In clustering, the cost function that is commonly used involves calculating all-pairwise squared distances. In this paper, we formulate the cost function using mean squared error and show that this leads to more balanced clustering compared to centroid-based distance functions, like the sum of squared distances in k-means. The clustering method has been formulated as a cut-based approach, more intuitively called Squared cut (Scut). We introduce an algorithm for the problem which is faster than the existing one based on the Stirling approximation. Our algorithm is a sequential variant of a local search algorithm. We show by experiments that the proposed approach provides better overall optimization of both mean squared error and cluster balance compared to existing methods.
Citation: Mikko I. Malinen, Pasi Fränti. All-pairwise squared distances lead to more balanced clustering[J]. Applied Computing and Intelligence, 2023, 3(1): 93-115. doi: 10.3934/aci.2023006
[1] | Jamie L. Flexon, Lisa Stolzenberg, Stewart J. D'Alessio . The impact of cannabis legislation on benzodiazepine and opioid use and misuse. AIMS Medical Science, 2024, 11(1): 1-24. doi: 10.3934/medsci.2024001 |
[2] | Hicham Rahmi, Ben Yamine Mallouki, Fatiha Chigr, Mohamed Najimi . The effects of smoking Haschich on blood parameters in young people from the Beni Mellal region Morocco. AIMS Medical Science, 2021, 8(4): 276-290. doi: 10.3934/medsci.2021023 |
[3] | Gili Eshel, Baruch Harash, Maayan Ben Sasson, Amir Minerbi, Simon Vulfsons . Validation of the Hebrew version of the questionnaire “know pain 50”. AIMS Medical Science, 2022, 9(1): 51-64. doi: 10.3934/medsci.2022006 |
[4] | Carlos Forner-Álvarez, Ferran Cuenca-Martínez, Rafael Moreno-Gómez-Toledano, Celia Vidal-Quevedo, Mónica Grande-Alonso . Multimodal physiotherapy treatment based on a biobehavioral approach in a patient with chronic low back pain: A case report. AIMS Medical Science, 2024, 11(2): 77-89. doi: 10.3934/medsci.2024007 |
[5] | Carlos Forner-Álvarez, Ferran Cuenca-Martínez, Alba Sebastián-Martín, Celia Vidal-Quevedo, Mónica Grande-Alonso . Combined face-to-face and telerehabilitation physiotherapy management in a patient with chronic pain related to piriformis syndrome: A case report. AIMS Medical Science, 2024, 11(2): 113-123. doi: 10.3934/medsci.2024010 |
[6] | Diogo Henrique Constantino Coledam, Philippe Fanelli Ferraiol, Gustavo Aires de Arruda, Arli Ramos de Oliveira . Correlates of the use of health services among elementary school teachers: A cross-sectional exploratory study. AIMS Medical Science, 2023, 10(4): 273-290. doi: 10.3934/medsci.2023021 |
[7] | Benjamin P Jones, Srdjan Saso, Timothy Bracewell-Milnes, Jen Barcroft, Jane Borley, Teodor Goroszeniuk, Kostas Lathouras, Joseph Yazbek, J Richard Smith . Laparoscopic uterosacral nerve block: A fertility preserving option in chronic pelvic pain. AIMS Medical Science, 2019, 6(4): 260-267. doi: 10.3934/medsci.2019.4.260 |
[8] | Kaye Ervin, Julie Pallant, Daniel R. Terry, Lisa Bourke, David Pierce, Kristen Glenister . A Descriptive Study of Health, Lifestyle and Sociodemographic Characteristics and their Relationship to Known Dementia Risk Factors in Rural Victorian Communities. AIMS Medical Science, 2015, 2(3): 246-260. doi: 10.3934/medsci.2015.3.246 |
[9] | Joann E. Bolton, Elke Lacayo, Svetlana Kurklinsky, Christopher D. Sletten . Improvement in montreal cognitive assessment score following three-week pain rehabilitation program. AIMS Medical Science, 2019, 6(3): 201-209. doi: 10.3934/medsci.2019.3.201 |
[10] | Mansour Shakiba, Mohammad Hashemi, Zahra Rahbari, Salah Mahdar, Hiva Danesh, Fatemeh Bizhani, Gholamreza Bahari . Lack of Association between Human µ-Opioid Receptor (OPRM1) Gene Polymorphisms and Heroin Addiction in A Sample of Southeast Iranian Population. AIMS Medical Science, 2017, 4(2): 233-240. doi: 10.3934/medsci.2017.2.233 |
In clustering, the cost function that is commonly used involves calculating all-pairwise squared distances. In this paper, we formulate the cost function using mean squared error and show that this leads to more balanced clustering compared to centroid-based distance functions, like the sum of squared distances in k-means. The clustering method has been formulated as a cut-based approach, more intuitively called Squared cut (Scut). We introduce an algorithm for the problem which is faster than the existing one based on the Stirling approximation. Our algorithm is a sequential variant of a local search algorithm. We show by experiments that the proposed approach provides better overall optimization of both mean squared error and cluster balance compared to existing methods.
In 2005, Rodríguez [1] used the Lyapunov-Schmidt method and Brower fixed-point theorem to discuss the following discrete Sturm-Liouville boundary value problem
{Δ[p(t−1)Δy(t−1)]+q(t)y(t)+λy(t)=f(y(t)), t∈[a+1,b+1]Z,a11y(a)+a12Δy(a)=0, a21y(b+1)+a22Δy(b+1)=0, |
where λ is the eigenvalue of the corresponding linear problem and the nonlinearity f is bounded.
Furthermore, in 2007, Ma [2] studied the following discrete boundary value problem
{Δ[p(t−1)Δy(t−1)]+q(t)y(t)+λy(t)=f(t,y(t))+h(t), t∈[a+1,b+1]Z,a11y(a)+a12Δy(a)=0, a21y(b+1)+a22Δy(b+1)=0, |
where f is subject to the sublinear growth condition
|f(t,s)|≤A|s|α+B,s∈R |
for some 0≤α<1 and A,B∈(0,∞). Additional results to the existence of solutions to the related continuous and discrete problems on the nonresonance and the resonance can be found in [3,4,5,6,7,8,9,10,11,12,13] and the references therein. For example, Li and Shu [14] considered the existence of solutions to the continuous Sturm-Liouville problem with random impulses and boundary value problems using the Dhage's fixed-point theorem and considered the existence of upper and lower solutions to a second-order random impulsive differential equation in [15] using the monotonic iterative method.
Inspired by the above literature, we use the solution set connectivity theory of compact vector field [16] to consider the existence of solutions to discrete resonance problems
{−Δ[p(t−1)Δy(t−1)]+q(t)y(t)=λkr(t)y(t)+f(t,y(t))+γψk(t)+¯g(t), t∈[1,T]Z,(a0λk+b0)y(0)=(c0λk+d0)Δy(0),(a1λk+b1)y(T+1)=(c1λk+d1)∇y(T+1), | (1.1) |
where p:[0,T]Z→(0,∞), q:[1,T]Z→R, ¯g:[1,T]Z→R, r(t)>0, t∈[1,T]Z, (λk,ψk) is the eigenpair of the corresponding linear problem
{−Δ[p(t−1)Δy(t−1)]+q(t)y(t)=λr(t)y(t), t∈[1,T]Z,(a0λ+b0)y(0)=(c0λ+d0)Δy(0),(a1λ+b1)y(T+1)=(c1λ+d1)∇y(T+1). | (1.2) |
It is worth noting that the difference between the problem (1.1) and the above questions is the eigenvalue that not only appears in the equation but also in the boundary conditions, which causes us considerable difficulties. Furthermore, it should be noted that these problems also apply to a number of physical problems, including those involving heat conduction, vibrating strings, and so on. For instance, Fulton and Pruess [17] discussed a kind of heat conduction problem, which has the eigenparameter-dependent boundary conditions. However, to discuss this kind of problem, we should know the spectrum of the problem (1.2). Fortunately, in 2016, Gao and Ma [18] obtained the eigenvalue theory of problem (1.2) under the conditions listed as follows:
(A1) δ0:=a0d0−b0c0<0,c0≠0, d1−b1≠0,
(A2) δ1:=a1d1−b1c1>0,c1≠0, b0+d0≠0,
which laid a theoretical foundation for this paper.
Under the conditions (A1) and (A2), we assume the following conditions hold:
(H1) (Sublinear growth condition) f:[1,T]Z×R→R is continuous and there exist α∈[0,1) and A,B∈(0,∞), such that
|f(t,y)|≤A|y|α+B, |
(H2) (Symbol condition) There exists ω>0, such that
yf(t,y)>0,t∈[1,T]Zfor|y|>ω, | (1.3) |
or
yf(t,y)<0,t∈[1,T]Zfor|y|>ω, | (1.4) |
(H3) ¯g:[1,T]Z→R satisfies
T∑s=1¯g(s)ψk(s)=0, | (1.5) |
(H4) f:[1,T]Z×R→R is continuous and
lim|y|→∞f(t,y)=0 |
uniformly for t∈[1,T]Z.
The organization of this paper is as follows. In the second section, we construct a completely new inner product space. In the new inner product space, we discuss the basic self-adjointness of the corresponding linear operator and the properties of the eigenpair of (1.2). Finally, under the above properties, the Lyapunov-Schmidit method is used to decompose the inner product space and transform our problem to an equivalent system, that is to say, finding the solutions of (1.1) is equivalent to finding the solutions of this system. Under the sublinear condition and sign conditions on nonlinear terms, an existence result of solutions to the problem (1.1) is obtained using Schauder's fixed-point theorem and the connectivity theories of the solution set of compact vector fields. Based on the first result, the existence of two solutions to the problem (1.1) is also obtained in this section.
Definition 2.1. ([19]) A linear operator P from the linear space X to itself is called the projection operator, if P2=P.
Lemma 2.2. ([16]) Let C be a bounded closed convex set in Banach space E, T:[α,β]×C→C(α<β) be a continuous compact mapping, then the set
Sα,β={(ρ,x)∈[α,β]×C|T(ρ,x)=x} |
contains a connected branch connecting {α}×C and {β}×C.
Lemma 2.3. ([20])(Schauder) Let D be a bounded convex closed set in E, A:D→D is completely continuous, then A has a fixed point in D.
First, we construct the inner product space needed in this paper.
Let
Y:={u|u:[1,T]Z→R}, |
then Y is a Hilbert space under the following inner product
⟨y,z⟩Y=T∑t=1y(t)z(t) |
and its norm is ‖y‖Y:=√⟨y,y⟩Y.
Furthermore, consider the space H:=Y⊕R2. Define the inner product as follows:
⟨[y,α,β]⊤,[z,ζ,ρ]⊤⟩=⟨y,z⟩Y+p(0)|δ0|αζ+p(T)|δ1|βρ, |
which norm is defined as
‖y∗‖=⟨[y,α,β]⊤,[y,α,β]⊤⟩12, |
where ⊤ is transposition to a matrix.
Let
y0,0=b0y(0)−d0Δy(0), y0,1=a0y(0)−c0Δy(0) |
and
yT+1,0=b1y(T+1)−d1∇y(T+1), yT+1,1=a1y(T+1)−c1∇y(T+1). |
For y∗=[y,α,β]⊤, define an operator L:D→H as follows:
Ly∗=[−Δ[p(t−1)Δy(t−1)]+q(t)y(t)−y0,0−yT+1,0]:=[Ly−y0,0−yT+1,0], |
where D={[y,α,β]⊤:y∈Y, y0,1=α, yT+1,1=β}. Define S:D→H as follows:
Sy∗=S[yαβ]=[ryαβ]. |
Then, the problem (1.2) is equivalent to the eigenvalue problem as follows:
Ly∗=λSy∗, | (2.1) |
that is, if (λk,y) is the eigenpair of the problem (1.2), then (λk,y∗) is the eigenpair of the opertor L. Conversely, if (λk,y∗) is the eigenpair of the operator L, then (λk,y) is the eigenpair of the problem (1.2).
Eventually, we define A:D→H as follows:
Ay∗=F(t,y∗)+[γψk+¯g,0,0]⊤, |
where F(t,y∗)=F(t,[y,α,β]⊤)=[f(t,y),0,0]⊤. Obviously, the solution of the problem (1.1) is equivalent to the fixed point of the following operator
Ly∗=λkSy∗+Ay∗. | (2.2) |
It can be seen that there is a homomorphism mapping (λk,y)↔(λk,y∗) between the problem (1.1) and the operator Eq (2.2).
Next, we are committed to obtaining the orthogonality of the eigenfunction.
Lemma 2.4. Assume that (λ,y∗) and (μ,z∗) are eigenpairs of L, then
⟨y∗,Lz∗⟩−⟨Ly∗,z∗⟩=(μ−λ)⟨y∗,Sz∗⟩. |
Proof Let y∗=[y,α,β]⊤∈D, z∗=[z,ζ,ρ]⊤∈D, then
⟨y∗,Lz∗⟩=⟨[y,α,β]⊤,[Lz,−z0,0,−zT+1,0]⊤⟩=⟨y,Lz⟩Y+p(0)|δ0|α(−z0,0)+p(T)|δ1|β(−zT+1,0)=μ⟨y,rz⟩Y+p(0)|δ0|α(μζ)+p(T)|δ1|β(μρ)=μ⟨y∗,Sz∗⟩. | (2.3) |
Similarly, we have
⟨Ly∗,z∗⟩=⟨[Ly,−y0,0,−yT+1,0]⊤,[z,ζ,ρ]⊤⟩=⟨Ly,z⟩Y+p(0)|δ0|(−y0,0)ζ+p(T)|δ1|(−yT+1,0)ρ=λ⟨ry,z⟩Y+p(0)|δ0|λαζ+p(T)|δ1|λβρ=λ⟨y∗,Sz∗⟩. | (2.4) |
It can be seen from (2.3) and (2.4)
⟨y∗,Lz∗⟩−⟨Ly∗,z∗⟩=(μ−λ)⟨y∗,Sz∗⟩. |
Lemma 2.5. The operator L is the self-adjoint operator in H.
Proof For y∗=[y,α,β]⊤∈D,z∗=[z,ζ,ρ]⊤∈D, we just need to prove that ⟨y∗,Lz∗⟩=⟨Ly∗,z∗⟩. By the definition of inner product in H. we obtain
⟨y∗,Lz∗⟩=⟨y,Lz⟩Y+p(0)|δ0|α(−z0,0)+p(T)|δ1|β(−zT+1,0), |
and
⟨Ly∗,z∗⟩=⟨Ly,z⟩Y+p(0)|δ0|(−y0,0)ζ+p(T)|δ1|(−yT+1,0)ρ. |
Therefore,
⟨y∗,Lz∗⟩−⟨Ly∗,z∗⟩=⟨y,Lz⟩Y−⟨Ly,z⟩Y+p(0)|δ0|[α(−z0,0)−(−y0,0)ζ]+p(T)|δ1|[β(−zT+1,0)−(−yT+1,0)ρ], |
where
⟨y,Lz⟩Y=T∑t=1y(t)(−Δ[p(t−1)Δz(t−1)]+q(t)z(t))=T∑t=1y(t)p(t−1)Δz(t−1)−T∑t=1y(t)p(t)Δz(t)+T∑t=1q(t)y(t)z(t)=T−1∑t=0y(t+1)p(t)Δz(t)−T∑t=1y(t)p(t)Δz(t)+T∑t=1q(t)y(t)z(t)=T−1∑t=0p(t)Δy(t)Δz(t)+p(0)y(0)Δz(0)−p(T)y(T)Δz(T)+T∑t=1q(t)y(t)z(t) |
and
⟨Ly,z⟩Y=T−1∑t=0p(t)Δy(t)Δz(t)+p(0)Δy(0)z(0)−p(T)Δy(T)z(T)+T∑t=1q(t)y(t)z(t). |
Moreover, from
α(−z0,0)−(−y0,0)ζ=[a0y(0)−c0Δy(0)][d0Δz(0)−b0z(0)]−[d0Δy(0)−b0y(0)][a0z(0)−c0Δz(0)]=(a0d0−b0c0)[y(0)Δz(0)−Δy(0)z(0)] |
and
β(−zT+1,0)−(−yT+1,0)ρ=[a1y(T+1)−c1∇y(T+1)][−b1z(T+1)+d1∇z(T+1)]−[−b1y(T+1)+d1∇y(T+1)][a1z(T+1)−c1∇z(T+1)]=(a1d1−b1c1)[y(T+1)∇z(T+1)−∇y(T+1)z(T+1)], |
we have
⟨y∗,Lz∗⟩−⟨Ly∗,z∗⟩=p(0)|y(0)Δy(0)z(0)Δz(0)|−p(T)|y(T)Δy(T)z(T)Δz(T)|−p(0)|y(0)Δy(0)z(0)Δz(0)|+p(T)|y(T+1)∇y(T+1)z(T+1)∇z(T+1)|=0. |
In order to obtain the orthogonality of the eigenfunction, we define a weighted inner product related to the weighted function r(t) in H. First, we define the inner product in Y as ⟨y,z⟩r=T∑t=1r(t)y(t)z(t).
Similarly, the inner product associated with the weight function r(t) in the space H is defined as follows:
⟨[y,α,β]⊤,[z,ζ,ρ]⊤⟩r=⟨y,z⟩r+p(0)|δ0|αζ+p(T)|δ1|βρ. |
Lemma 2.6. (Orthogonality theorem) Assume that (A1) and (A2) hold. If (λ,y∗) and (μ,z∗) are two different eigenpairs corresponding to L, then y∗ and z∗ are orthogonal under the weight inner product related to the weight function r(t).
Proof Assume that (λ,y∗) and (μ,z∗) is the eigenpair of L, then it can be obtained from Lemmas 2.4 and 2.5
0=(μ−λ)⟨y∗,Sz∗⟩=(μ−λ)⟨y∗,z∗⟩r. |
Therefore, if λ≠μ, then ⟨y∗,z∗⟩r=0, which implies that y∗ and z∗ are orthogonal to the inner product defined by the weighted function r(t).
Lemma 2.7. ([18]) Suppose that (A1) and (A2) hold. Then (1.2) has at least T or at most T+2 simple eigenvalues.
In this paper, we consider that λk is a simple eigenvalue, that is, the eigenspace corresponding to each eigenvalue is one-dimensional. Let ψ∗k=[ψk,α,β]⊤∈D be the eigenfunction corresponding to λk, and assume that it satisfies
⟨ψ∗k,ψ∗k⟩=1. | (2.5) |
Denote by L:=L−λkS, then the operator (2.2) is transformed into
Ly∗=Ay∗. | (2.6) |
Define P:D→D by
(Px∗)(t)=ψ∗k(t)⟨ψ∗k(t),x∗(t)⟩. |
Lemma 2.8. P is a projection operator and Im(P)=Ker(L).
Proof Obviously, P is a linear operator, next, we need to prove P2=P.
(P2x∗)(t)=P(Px∗)(t)=ψ∗k(t)⟨ψ∗k(t),Px∗(t)⟩=ψ∗k(t)⟨ψ∗k(t),ψ∗k(t)⟨ψ∗k(t),x∗(t)⟩⟩=ψ∗k(t)⟨ψ∗k(t),x∗(t)⟩⟨ψ∗k(t),ψ∗k(t)⟩=ψ∗k(t)⟨ψ∗k(t),x∗(t)⟩=(Px∗)(t). |
It can be obtained from the Definition 2.1, P is a projection operator. In addition, Im(P)=span{ψ∗k}=Ker(L).
Define H:H→H by
H([yαβ])=[yαβ]−⟨[yαβ],ψ∗k⟩ψ∗k. |
Lemma 2.9. H is a projection operator and Im(H)=Im(L).
Proof Obviously, H is a linear operator, next, we need to prove that H2=H.
H2([yαβ])=H(H[yαβ])=H[yαβ]−⟨H[yαβ],ψ∗k⟩ψ∗k=[yαβ]−⟨[yαβ],ψ∗k⟩ψ∗k−⟨[yαβ]−⟨[yαβ],ψ∗k⟩ψ∗k,ψ∗k⟩ψ∗k=[yαβ]−2⟨[yαβ],ψ∗k⟩ψ∗k+⟨⟨[yαβ],ψ∗k⟩ψ∗k,ψ∗k⟩ψ∗k=[yαβ]−2⟨[yαβ],ψ∗k⟩ψ∗k+⟨[yαβ],ψ∗k⟩⟨ψ∗k,ψ∗k⟩ψ∗k=H([yαβ]). |
It can be obtained from Definition 2.1 that H is a projection operator. On the one hand, for any [y,α,β]⊤∈H, we have
⟨H[yαβ],ψ∗k⟩=⟨[yαβ]−⟨[yαβ],ψ∗k⟩ψ∗k,ψ∗k⟩=⟨[yαβ],ψ∗k⟩−⟨⟨[yαβ],ψ∗k⟩ψ∗k,ψ∗k⟩=0, |
thus, Im(H)⊂Im(L). On the other hand, for any y∗∈Im(L), we have
⟨y∗,ψ∗k⟩=0. |
In summary, Im(H)=Im(L).
Denote that I is a identical operator, then
D=Im(P)⊕Im(I−P),H=Im(H)⊕Im(I−H). |
The restriction of the operator L on L|Im(I−P) is a bijection from Im(I−P) to Im(H). Define M:Im(H)→Im(I−P) by
M:=(L|Im(I−P))−1. |
It can be seen from KerL=span{ψ∗k} that there is a unique decomposition for any y∗=[y,α,β]⊤∈D
y∗=ρψ∗k+x∗, |
where ρ∈R,x∗=[x,α,β]⊤∈Im(I−P).
Lemma 2.10. The operator Eq (2.6) is equivalent to the following system
x∗=MHA(ρψ∗k+x∗), | (2.7) |
T∑t=1ψk(t)f(t,ρψk(t)+x(t))=γ(p(0)|δ0|α2+p(T)|δ1|β2−1):=θ, | (2.8) |
where α=a0ψk(0)−c0Δψk(0),β=a1ψk(T+1)−c1∇ψk(T+1).
Proof (ⅰ) For any y∗=ρψ∗k+x∗, we have
Ly∗=Ay∗ ⟺H(L(ρψ∗k+x∗)−A(ρψ∗k+x∗))=0⟺Lx∗−HA(ρψ∗k+x∗)=0⟺x∗=MHA(ρψ∗k+x∗). |
(ⅱ) Since ⟨Ly∗,ψ∗k⟩=0, we have ⟨Ay∗,ψ∗k⟩=0. Therefore,
⟨f(t,y)+γψk+¯g,ψk⟩Y=T∑t=1f(t,ρψk(t)+x(t))ψk(t)+T∑t=1γψk(t)ψk(t)+T∑t=1¯g(t)ψk(t)=0. |
Combining (H3) with (2.5), we have
T∑t=1ψk(t)f(t,ρψk(t)+x(t))=γ(p(0)|δ0|α2+p(T)|δ1|β2−1)=θ, |
where α=a0ψk(0)−c0Δψk(0),β=a1ψk(T+1)−c1∇ψk(T+1).
Let
A+={t∈{1,2,⋯,T} s.t. ψk(t)>0}, |
A−={t∈{1,2,⋯,T} s.t. ψk(t)<0}. |
Obviously,
A+∪A−≠∅, min{|ψk(t)||t∈A+∪A−}>0. |
Lemma 3.1. Supposed that (H1) holds, then there exist constants M0 and M1, such that
‖x∗‖≤M1(|ρ|‖ψk‖Y)α, |
where (ρ,x∗) is the solution of (2.7) and satisfies |ρ|≥M0.
Proof Since
A(ρψ∗k+x∗)=F(t,ρψ∗k+x∗)+[γψk+¯g,0,0]⊤=[f(t,ρψk+x)+γψk+¯g,0,0]⊤, |
we have
‖x∗‖≤‖M‖Im(H)→Im(I−P)‖H‖H→Im(H)[‖¯g‖Y+γ‖ψk‖Y+A(|ρ|‖ψk‖Y+‖x‖Y)α+B]=‖M‖Im(H)→Im(I−P)‖H‖H→Im(H)[‖¯g‖Y+A(|ρ|‖ψk‖Y)α(1+‖x‖Y|ρ|‖ψk‖Y)α+B−θ]≤‖M‖Im(H)→Im(I−P)‖H‖H→Im(H)[‖¯g‖Y+A(|ρ|‖ψk‖Y)α(1+α‖x‖Y|ρ|‖ψk‖Y)+B−θ]=‖M‖Im(H)→Im(I−P)‖H‖H→Im(H)[‖¯g‖Y+A(|ρ|‖ψk‖Y)α(1+α(|ρ|‖ψk‖Y)1−α‖x‖Y(|ρ|‖ψk‖Y)α)+B−θ]. |
Denote that
D0=‖M‖Im(H)→Im(I−P)‖H‖H→Im(H)(‖¯g‖Y+B−θ),D1=A‖M‖Im(H)→Im(I−P)‖H‖H→Im(H). |
Furthermore, we have
‖x∗‖(|ρ|‖ψk‖Y)α≤D0(|ρ|‖ψk‖Y)α+D1+αD1(|ρ|‖ψk‖Y)1−α‖x‖Y(|ρ|‖ψk‖Y)α≤D0(|ρ|‖ψk‖Y)α+D1+αD1(|ρ|‖ψk‖Y)1−α‖x∗‖(|ρ|‖ψk‖Y)α. |
So, if we let
αD1(|ρ|‖ψk‖Y)1−α≤12, |
we have
|ρ|≥(2αD1)11−α‖ψk‖Y:=M0. |
Thus,
‖x∗‖(|ρ|‖ψk‖Y)α≤2D0(M0‖ψk‖Y)α+2D1:=M1. |
This implies that
‖x∗‖≤M1(|ρ|‖ψk‖Y)α. |
Lemma 3.2. Suppose that (H1) holds, then there exist constants M0 and Γ, such that
‖x∗‖≤Γ(|ρ|min{|ψk(t)||t∈A+∪A−})α, |
where (ρ,x∗) is the solution of (2.7) and satisfies |ρ|≥M0.
According to Lemma 3.2, choose constant ρ0, such that
ρ0>max{M0,Γ(|ρ0|min{|ψk(t)||t∈A+∪A−})α}. | (3.1) |
Let
K:={x∗∈Im(I−P)|x∗=MHA(ρψ∗k+x∗),|ρ|≤ρ0}. |
Then, for sufficiently large ρ≥ρ0, there is
ρψk(t)+x(t)≥ω, ∀t∈A+,x∗∈K, | (3.2) |
ρψk(t)+x(t)≤−ω, ∀t∈A−,x∗∈K, | (3.3) |
and for sufficiently small ρ≤−ρ0, there is
ρψk(t)+x(t)≤−ω, ∀t∈A+,x∗∈K, | (3.4) |
ρψk(t)+x(t)≥ω, ∀t∈A−,x∗∈K. | (3.5) |
Theorem 3.3. Suppose that (A1), (A2) and (H1)–(H3) hold, then there exists a non-empty bounded set Ω¯g⊂R, such that the problem (1.1) has a solution if and only if θ∈Ω¯g. Furthermore, Ω¯g contains θ=0 and has a non-empty interior.
Proof We prove only the case of (1.3) in (H2), and the case of (1.4) can be similarly proved.
From (1.3) and (3.2)–(3.5), it is not difficult to see that
f(t,ρψk(t)+x(t))>0, ∀t∈A+, x∗∈K, |
f(t,ρψk(t)+x(t))<0, ∀t∈A−, x∗∈K, |
for sufficiently large ρ≥ρ0 and for sufficiently small ρ≤−ρ0,
f(t,ρψk(t)+x(t))<0, ∀t∈A+, x∗∈K, |
f(t,ρψk(t)+x(t))>0, ∀t∈A−, x∗∈K. |
Therefore, if ρ≥ρ0 is sufficiently large,
ψk(t)f(t,ρψk(t)+x(t))>0, ∀t∈A+∪A−, x∗∈K, | (3.6) |
if ρ≤−ρ0 is sufficiently small,
ψk(t)f(t,ρψk(t)+x(t))<0, ∀t∈A+∪A−, x∗∈K. | (3.7) |
Let
C:={x∗∈Im(I−P)|‖x∗‖≤ρ0}. |
Define Tρ:Im(I−P)→Im(I−P) by
Tρ:=MHA(ρψ∗k+x∗). |
Obviously, Tρ is completely continuous. By (3.1), for x∗∈C and ρ∈[−ρ0,ρ0],
‖Tρx∗‖≤Γ(|ρ|min{|ψk(t)||t∈A+∪A−})α≤Γ(|ρ0|min{|ψk(t)||t∈A+∪A−})α≤ρ0, |
i.e.,
Tρ(C)⊆C. |
According to Schauder's fixed point theorem, Tρ has a fixed point on C, such that Tρx∗=x∗. It can be seen from Lemma 2.10 that the problem (1.1) is equivalent to the following system
Ψ(s,x∗)=θ, (s,x∗)∈S¯g, |
where
S¯g:={(ρ,x∗)∈R×Im(I−P)|x∗=MHA(ρψ∗k+x∗)}, |
Ψ(ρ,x∗):=T∑s=1ψk(s)f(s,ρψk(s)+x(s)). |
At this time, the Ω¯g in Theorem 3.3 can be given by Ω¯g=Ψ(S¯g). There exists a solution to the problem (1.1) for θ∈Ω¯g.
From (3.6), (3.7) and A+∪A−≠∅, we can deduce that for any x∗∈K
T∑s=1ψk(s)f(s,−ρ0ψk(s)+x(s))<0, T∑s=1ψk(s)f(s,ρ0ψk(s)+x(s))>0. |
Thus,
Ψ(−ρ0,x∗)<0<Ψ(ρ0,x∗), ∀x∗∈K. | (3.8) |
According to Lemma 2.2, S¯g⊂RׯBρ0 contains a connected branch ξ−ρ0,ρ0 connecting {−ρ0}×C and {ρ0}×C. Combined with (3.8), Ω¯g contains θ=0 and has a non-empty interior.
Theorem 3.4. Suppose that (A1), (A2), (H2)–(H4) hold. Ω¯g as shown in Theorem 3.3, then there exists a nonempty set Ω∗¯g⊂Ω¯g∖{0}, such that problem (1.1) has at least two solutions for θ∈Ω∗¯g.
Proof We prove only the case of (1.3), and the case of (1.4) can be similarly proved. Since the condition (H4) implies that (H1), using Theorem 3.3, we know that there exists ρ0>0, such that
Ψ(ρ0,x∗)>0, ∀x∗∈K. |
Let
δ:=min{Ψ(ρ0,x∗)|x∗∈K}, |
then δ>0.
Next, we prove that problem (1.1) has at least two solutions for any θ∈(0,δ).
Let
S¯g:={(ρ,x∗)∈R×Im(I−P)|x∗=MHA(ρψ∗k+x∗)}, |
¯K:={x∗∈Im(I−P)|(ρ,x∗)∈S¯g}. |
By (H4), there exists a constant A0 such that
‖x∗‖≤A0, ∀x∗∈K. |
Similar to the derivation of Theorem 3.3, there exists ρ∗>ρ0 such that the following results hold:
(ⅰ) For ρ≥ρ∗, there is
ψk(t)f(t,ρψk(t)+x(t))>0, ∀t∈A+∪A−, x∗∈¯K, | (3.9) |
(ⅱ) For ρ≤−ρ∗, there is
ψk(t)f(t,ρψk(t)+x(t))<0, ∀t∈A+∪A−, x∗∈¯K. | (3.10) |
Let
C∗:={x∗∈Im(I−P)|‖x∗‖≤A0}. |
According to (H4), (3.9) and (3.10), we have
lim|ρ|→∞T∑s=1ψk(s)f(s,ρψk(s)+x(s))=0 |
uniformly for x∗∈¯K, i.e.
lim|ρ|→∞Ψ(ρ,x∗)=0, x∗∈¯K. |
Therefore, there exists a constant l:l>ρ∗>ρ0>0 such that S¯g contains a connected branch between {−l}×C∗ and {l}×C∗, and
max{|Ψ(ρ,x∗)||ρ=±l, (ρ,x∗)∈ξ−l,l}≤max{|Ψ(ρ,x∗)||(ρ,x∗)∈{−l,l}ׯK}≤θ3. |
It can be seen from the connectivity of ξ−l,l that there exist (ρ1,x∗1) and (ρ2,x∗2) in ξ−l,l(⊂S¯g), such that
Ψ(ρ1,x∗1)=θ, Ψ(ρ2,x∗2)=θ, |
where ρ1∈(−l,ρ0),ρ2∈(ρ0,l). It can be proved that ρ1ψ∗k+x∗1 and ρ2ψ∗k+x∗2 are two different solutions of problem (1.1).
In this section, we give a concrete example of the application of our major results of Theorems 3.3 and 3.4. We choose T=3,a0,d0,b1,c1=0 and a1,d1,b0,c0=1, which implies that the interval becomes [1,3]Z and the conditions (A1),(A2) hold.
First, we consider the eigenpairs of the corresponding linear problem
{−Δ2y(t−1)=λy(t), t∈[1,3]Z,y(0)=λΔy(0), λy(4)=∇y(4). | (4.1) |
Define the equivalent matrix of (4.1) as follows,
Aλ=(λ−2+λ1+λ101λ−2101λ−2+11−λ) |
Consequently, Aλy=0 is equivalent to (4.1). Let |Aλ|=0, we have
λ1=−1.4657,λ2=0.1149,λ3=0.8274,λ4=2.0911,λ5=3.4324, |
which are the eigenvalues of (4.1). Next, we choose λ=λ1=−1.4657, then we obtain the corresponding eigenfunction
ψ1(t)={1,t=1,3.4657,t=2,3.46572−1,t=3. |
Example 4.1. Consider the following problem
{−Δ2y(t−1)=−1.4657y(t)+f(t,y(t))+ψ1(t)+¯g(t), t∈[1,3]Z,y(0)=−1.4657Δy(0), −1.4657y(4)=∇y(4), | (4.2) |
where
f(t,s)={ts3,s∈[−1,1],t5√s,s∈(−∞,−1)∪(1,+∞), |
and
¯g(t)={0,t=1,3.46572−1,t=2,−3.4657,t=3. |
Then, for f(t,y(t)), we have |f(t,y(t))|≤3|y(t)|13. If we choose ω=1, yf(t,y)>0 for |y(t)|>1. For ¯g(t), we have 3∑s=1¯g(s)ψ1(s)=0.
Therefore, the problem (4.2) satisfies the conditions (A1),(A2), (H1)–(H3), which implies that the problem (4.2) has at least one solution by Theorem 3.3.
Example 4.2. Consider the following problem
{−Δ2y(t−1)=−1.4657y(t)+f(t,y(t))+ψ1(t)+¯g(t), t∈[1,3]Z,y(0)=−1.4657Δy(0), −1.4657y(4)=∇y(4), | (4.3) |
where
f(t,s)=tse|s|, t∈[1,3]Z |
and
¯g(t)={0,t=1,1−3.46572,t=2,3.4657,t=3. |
Then, for f(t,y(t)), we always have yf(t,y)>0 for all y(t)>0 or y(t)<0, f is continuous and satisfies
lim|y|→∞f(t,y)=0. |
For ¯g(t), we have 3∑s=1¯g(s)ψ1(s)=0.
Therefore, the problem (4.3) satisfies the conditions (A1),(A2), (H2)–(H4), which implies that the problem (4.3) has at least two solutions by Theorem 3.4.
The authors declare that they have not used Artificial Intelligence (AI) tools in the creation of this article.
Supported by National Natural Science Foundation of China [Grant No. 11961060] and Natural Science Foundation of Qinghai Province(No.2024-ZJ-931).
The authors declare that there are no conflicts of interest.
[1] |
J. H. Ward Jr, Hierarchical grouping to optimize an objective function, J. Am. Stat. Assoc., 58 (1963), 236–244. https://doi.org/10.1080/01621459.1963.10500845 doi: 10.1080/01621459.1963.10500845
![]() |
[2] |
T. Kohonen, Median strings, Pattern Recogn. Lett., 3 (1985), 309–313. https://doi.org/10.1016/0167-8655(85)90061-3 doi: 10.1016/0167-8655(85)90061-3
![]() |
[3] | V. Hautamäki, P. Nykänen, P. Fränti, Time-series clustering by approximate prototypes, 19th International conference on pattern recognition, (2008), 1–4. IEEE. https://doi.org/10.1109/ICPR.2008.4761105 |
[4] |
P. Fränti, R. Mariescu-Istodor, Averaging gps segments: competition 2019, Pattern Recogn., 112 (2021), 107730. https://doi.org/10.1016/j.patcog.2020.107730 doi: 10.1016/j.patcog.2020.107730
![]() |
[5] | P. Fränti, S. Sieranoja, K. Wikström, T. Laatikainen, Clustering diagnoses from 58m patient visits in Finland 2015-2018, 2022. |
[6] | M. Fatemi, P. Fränti, Clustering nordic twitter users based on their connections, 2023. |
[7] |
M. I. Malinen, P. Fränti, Clustering by analytic functions, Inform. Sciences, 217 (2012), 31–38. https://doi.org/10.1016/j.ins.2012.06.018 doi: 10.1016/j.ins.2012.06.018
![]() |
[8] | M. I. Malinen, P. Fränti, Balanced k-means for clustering, in: Joint Int. Workshop on Structural, Syntactic, and Statistical Pattern Recognition (S+SSPR 2014), LNCS 8621, Joensuu, Finland, 2014. |
[9] |
D. Aloise, A. Deshpande, P. Hansen, P. Popat, NP-hardness of Euclidean sum-of-squares clustering, Mach. Learn., 75 (2009), 245–248. https://doi.org/10.1007/s10994-009-5103-0 doi: 10.1007/s10994-009-5103-0
![]() |
[10] |
M. Inaba, N. Katoh, H. Imai, Applications of Weighted Voronoi Diagrams and Randomization to Variance-Based k-Clustering, ACM symposium on computational geometry (SCG 1994), (1994), 332–339. https://doi.org/10.1145/177424.178042 doi: 10.1145/177424.178042
![]() |
[11] | J. MacQueen, Some methods of classification and analysis of multivariate observations, Berkeley Symp. Mathemat. Statist. Probab., 1 (1967), 281–297. |
[12] |
W. H. Equitz, A New Vector Quantization Clustering Algorithm, IEEE Trans. Acoust., Speech, Signal Processing, 37 (1989), 1568–1575. https://doi.org/10.1109/29.35395 doi: 10.1109/29.35395
![]() |
[13] |
P. Fränti, O. Virmajoki, V. Hautamäki, Fast agglomerative clustering using a k-nearest neighbor graph, IEEE T. Pattern Anal., 28 (2006), 1875–1881. https://doi.org/10.1109/TPAMI.2006.227 doi: 10.1109/TPAMI.2006.227
![]() |
[14] |
P. Fränti, O. Virmajoki, Iterative shrinking method for clustering problems, Pattern Recogn., 39 (2006), 761–765. https://doi.org/10.1016/j.patcog.2005.09.012 doi: 10.1016/j.patcog.2005.09.012
![]() |
[15] |
P. Fränti, Efficiency of random swap clustering, Journal of Big Data, 5 (2018), 1–29. https://doi.org/10.1186/s40537-018-0122-y doi: 10.1186/s40537-018-0122-y
![]() |
[16] | B. Fritzke, Breathing k-means, arXiv: 2006.15666. |
[17] | C. Baldassi, Recombinator-k-means:an evolutionary algorithm that exploits k-means++ for recombination, IEEE T. Evolut. Comput., 26 (2022), 991–1003. |
[18] |
A. P. Dempster, N. M. Laird, D. B. Rubin, Maximun likelihood from incomplete data via the EM algorithm, J. R. Stat. Soc. B, 39 (1977), 1–38. https://doi.org/10.1111/j.2517-6161.1977.tb01600.x doi: 10.1111/j.2517-6161.1977.tb01600.x
![]() |
[19] |
Q. Zhao, V. Hautamäki, I. Kärkkäinen, P. Fränti, Random swap EM algorithm for finite mixture models in image segmentation, IEEE International Conference on Image Processing (ICIP), (2009), 2397–2400. https://doi.org/10.1109/ICIP.2009.5414459 doi: 10.1109/ICIP.2009.5414459
![]() |
[20] |
J. Shi, J. Malik, Normalized cuts and image segmentation, IEEE T. Pattern Anal., 22 (2000), 888–905. https://doi.org/10.1109/34.868688 doi: 10.1109/34.868688
![]() |
[21] | C. H. Q. Ding, X. He, H. Zha, M. Gu, H. D. Simon, A min-max cut algorithm for graph partitioning and data clustering, IEEE International Conference on Data Mining (ICDM), (2001), 107–114. |
[22] |
M. I. Malinen, P. Fränti, K-means*: Clustering by gradual data transformation, Pattern Recogn., 47 (2014), 3376–3386. https://doi.org/10.1016/j.patcog.2014.03.034 doi: 10.1016/j.patcog.2014.03.034
![]() |
[23] | R. Nallusamy, K. Duraiswamy, R. Dhanalaksmi, P. Parthiban, Optimization of non-linear multiple traveling salesman problem using k-means clustering, shrink wrap algorithm and meta-heuristics, International Journal of Nonlinear Science, 9 (2010), 171–177. |
[24] |
R. Mariescu-Istodor, P. Fränti, Solving the large-scale tsp problem in 1 h: Santa claus challenge 2020, Front. Robot. AI, (2021), 1–20. https://doi.org/10.3389/frobt.2021.689908 doi: 10.3389/frobt.2021.689908
![]() |
[25] | D. W. Sambo, B. O. Yenke, A. Förster, P. Dayang, Optimized clustering algorithms for large wireless sensor networks: A review, Sensors, 19 (2019), 322. |
[26] | J. Singh, R. Kumar, A. K. Mishra, Clustering algorithms for wireless sensor networks: A review, International Conference on Computing for Sustainable Global Development (INDIACom), (2015), 637–642. |
[27] |
Y. Liao, H. Qi, W. Li, Load-Balanced Clustering Algorithm With Distributed Self-Organization for Wireless Sensor Networks, IEEE Sens. J., 13 (2013), 1498–1506. https://doi.org/10.1109/JSEN.2012.2227704 doi: 10.1109/JSEN.2012.2227704
![]() |
[28] | L. Yao, X. Cui, M. Wang, An energy-balanced clustering routing algorithm for wireless sensor networks, IEEE World Congress on Computer Science and Information Engineering, 3 (2009), 316–320. |
[29] | P. S. Bradley, K. P. Bennett, A. Demiriz, Constrained k-means clustering, Tech. rep., MSR-TR-2000-65, Microsoft Research, 2000. |
[30] |
S. Zhu, D. Wang, T. Li, Data clustering with size constraints, Knowledge-Based Syst., 23 (2010), 883–889. https://doi.org/10.1016/j.knosys.2010.06.003 doi: 10.1016/j.knosys.2010.06.003
![]() |
[31] |
A. Banerjee, J. Ghosh, Frequency sensitive competitive learning for balanced clustering on high-dimensional hyperspheres, IEEE Transactions on Neural Networks, 15 (2004), 702–719. https://doi.org/10.1109/TNN.2004.824416 doi: 10.1109/TNN.2004.824416
![]() |
[32] | C. T. Althoff, A. Ulges, A. Dengel, Balanced clustering for content-based image browsing, in: GI-Informatiktage 2011, Gesellschaft für Informatik e.V., 2011. |
[33] |
A. Banerjee, J. Ghosh, On scaling up balanced clustering algorithms, SIAM International Conference on Data Mining, (2002), 333–349. https://doi.org/10.1137/1.9781611972726.20 doi: 10.1137/1.9781611972726.20
![]() |
[34] | Y. Chen, Y. Zhang, X. Ji, Size regularized cut for data clustering, Advances in Neural Information Processing Systems, 2005. |
[35] |
Y. Kawahara, K. Nagano, Y. Okamoto, Submodular fractional programming for balanced clustering, Pattern Recogn. Lett., 32 (2011), 235–243. https://doi.org/10.1016/j.patrec.2010.08.008 doi: 10.1016/j.patrec.2010.08.008
![]() |
[36] |
G. Tzortzis, A. Likas, The minmax k-means clustering algorithm, Pattern Recogn., 47 (2014), 2505–2516. https://doi.org/10.1016/j.patcog.2014.01.015 doi: 10.1016/j.patcog.2014.01.015
![]() |
[37] |
W. Tang, Y. Yang, L. Zeng, Y. Zhan, Optimizing mse for clustering with balanced size constraints, Symmetry, 11 (2019), 338. https://doi.org/10.3390/sym11030338 doi: 10.3390/sym11030338
![]() |
[38] |
L. Hagen, A. B. Kahng, New spectrxal methods for ratio cut partitioning and clustering, IEEE T. Computer-Aided D., 11 (1992), 1074–1085. https://doi.org/10.1109/43.159993 doi: 10.1109/43.159993
![]() |
[39] | T. H. Cormen, C. E. Leiserson, R. L. Rivest, C. Stein, Introduction to algorithms (2nd ed.), MIT Press and McGraw-Hill, 2001. |
[40] |
M. X. Goemans, D. P. Williamson, Improved approximation algorithms for maximum cut and satisfiability problems using semidefinite programming, J. ACM, 42 (1995), 1115–1145. https://doi.org/10.1145/227683.227684 doi: 10.1145/227683.227684
![]() |
[41] |
S. Arora, S. Rao, U. Vazirani, Expander flows, geometric embeddings and graph partitioning, J. ACM, 56 (2009), 1–37. https://doi.org/10.1145/1502793.1502794 doi: 10.1145/1502793.1502794
![]() |
[42] |
U. von Luxburg, A tutorial on spectral clustering, Stat. Comput., 17 (2007), 395–416. https://doi.org/10.1007/s11222-007-9033-z doi: 10.1007/s11222-007-9033-z
![]() |
[43] | M. R. Garey, D. S. Johnson, Computers and intractability: A guide to the theory of NP-completeness, W. H. Freeman, 1979. |
[44] | T. D. Bie, N. Cristianini, Fast sdp relaxations of graph cut clustering, transduction, and other combinatorial problems, J. Mach. Learn. Res., 7 (2006), 1409–1436. |
[45] |
A. Frieze, M. Jerrum, Improved approximation algorithms for max-k-cut and max bisection, Algorithmica, 18 (1997), 67–81. https://doi.org/10.1007/BF02523688 doi: 10.1007/BF02523688
![]() |
[46] |
W. Zhu, C. Guo, A local search approximation algorithm for max-k-cut of graph and hypergraph, International Symposium on Parallel Architectures, Algorithms and Programming, (2011), 236–240. https://doi.org/10.1109/PAAP.2011.35 doi: 10.1109/PAAP.2011.35
![]() |
[47] |
A. V. Kel'manov, A. V. Pyatkin, On the complexity of some quadratic euclidean 2-clustering problems, Comput. Math. Math. Phys., 56 (2016), 491–497. https://doi.org/10.1134/S096554251603009X doi: 10.1134/S096554251603009X
![]() |
[48] |
L. J. Schulman, Clustering for edge-cost minimization, Ann. ACM Symp. on Theory of Computing (STOC), (2000), 547–555. https://doi.org/10.1145/335305.335373 doi: 10.1145/335305.335373
![]() |
[49] |
S. Sahni, T. Gonzalez, P-complete approximation problems, J. ACM, 23 (1976), 555–565. https://doi.org/10.1145/321958.321975 doi: 10.1145/321958.321975
![]() |
[50] |
W. F. de la Vega, M. Karpinski, C. Kenyon, Y. Rabani, Approximation schemes for clustering problems, ACM symposium on Theory of computing (STOC '03), (2003), 50–58. https://doi.org/10.1145/780542.780550 doi: 10.1145/780542.780550
![]() |
[51] |
N. Guttmann-Beck, R. Hassin, Approximation algorithms for min-sum p-clustering, Discrete Appl. Math., 89 (1998), 125–142. https://doi.org/10.1016/S0166-218X(98)00100-0 doi: 10.1016/S0166-218X(98)00100-0
![]() |
[52] | H. Späth, Cluster analysis algorithms for data reduction and classification of objects, Wiley, New York, 1980. |
[53] | P. Fränti, S. Sieranoja, Clustering datasets, University of Eastern Finland, 2020. Available from: http://cs.uef.fi/sipu/datasets/. |
[54] |
P. Fränti, M. Rezaei, Q. Zhao, Centroid index: Cluster level similarity measure, Pattern Recogn., 47 (2014), 3034–3045. https://doi.org/10.1016/j.patcog.2014.03.017 doi: 10.1016/j.patcog.2014.03.017
![]() |
[55] |
S. Sieranoja, P. Fränti, Fast and general density peaks clustering, Pattern Recogn. Lett., 128 (2019), 551–558. https://doi.org/10.1016/j.patrec.2019.10.019 doi: 10.1016/j.patrec.2019.10.019
![]() |
[56] |
P. Fränti, Genetic algorithm with deterministic crossover for vector quantization, Pattern Recogn. Lett., 21 (2000), 61–68. https://doi.org/10.1016/S0167-8655(99)00133-6 doi: 10.1016/S0167-8655(99)00133-6
![]() |
[57] | T. Cour, S. Yu, J. Shi, Normalized Cut Segmentation Code, 2004. |