0 | 2 | 4 | 5 | 5 | 16 | ||
6 | 0 | 5 | 10 | 14 | 16 | 16 | 61 | |
7 | 0 | 16 | 32 | 46 | 56 | 61 | 61 | 271 |
-6 | -5 | -4 | -3 | -2 | -1 | 1 | 2 | 3 | 4 | 5 | 6 | ||
1 | 1 | 1 | |||||||||||
2 | 0 | 1 | 1 | 2 | |||||||||
3 | 0 | 2 | 3 | 3 | 4 | 4 | |||||||
4 | 0 | 4 | 8 | 11 | 11 | 14 | 16 | 16 | |||||
5 | 0 | 16 | 32 | 46 | 57 | 57 | 68 | 76 | 80 | 80 | |||
6 | 0 | 80 | 160 | 236 | 304 | 361 | 361 | 418 | 464 | 496 | 512 | 512 |
![]() |
||||
![]() |
||||
![]() |
||||
![]() |
||||
![]() |
![]() |
|||||
![]() |
|||||
![]() |
|||||
![]() |
|||||
![]() |
|||||
![]() |
|||||
![]() |
|||||
![]() |
|||||
![]() |
|||||
![]() |
|||||
![]() |