Loading [MathJax]/jax/output/SVG/jax.js
Research article Special Issues

Multiscale regression on unknown manifolds

  • We consider the regression problem of estimating functions on RD but supported on a d-dimensional manifold M  RD with dD. Drawing ideas from multi-resolution analysis and nonlinear approximation, we construct low-dimensional coordinates on M at multiple scales, and perform multiscale regression by local polynomial fitting. We propose a data-driven wavelet thresholding scheme that automatically adapts to the unknown regularity of the function, allowing for efficient estimation of functions exhibiting nonuniform regularity at different locations and scales. We analyze the generalization error of our method by proving finite sample bounds in high probability on rich classes of priors. Our estimator attains optimal learning rates (up to logarithmic factors) as if the function was defined on a known Euclidean domain of dimension d, instead of an unknown manifold embedded in RD. The implemented algorithm has quasilinear complexity in the sample size, with constants linear in D and exponential in d. Our work therefore establishes a new framework for regression on low-dimensional sets embedded in high dimensions, with fast implementation and strong theoretical guarantees.

    Citation: Wenjing Liao, Mauro Maggioni, Stefano Vigogna. Multiscale regression on unknown manifolds[J]. Mathematics in Engineering, 2022, 4(4): 1-25. doi: 10.3934/mine.2022028

    Related Papers:

    [1] Grigoriy Gogoshin, Sergio Branciamore, Andrei S. Rodin . Synthetic data generation with probabilistic Bayesian Networks. Mathematical Biosciences and Engineering, 2021, 18(6): 8603-8621. doi: 10.3934/mbe.2021426
    [2] Eugene Kashdan, Dominique Duncan, Andrew Parnell, Heinz Schättler . Mathematical methods in systems biology. Mathematical Biosciences and Engineering, 2016, 13(6): i-ii. doi: 10.3934/mbe.201606i
    [3] Chichia Chiu, Jui-Ling Yu . An optimal adaptive time-stepping scheme for solving reaction-diffusion-chemotaxis systems. Mathematical Biosciences and Engineering, 2007, 4(2): 187-203. doi: 10.3934/mbe.2007.4.187
    [4] Madeleine Dawson, Carson Dudley, Sasamon Omoma, Hwai-Ray Tung, Maria-Veronica Ciocanel . Characterizing emerging features in cell dynamics using topological data analysis methods. Mathematical Biosciences and Engineering, 2023, 20(2): 3023-3046. doi: 10.3934/mbe.2023143
    [5] Dawei Ren, Xiaodong Zhang, Shaojuan Lei, Zehua Bi . Research on flexibility of production system based on hybrid modeling and simulation. Mathematical Biosciences and Engineering, 2021, 18(1): 933-949. doi: 10.3934/mbe.2021049
    [6] Hilla Behar, Alexandra Agranovich, Yoram Louzoun . Diffusion rate determines balance between extinction and proliferationin birth-death processes. Mathematical Biosciences and Engineering, 2013, 10(3): 523-550. doi: 10.3934/mbe.2013.10.523
    [7] Urszula Ledzewicz, Avner Friedman, Jacek Banasiak, Heinz Schättler, Edward M. Lungu . From the guest editors. Mathematical Biosciences and Engineering, 2013, 10(3): i-ii. doi: 10.3934/mbe.2013.10.3i
    [8] Cristeta U. Jamilla, Renier G. Mendoza, Victoria May P. Mendoza . Explicit solution of a Lotka-Sharpe-McKendrick system involving neutral delay differential equations using the r-Lambert W function. Mathematical Biosciences and Engineering, 2020, 17(5): 5686-5708. doi: 10.3934/mbe.2020306
    [9] Meijuan Sun . A Vision sensing-based automatic evaluation method for teaching effect based on deep residual network. Mathematical Biosciences and Engineering, 2023, 20(4): 6358-6373. doi: 10.3934/mbe.2023275
    [10] Anissa Guillemin, Michael P. H. Stumpf . Non-equilibrium statistical physics, transitory epigenetic landscapes, and cell fate decision dynamics. Mathematical Biosciences and Engineering, 2020, 17(6): 7916-7930. doi: 10.3934/mbe.2020402
  • We consider the regression problem of estimating functions on RD but supported on a d-dimensional manifold M  RD with dD. Drawing ideas from multi-resolution analysis and nonlinear approximation, we construct low-dimensional coordinates on M at multiple scales, and perform multiscale regression by local polynomial fitting. We propose a data-driven wavelet thresholding scheme that automatically adapts to the unknown regularity of the function, allowing for efficient estimation of functions exhibiting nonuniform regularity at different locations and scales. We analyze the generalization error of our method by proving finite sample bounds in high probability on rich classes of priors. Our estimator attains optimal learning rates (up to logarithmic factors) as if the function was defined on a known Euclidean domain of dimension d, instead of an unknown manifold embedded in RD. The implemented algorithm has quasilinear complexity in the sample size, with constants linear in D and exponential in d. Our work therefore establishes a new framework for regression on low-dimensional sets embedded in high dimensions, with fast implementation and strong theoretical guarantees.



    In this paper, we consider the following p-Laplacian fractional differential equation involving Riemann-Stieltjes integral boundary condition

    {Dβt(φp(Dαtz(t)g(t,z(t),Dγtz(t))))=f(t,z(t),Dγtz(t)), 0<t<1,Dαtz(0)=Dα+1tz(0)=Dγtz(0)=0,Dαtz(1)=0,Dγtz(1)=10Dγtz(s)dA(s),  (1.1)

    where Dαt,Dβt,Dγt are the Riemann-Liouville fractional derivatives of orders α,β,γ with 0<γ1<α2<β<3,αγ>1,10Dγtz(t)dA(s) denotes a Riemann-Stieltjes integral, and A is a function of bounded variation. The p-Laplacian operator is defined as φp(s)=|s|p2s, p>2, φp(s) is invertible and its inverse operator is φq(s), where q=pp1 is the conjugate index of p.

    Fractional calculus and fractional differential equations arise in many fields, such as, mathematics, physics, economics, engineering, biology, electroanalytical chemistry, capacitor theory, electrical circuits, control theory, and fluid dynamics, see [1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49]. The problem (1.1) can be regarded as a fractional order model for the turbulent flow in a porous medium, see [8,50]. As we know, integral boundary value problems have different applications in applied fields such as blood flow problems, chemical engineering, underground water flow and population dynamics. For example, in [31], Meng and Cui considered the following fractional differential equation involving integral boundary condition

    {Dαx(t)=f(t,x(t)), 0<t<1,x(0)=10x(t)dA(t),  (1.2)

    where fC([0,1]×R,R), 10x(t)dA(t) denotes the Riemann-Stieltjes integral with positive Stieltjes measure. Dα is the conformable fractional derivative of order 0<α1 at t>0. By topological degree theory, the method of lower and upper solutions and a fixed point theorem, they discussed the existence of at least three solutions to the problem (1.2).

    In [8], the authors studied the following fractional differential boundary value problem

    {Dβt(φp(Dαtx))(t)=f(x(t),Dγtx(t)), t(0,1),Dαtx(0)=Dα+1tx(0)=Dαtx(1)=0,Dγtx(0)=0, Dγtx(1)=10Dγtx(s)dA(s),  (1.3)

    where Dαt, Dβt, Dγt are the Riemann-Liouville derivatives, 10x(s)dA(s) is the Riemann-Stieltjes integral and 0<γ1<α2<β<3, αγ>1, A is a function bounded variation, φp is the p-Laplacian operator. By employing a fixed point theorem for mixed monotone operator, they obtained the existence and uniqueness of positive solutions for the problem (1.3).

    When g0 in (1.1), the author in [21] gave the existence and uniqueness of positive solutions by using monotone iterative technique. Motivated by the results mentioned above and wide applications of different boundary value conditions, we consider the existence and uniqueness of positive solutions for p-Laplacian fractional order differential equation involving Riemann-Stieltjes integral boundary condition (1.1). In Section 2, we present some preliminaries that can be used to prove our main results. The main theorems are formulated and proved in Section 3. Two simple examples are given to illustrate the main results in Section 4.

    In the following, we start with some basic concepts and lemmas.

    Definition 2.1. [1] For a function x:(0,+)R, the Riemann-Liouville fractional integral of order α>0 is

    Iαx(t)=1Γ(α)t0(ts)α1x(s)ds,

    provide that the right-hand side is pointwise defined on (0,+).

    Definition 2.2. [1] For a function x:(0,+)R, the Riemann-Liouville fractional derivative of order α>0 is

    Dαtx(t)=1Γ(nα)(ddt)nt0(ts)nα1x(s)ds,

    where n=[α]+1, [α] denotes the integer part of the number α, provided that the right-hand side is pointwise defined on (0,+).

    To reduce the p-Laplacian fractional order differential equation (1.1) to a convenient form, for xC[0,1], making a change of variable z(t)=Iγx(t). By the definitions of the Riemann-Liouville fractional integral and derivative, we can see that Iγx(t)0,Dαtx(t)0 as t0+. So we first get x(0)=0. From [8,21], the problem (1.1) reduces to an equivalent boundary value problem as follows:

    {Dβtφp(Dαγtx(t)g(t,Iγx(t),x(t)))=f(t,Iγx(t),x(t))Dαγtx(0)=Dαγ+1tx(0)=Dαγtx(0)=0,x(0)=0,x(1)=10x(s)dA(s).  (2.1)

    So, to get the existence and uniqueness of positive solutions for the problem (1.1), we only need to condiser the equivalent problem (2.1). To do this, we fist give an important function

    Gβ(t,s)=1Γ(β){[t(1s)]β1, 0ts1,1[t(1s)]β1(ts)β1, 0st1. (2.2)

    From Lemma 2.2 in [8], we have the following conclusion:

    Lemma 2.1. Given f, gL1[0,1], 0<γ1<α2<β<3 and αγ>1, the fractional order p-Laplacian differential equation

    {Dβt(φp(Dαγtx(t)g(t))=f(t),Dαγtx(0)=Dαγ+1tx(0)=Dαγtx(1)=0,x(0)=0,x(1)=10x(s)dA(s)  (2.3)

    has a unique solution

    x(t)=10H(t,s)φq(10Gβ(s,τ)f(τ)dτ)ds+10H(t,s)g(s)ds,

    where

    H(t,s)=GA(s)1Atαγ1+Gαγ(t,s) (2.4)

    with

    A=10tαγ1dA(t), GA(s)=10Gαβ(t,s)dA(t).

    Lemma 2.2. [21] Let 0A<1 and GA(s)0 for s[0,1], then the functions Gβ(t,s) and H(t,s) satisfy:

    (1) Gβ(t,s)>0,H(t,s)>0, for t,s(0,1);

    (2) tβ1(1t)s(1s)β1Γ(β)Gβ(t,s)β1Γ(β)tβ1(1t) for t,s[0,1];

    (3) There exist two positive constants d,e such that

    dtαγ1GA(s)H(t,s)etαγ1, t,s[0,1].

    Let (E,) be a real Banach space and θ be the zero element of E. E is partially ordered by a cone PE, i.e., xy if and only if yxP. A cone P is called normal if there exists a constant N>0 such that, for all x,yE, θxyxNy; in this case, N is called the normality constant of P. We say that an operator A:EE is increasing (decreasing) if xy implies AxAy(AxAy).

    For x,yE, the notation xy denotes that there exist λ>0 and μ>0 such that λxyμx. Clearly, is an equivalence relation. Given h>θ (i.e., hθ and hθ), define Ph={xE:xh}. It is clear to see that PhP.

    Definition 2.3. [51] Let 0<δ<1. An operator A:PP is said to be δconcave if A(tx)tδAx for t(0,1), xP. An operator A:PP is called to be sub-homogeneous if A(tx)tAx for t>0, xP.

    In papers [52,53], the authors investigated a sum operator equation

    Ax+Bx=x, (2.5)

    where A,B are monotone operators. They gave the existence and uniqueness of positive solutions for (2.5) and obtained some interesting theorems.

    Lemma 2.3. [52] Let E be a real Banach space. P is a normal cone in E, A:PP is an increasing δ-concave operator and B : PP is an increasing sub-homogeneous operator. Suppose that

    (ⅰ) there is h>θ such that AhPh and BhPh;

    (ⅱ) there exists a constant δ0>0 such that Axδ0Bx for all xP.

    Then the operator equation (2.5) has a unique solution x in Ph. Further, making the sequence yn=Ayn1+Byn1, n=1,2 for any initial value y0Ph, one has ynx as n.

    Lemma 2.4. [53] Let E be a real Banach space. P is a normal cone in E, A:PP is an increasing operator, and B:PP is a decreasing operator. In addition,

    (ⅰ) for xP and t(0,1), there exist ϕi(t)(t,1),i=1,2 such that

    A(tx)ϕ1(t)Ax,  B(tx)1ϕ2(t)Bx; (2.6)

    (ⅱ) there is h0Ph such that Ah0+Bh0Ph.

    Then the operator equation (2.5) has a unique solution x in Ph. Further, for any initial values x0,y0Ph, making the sequences

    xn=Axn1+Byn1,yn=Ayn1+Bxn1,n=1,2,

    one has xnx,ynx as n.

    Remark 2.1. If B is a null operator, the conclusions in Lemmas 2.1 and 2.2 are still right.

    In this section, we intend to obtain some results on the existence and uniqueness of positive solutions for the problem (1.1) by using Lemmas 2.3 and 2.4.

    We work in a Banach space E=C[0,1] with the usual norm x=sup{|x(t)|:t[0,1]}. Let P={xC[0,1]:x(t)0,t[0,1]}, then it is a normal cone in C[0,1]. Hence this space is equipped with a partial order

    xy, x,yC[0,1]x(t)y(t), t[0,1].

    Theorem 3.1. Let 0A<1 and GA(s)0 for s[0,1]. Assume

    (H1) f,g:[0,1)×[0,+)×[0,+)[0,+) are continuous and increasing with respect to the second and third arguments, g(t,0,0)0, t[0,1];

    (H2) for λ(0,1), f(t,λx,λy)λ1q1f(t,x,y) for x,y[0,+) and there exists a constant δ(0,1) such that g(t,λx,λy)λδg(t,x,y) for all t[0,1], x,y[0,+);

    (H3) There exists a constant δ0>0 such that f(t,x,y)δ0g(t,0,0), t[0,1], x0, y0.

    Then there is a unique yPh, where h(t)=tαγ1, t[0,1], such that the problem (1.1) has a unique positive solution z(t)=Iγy(t) in set Ω:={Iγy(t)|yPh}. And for any initial value y0Ph, making sequences

    yn(t)=10H(t,s)φq(10Gβ(s,τ)f(τ,Iγyn1(τ),yn1(τ))dτ)ds+10H(t,s)g(s,Iγyn1(s),yn1(s))ds

    and zn(t)=Iγyn(t), n=1,2, we have yn(t)y(t) and zn(t)z(t) as n, where Gβ(s,τ),H(t,s) are given as in (2.2), (2.4) respectively.

    Proof. From Lemma 2.1, we know that the problem (2.1) has an integral formulation give by

    y(t)=10H(t,s)φq(10Gβ(s,τ)f(τ,Iγy(τ),y(τ))dτ)ds+10H(t,s)g(s,Iγy(s),y(s))ds.

    Define two operators A:PE and B:PE by

    Ay(t)=10H(t,s)g(s,Iγy(s),y(s))ds,By(t)=10H(t,s)φq(10Gβ(s,τ)f(τ,Iγy(τ),y(τ))dτ)ds.

    Then we see that y is the solution of the problem (2.1) if and only if y=Ay+By. From (H1), (2.4) and Lemma 2.2, we can easily get A:PP and B:PP. In the following, we show that A,B satisfy all assumptions of Lemma 2.3.

    Firstly, we prove that A,B are two increasing operators. For y1,y2P with y1y2, we have y1(t)y2(t), t[0,1] and thus Iγy1(t)Iγy2(t). By (H1), Lemma 2.2,

    Ay1(t)=10H(t,s)g(s,Iγy1(s),y1(s))ds10H(t,s)g(s,Iγy2(s),y2(s))ds=Ay2(t).

    Further, noting that φp(t) is increasing in t, we obtain

    By1(t)=10H(t,s)φq(10Gβ(s,τ)f(τ,Iγy1(τ),y1(τ))dτ)ds10H(t,s)φq(10Gβ(s,τ)f(τ,Iγy2(τ),y2(τ))dτ)ds=By2(t).

    That is, Ay1Ay2 and By1By2.

    Secondly, we claim that operator A is δconcave and operator B is sub-homogeneous. For any λ(0,1) and yP, from (H2),

    A(λy)(t)=10H(t,s)g(s,Iγ(λy)(s),λy(s))ds=10H(t,s)g(s,λIγy(s),λy(s))dsλδ10H(t,s)g(s,Iγy(s),y(s))ds=λδAy(t),

    that is, A(λy)λδAy for λ(0,1), yP. So operator A is δconcave. Also, for any λ(0,1) and yP, by (H2),

    B(λy)(t)=10H(t,s)φq(10Gβ(s,τ)f(τ,Iγ(λy)(τ),λy(τ))dτ)ds=10H(t,s)φq(10Gβ(s,τ)f(τ,λIγy(τ),λy(τ))dτ)ds10H(t,s)φq(λ1q110Gβ(s,τ)f(τ,Iγy(τ),y(τ))dτ)ds=λ10H(t,s)φq(10Gβ(s,τ)f(τ,Iγy(τ),y(τ))dτ)ds=λBy(t),

    that is, B(λy)λBy for λ(0,1), yP. So operator B is sub-homogeneous.

    Thirdly, we show AhPh and BhPh. Let

    m1=d10GA(s)g(s,0,0)ds, m2=e10g(s,1Γ(γ+1),1)ds,
    l1=d(Γ(β))q110GA(s)[sβ1(1s)]q1ds[10τβ1(1τ)f(τ,0,0)dτ]q1,
    l2=e(β1Γ(β))q1[10f(τ,1Γ(γ+1),1)dτ]q1.

    From (H1) and Lemma 2.2,

    Ah(t)=10H(t,s)g(s,Iγh(s),h(s))dse10g(s,Iγ1,1)dstαγ1=e10g(s,tγΓ(γ+1),1)dsh(t)e10g(s,1Γ(γ+1),1)dsh(t)=m2h(t).

    Also,

    Ah(t)=10H(t,s)g(s,Iγh(s),h(s))dsd10GA(s)g(s,Iγ0,0)dstαγ1=d10GA(s)g(s,0,0)dsh(t)=m1h(t).

    By similar discussion, it follows from (H1) and Lemma 2.2 that

    Bh(t)=10H(t,s)φq(10Gβ(s,τ)f(τ,Iγh(τ),h(τ))dτ)ds10etαγ1(10β1Γ(β)sβ1(1s)f(τ,Iγh(τ),h(τ))dτ)q1dse(β1Γ(β))q1(10f(τ,Iγh(τ),h(τ))dτ)q1tαγ1e(β1Γ(β))q1(10f(τ,Iγ1,1)dτ)q1tαγ1=e(β1Γ(β))q1(10f(τ,τγΓ(γ+1),1)dτ)q1h(t)e(β1Γ(β))q1(10f(τ,1Γ(γ+1),1)dτ)q1h(t)=l2h(t)

    and

    Bh(t)=10H(t,s)φq(10Gβ(s,τ)f(τ,Iγh(τ),h(τ))dτ)dsdtαγ110GA(s)(10τβ1(1τ)s(1s)β1Γ(β)f(τ,Iγh(τ),h(τ))dτ)q1ds=d(Γ(β))q110GA(s)[sβ1(1s)]q1ds(10τβ1(1τ)f(τ,Iγh(τ),h(τ))dτ)q1tαγ1d(Γ(β))q110GA(s)[sβ1(1s)]q1ds(10τβ1(1τ)f(τ,Iγ0,0)dτ)q1tαγ1=d(Γ(β))q110GA(s)[sβ1(1s)]q1ds(10τβ1(1τ)f(τ,0,0)dτ)q1h(t)=l1h(t).

    Note that g(t,0,0)0, GA(s)0 and f(τ,1Γ(γ+1))f(τ,0,0), we can easily prove 0<m1m2 and 0<l1l2, and thus m1hAhm2h, l1hBhl2h. So we have Ah,BhPh. It means that the first condition of Lemma 2.3 holds.

    Next we prove that the second condition of Lemma 2.3 is also satisfied. For yP, by (H3),

    By(t)=10H(t,s)φq(10Gβ(s,τ)f(τ,Iγy(τ),y(τ))dτ)ds10H(t,s)φq(10β1Γ(β)sq1(1s)f(τ,Iγy(τ),y(τ))dτ)ds(β1Γ(β))q110H(t,s)dsφq(10f(τ,Iγy(τ),y(τ))dτ)(β1Γ(β))q110δ0q1H(t,s)ds=(β1Γ(β))q1δq2010δ0H(t,s)ds(β1Γ(β))q1δq2010H(t,s)g(s,0,0)ds(1Γ(β1))q1δq2010H(t,s)g(s,Iγy(s),y(s))ds=(1Γ(β1))q1δq20Ay(t).

    Let δ0=[Γ(β1)]q1δ2q0, so we obtain Ay(t)δ0By(t), t[0,1]. Therefore, Ayδ0By for yP.

    By the above discussion and Lemma 2.3, we know that operator equation Ay+By=y has a unique solution y in Ph; for any initial value y0Ph, making a sequence yn=Ayn1+Byn1, n=1,2,, we have yny as n. Evidently, z(t):=Iγy(t) is the unique solution of the problem (1.1) in Ω={Iγy(t)|yPh}. And for any initial value y0Ph, the sequences

    yn+1(t)=10H(t,s)φq(10Gβ(s,τ)f(τ,Iγyn(τ),yn(τ))dτ)ds+10H(t,s)g(s,Iγyn(s),yn(s))ds

    and zn(t)=Iγyn(t), n=1,2 satisfy yn(t)y(t) and zn(t)z(t) as n.

    Corollary 3.1. Let 0A<1 and GA(s)0 for s[0,1]. Assume that f satisfies (H1) and for λ(0,1), there exists a constant δ(0,1) such that f(t,λx,λy)λδf(t,x,y) for all t[0,1], x,y[0,+).

    Then there is a unique yPh, where h(t)=tαγ1, t[0,1], such that the following problem

    {Dβt(φp(Dαtz(t))=f(t,z(t),Dγtz(t)), 0<t<1,Dαtz(0)=Dα+1tz(0)=Dγtz(0)=0,Dαtz(1)=0,Dγtz(1)=10Dγtz(s)dA(s), 

    has a unique positive solution z=Iγy in Ω={Iγy(t)|yPh}. And for any initial value y0Ph, making the sequences

    yn+1(t)=10H(t,s)φq(10Gβ(s,τ)g(τ,Iγyn(τ),yn(τ))dτ)ds, n=0,1,2

    and zn(t)=Iγyn(t), n=1,2, we have yn(t)y(t) and zn(t)z(t) as n, where Gβ(s,τ),H(t,s) are given as in (2.2), (2.4) respectively.

    Proof. From Remark 2.1 and Theorem 3.1, the conclusion holds.

    Theorem 3.2. Let 0A<1 and GA(s)0 for s[0,1]. Assume f satisfies (H1) and

    (H4) g:[0,1]×[0,+)×[0,+) is continuous and decreasing with respect to second and third arguments, g(t,1Γ(γ+1),1)0,t[0,1];

    (H5) for λ(0,1), there exist ϕi(λ)(λ,1)(i=1,2) such that

    f(t,λx,λy)ϕ11q1(λ)f(t,x,y), g(t,λx,λy)1ϕ2(λ)g(t,x,y)

    for t[0,1],x,y[0,+).

    Then there is a unique yPh, where h(t)=tαγ1, t[0,1], such that the problem (1.1) has a unique positive solution z(t)=Iγy(t) in set Ω={Iγy(t)|yPh}. And for any initial values x0,y0Ph, putting the sequences

    xn+1(t)=10H(t,s)φq(10Gβ(s,τ)f(τ,Iγxn(τ),xn(τ))dτ)ds+10H(t,s)g(s,Iγyn(s),yn(s))dsyn+1(t)=10H(t,s)φq(10Gβ(s,τ)f(τ,Iγyn(τ),yn(τ))dτ)ds+10H(t,s)g(s,Iγxn(s),xn(s))ds

    and ¯zn(t)=Iγxn(t), zn(t)=Iγyn(t), n=0,1,2, we have xn(t)y(t),yn(t)y(t),¯zn(t)z(t), zn(t)z(t) as n, where Gβ(s,τ),H(t,s) are given as in (2.2), (2.4) respectively.

    Proof. Similar to the proof of Theorem 3.1, we still consider two operators A:PE and B:PE given by

    Ay(t)=10H(t,s)φq(10Gβ(s,τ)f(τ,Iγy(τ),y(τ))dτ)ds,
    By(t)=10H(t,s)g(s,Iγy(s),y(s))ds.

    It follows from Lemma 2.2, (H1) and (H4) that A:PP is increasing and B:PP is decreasing.

    Further, from (H5), for λ(0,1),

    A(λy)(t)=10H(t,s)φq(10Gβ(s,τ)f(τ,Iγ(λy)(τ),λy(τ))dτ)ds=10H(t,s)φq(10Gβ(s,τ)f(τ,λIγ(y)(τ),λy(τ))dτ)ds10H(t,s)(10Gβ(s,τ)ϕ11q1(λ)f(τ,Iγy(τ),y(τ))dτ)q1ds=ϕ1(λ)10H(t,s)φq(10Gβ(s,τ)f(τ,Iγy(τ),y(τ))dτ)ds=ϕ1(λ)Ay(t)

    and

    B(λy)(t)=10H(t,s)g(s,Iγ(λy(s)),λy(s))ds=10H(t,s)g(s,λIγ(y(s)),λy(s))ds10H(t,s)1ϕ2(λ)g(s,Iγy(s),y(s))ds=1ϕ2(λ)10H(t,s)g(s,Iγy(s),y(s))ds=1ϕ2(λ)By(t),

    that is, A,B satisfy (2.6). Next, we prove Ah+BhPh. Let

    n1=d10GA(s)g(s,1Γ(γ+1),1)ds, n2=e10g(s,0,0)ds.

    By Lemma 2.2,

    Ah(t)=10H(t,s)φq(10Gβ(s,τ)f(τ,Iγh(τ),h(τ))dτ)ds10etαγ1(10β1Γ(β)sβ1(1s)f(τ,Iγh(τ),h(τ))dτ)q1dse(β1Γ(β))q1(10f(τ,Iγh(τ),h(τ))dτ)q1tαγ1e(β1Γ(β))q1(10f(τ,Iγ1,1)dτ)q1h(t)e(β1Γ(β))q1(10f(τ,1γ+1,1)dτ)q1h(t)=l2h(t),Ah(t)=10H(t,s)φq(10Gβ(s,τ)f(τ,Iγh(τ),h(τ))dτ)dsdtαγ110GA(s)(10τβ1(1τ)s(1s)β1Γ(β)f(τ,Iγh(τ),h(τ))dτ)q1ds=d(Γ(β))q110GA(s)[sβ1(1s)]q1ds(10τβ1(1τ)f(τ,Iγh(τ),h(τ))dτ)q1tαγ1d(Γ(β))q110GA(s)[sβ1(1s)]q1ds(10τβ1(1τ)f(τ,Iγ0,0)dτ)q1h(t)=d(Γ(β))q110GA(s)[sβ1(1s)]q1ds(10τβ1(1τ)f(τ,0,0)dτ)q1h(t)=l1h(t)

    and

    Bh(t)=10H(t,s)g(s,Iγh(s),h(s))dsdtαγ110GA(s)g(s,Iγh(s),h(s))dsdtαγ110GA(s)g(s,Iγ1,1)ds=dtαγ110GA(s)g(s,1Γ(γ+1),1)ds=n1h(t),
    Bh(t)=10H(t,s)g(s,Iγh(s),h(s))dsetαγ110g(s,Iγ0,0)ds=etαγ110g(s,0,0)ds=n2h(t).

    Hence, Ah(t)+Bh(t)l2h(t)+n2h(t)=(l2+n2)h(t) and

    Ah(t)+Bh(t)l1h(t)+n1h(t)=(l1+n1)h(t).

    In addition, it is easy to show l2+n2l1+n1>0. Therefore, Ah+BhPh.

    Consequently, by using Lemma 2.4, operator equation Ay+By=y has a unique solution y in Ph; for given initial values x0, y0Ph, putting the sequences

    xn=Axn1+Byn1,  yn=Ayn1+Bxn1,  n=1,2,

    we have xny, yny as n. Evidently, z(t)=Iγy(t) is the unique solution of the problem (1.1) in Ω={Iγy(t)|yPh}. And for given initial values x0,y0Ph, the following sequences

    xn+1=10H(t,s)φq(10Gβ(s,τ)f(τ,Iγxn(τ),xn(τ))dτ)ds+10H(t,s)g(s,Iγyn(s),yn(s))ds,yn+1=10H(t,s)φq(10Gβ(s,τ)f(τ,Iγyn(τ),yn(τ))dτ)ds+10H(t,s)g(s,Iγyn(s),yn(s))ds

    and ¯zn(t)=Iγxn(t), zn(t)=Iγyn(t), n=0,1,2, satisfy xn(t)y(t),yn(t)y(t),¯zn(t)z(t), zn(t)z(t) as n.

    Corollary 3.2. Let 0A<1 and GA(s)0 for s[0,1]. Assume f satisfies (H1),(H5). Then there is a unique yPh, where h(t)=tαγ1, t[0,1], such that the following problem

    {Dβt(φp(Dαtz))(t)=f(t,z(t),Dγtz(t)), 0<t<1,Dαtz(0)=Dα+1tz(0)=Dγtz(0)=0,Dαtz(1)=0,Dγtz(1)=10Dγtz(s)dA(s), 

    has a unique positive solution z=Iγy in Ω={Iγy(t)|yPh}, where h(t)=tαγ1,t[0,1]. And for any initial value y0Ph, putting the sequences

    yn+1=10H(t,s)φq(10Gβ(s,τ)f(τ,Iγyn(τ),yn(τ))dτ)ds,n=0,1,2,

    and zn(t)=Iγyn(t), n=1,2, we have yn(t)y(t) and zn(t)z(t) as n, where Gβ(s,τ),H(t,s) are given as in (2.2), (2.4) respectively.

    Proof. From Remark 2.1 and Theorem 3.2, the conclusions hold.

    In this section, two examples are given to illustrate our main results.

    Example 4.1. Consider the following 3-Laplacian fractional differential equation with Riemann-Stieltjes integral boundary conditions

    {D94t(φ3(D74tz(t)t14(z14(t)+(D12tz(t))13)3))=cos2t+z13(t)1+z13(t)+(D12tz(t))141+(D12tz(t))14, 0<t<1,D74tz(0)=D114tz(0)=D12tz(0)=0,D74tz(1)=0,D12tz(1)=10D12tz(s)dA(s),  (4.1)

    where α=74, β=94, γ=12, p=3, q=32, A is a function of bounded variation by

    A(t)={0, 0t<14,13, 14t<12,2, 12t1.

    Further, f(t,x,y)=cos2t+x131+x13+y141+y14, g(t,x,y)=t14(x14+y13)+3, clearly, f, gC([0,1]×[0,+)×[0,+), [0,+)),g(t,0,0)0. For fixed t(0,1), f(t,x,y), g(t,x,y) are increasing in x and y. So, the condition (H1) is satisfied.

    In addition, take δ=12, for t[0,1], λ(0,1), x, y[0,+), we have

    g(t,λx,λy)=t14(λ14x14+λ13y13)+3λ12[t14(x14+y13)+3]=λδg(t,x,y).

    On the other hand, for t[0,1], λ(0,1), x,y[0,+),

    f(t,λx,λy)=cos2t+(λx)131+(λx)13+(λy)141+(λy)14λ2cos2t+λ2x131+x13+λ2y141+y14=λ1q1f(t,x,y).

    Hence, the condition (H2) is satisfied.

    Take δ0=3, then

    f(t,x,y)=cos2t+x131+x13+y141+y14δ0=3g(t,0,0).

    The condition (H3) is also satisfied. So Theorem 3.1 shows that the problem (4.1) has a unique positive solution in Ω={Iγy(t)|yPh}, where h(t)=t14, t[0,1].

    Example 4.2. Consider the following 3-Laplacian fractional differential equation with Riemann-Stieltjes integral boundary conditions:

    {D94t(φ3(D74tz(t)[t14(z14(t)+(D12tz(t))13)+1]1=t13[z13(t)+(D12tz(t))14]+2,t(0,1),D74tz(0)=D114tz(0)=D12tz(0)=0,D74tz(1)=0,D12tz(1)=10D12tz(s)dA(s),  (4.2)

    where α=74, β=94, γ=12, p=3, q=32, A is a function of bounded variation by

    A(t)={0, 0t<14,13, 14t<12,2, 12t1.

    Let f(t,x,y)=t13(x13+y14)+2, g(t,x,y)=[t14(x14+y13)+1]1, clearly, f, gC([0,1)×[0,+)×[0,+), [0,+)), f(t,0,0)0, g(t,1Γ(γ+1),1)0. For fixed t[0,1), f(t,x,y) is increasing in x and y, g(t,x,y) is decreasing in x and y. So, the conditions (H4) and (H5) are satisfied. Take ϕ1(λ)=λ16, ϕ2(λ)=λ13, then ϕ1(λ), ϕ2(λ)(λ,1) for λ(0,1). Thus,

    f(t,λx,λy)=t13(λ13x13+λ14y14)+2λ13[t13(x13+y14)+2]=(ϕ1(λ))1q1f(t,x,y),
    g(t,λx,λy)=[t14(λ14x14+λ13y13)+1]1λ13[t14(x14+y13)+1]1=1ϕ2(λ)g(t,x,y).

    So Theorem 3.2 implies that the problem (4.2) has a unique positive solution in Ω={Iγy(t)|yPh}, where h(t)=t14, t[0,1].

    Integral boundary value problems have many applications in applied fields such as blood flow problems, chemical engineering, underground water flow and population dynamics. For nonlinear fractional differential equations with p-Laplacian operator subject to different boundary conditions, there are many works reported on the existence or multiplicity of positive solutions. But the unique results are very rare. In this paper, we study a p-Laplacian fractional order differential equation involving Riemann-Stieltjes integral boundary condition (1.1). By means of the properties of Green's function and two fixed point theorems of a sum operator in partial ordering Banach spaces, we establish some new existence and uniqueness criteria for (1.1). Our result shows that the unique positive solution exists in a special set Ph and can be approximated by constructing an iterative sequence for any initial point in Ph. Finally, two interesting examples are given to illustrate the application of our main results.

    This paper was supported by the Youth Science Foundation of China (11801322), Shanxi Province Science Foundation (201901D111020) and Graduate Science and Technology Innovation Project of Shanxi (2019BY014).

    All authors declare no conflicts of interest in this paper.



    [1] W. K. Allard, G. Chen, M. Maggioni, Multi-scale geometric methods for data sets II: geometric multi-resolution analysis, Appl. Comput. Harmon. Anal., 32 (2012), 435-462. doi: 10.1016/j.acha.2011.08.001
    [2] M. Belkin, P. Niyogi, Laplacian eigenmaps for dimensionality reduction and data representation, Neural Comput., 15 (2003), 1373-1396. doi: 10.1162/089976603321780317
    [3] A. Beygelzimer, S. Kakade, J. Langford, Cover trees for nearest neighbor, In: Proceedings of the 23rd international conference on Machine learning, 2006, 97-104.
    [4] P. J. Bickel, B. Li, Local polynomial regression on unknown manifolds, Lecture Notes-Monograph Series, 54 (2007), 177-186.
    [5] P. Binev, A. Cohen, W. Dahmen, R. A. DeVore, Universal algorithms for learning theory part II: Piecewise polynomial functions, Constr. Approx., 26 (2007), 127-152.
    [6] P. Binev, A. Cohen, W. Dahmen, R. A. DeVore, V. N. Temlyakov, Universal algorithms for learning theory part I: Piecewise constant functions, J. Mach. Learn. Res., 6 (2005), 1297-1321.
    [7] V. Buldygin, E. Pechuk, Inequalities for the distributions of functionals of sub-Gaussian vectors, Theor. Probability and Math. Statist., 80 (2010), 25-36.
    [8] G. Chen, G. Lerman, Spectral Curvature Clustering (SCC), Int. J. Comput. Vis., 81 (2009), 317-330.
    [9] G. Chen, M. Maggioni, Multiscale geometric and spectral analysis of plane arrangements, In: IEEE Conference on Computer Vision and Pattern Recognition, 2011, 2825-2832.
    [10] M. Christ, A T(b) theorem with remarks on analytic capacity and the {C}auchy integral, Colloq. Math., 60/61 (1990), 601-628.
    [11] A. Cohen, W. Dahmen, I. Daubechies, R. A. DeVore, Tree approximation and optimal encoding, Appl. Comput. Harmon. Anal., 11 (2001), 192-226. doi: 10.1006/acha.2001.0336
    [12] R. R. Coifman, S. Lafon, A. B. Lee, M. Maggioni, B. Nadler, F. Warner, et al., Geometric diffusions as a tool for harmonic analysis and structure definition of data: diffusion maps, PNAS, 102 (2005), 7426-7431.
    [13] I. Daubechies, Ten lectures on wavelets, SIAM, 1992.
    [14] D. Deng, Y. Han, Harmonic analysis on spaces of homogeneous type, Springer, 2008.
    [15] D. L. Donoho, C. Grimes, Hessian eigenmaps: locally linear embedding techniques for high-dimensional data, PNAS, 100 (2003), 5591-5596.
    [16] D. L. Donoho, J. M. Johnstone, Ideal spatial adaptation by wavelet shrinkage, Biometrika, 81 (1994), 425-455.
    [17] D. L. Donoho, J. M. Johnstone, Adapting to unknown smoothness via wavelet shrinkage, J. Am. Stat. Assoc., 90 (1995), 1200-1224.
    [18] E. Elhamifar, R. Vidal, Sparse subspace clustering, In: IEEE Conference on Computer Vision and Pattern Recognition, 2009, 2790-2797.
    [19] H. Federer, Curvature measures, T. Am. Math. Soc., 93 (1959), 418-491.
    [20] J. Friedman, T. Hastie, R. Tibshirani, The elements of statistical learning, Springer, 2001.
    [21] L. Györfi, M. Kohler, A. Krzyżak, H. Walk, A distribution-free theory of nonparametric regression, Springer, 2002.
    [22] N. Halko, P. G. Martinsson, J. A. Tropp, Finding structure with randomness: stochastic algorithms for constructing approximate matrix decompositions, SIAM Rev., 53 (2011), 217-288.
    [23] P. C. Hansen, The truncated SVD as a method for regularization, Bit Numer. Math., 27 (1987), 534-553.
    [24] H. Hotelling, Analysis of a complex of statistical variables into principal components, Journal of Educational Psychology, 24 (1933), 417-441.
    [25] H. Hotelling, Relations between two sets of variates, Biometrika, 28 (1936), 321-377.
    [26] I. T. Jolliffe, A note on the use of principal components in regression, J. C. Stat. Soc. C. Appl., 31 (1982), 300-303.
    [27] G. Karypis, V. Kumar, A fast and high quality multilevel scheme for partitioning irregular graphs, SIAM J. Sci. Comput., 20 (1999), 359-392.
    [28] T. Klock, A. Lanteri, S. Vigogna, Estimating multi-index models with response-conditional least squares, Electron. J. Stat., 15 (2021), 589-629.
    [29] S. Kpotufe, k-NN regression adapts to local intrinsic dimension, In: Advances in Neural Information Processing Systems 24 (NIPS 2011), 2011,729-737.
    [30] S. Kpotufe, S. Dasgupta, A tree-based regressor that adapts to intrinsic dimension, J. Comput. Syst. Sci., 78 (2012), 1496-1515.
    [31] S. Kpotufe, V. K. Garg, Adaptivity to local smoothness and dimension in kernel regression, In: Advances in Neural Information Processing Systems 26 (NIPS 2011), 2013, 3075-3083.
    [32] A. Lanteri, M. Maggioni, S. Vigogna, Conditional regression for single-index models, 2020 arXiv: 2002.10008.
    [33] A. B. Lee, R. Izbicki, A spectral series approach to high-dimensional nonparametric regression, Electron. J. Stat., 10 (2016), 423-463.
    [34] W. Liao, M. Maggioni, Adaptive geometric multiscale approximations for intrinsically low-dimensional data, J. Mach. Learn. Res., 20 (2019), 1-63.
    [35] W. Liao, M. Maggioni, S. Vigogna, Learning adaptive multiscale approximations to data and functions near low-dimensional sets, In: IEEE Information Theory Workshop (ITW), 2016,226-230.
    [36] G. Liu, Z. Lin, Y. Yu, Robust subspace segmentation by low-rank representation, In: Proceedings of the 26 th International Conference on Machine Learning, 2010,663-670.
    [37] M. Maggioni, S. Minsker, N. Strawn, Multiscale dictionary learning: Non-asymptotic bounds and robustness, J. Mach. Learn. Res., 17 (2016), 1-51.
    [38] S. Mallat, A wavelet tour of signal processing, 2 Eds., Academic Press, 1999.
    [39] K. Pearson, On lines and planes of closest fit to systems of points in space, Philos. Mag., 2 (1901), 559-572.
    [40] S. T. Roweis, L. K. Saul, Nonlinear dimensionality reduction by locally linear embedding, Science, 290 (2000), 2323-2326.
    [41] I. Steinwart, D. R. Hush, C. Scovel, Optimal rates for regularized least squares regression, In: The 22nd Annual Conference on Learning Theory, 2009.
    [42] A. Szlam, Asymptotic regularity of subdivisions of euclidean domains by iterated PCA and iterated 2-means, Appl. Comput. Harmon. Anal., 27 (2009), 342-350.
    [43] J. B. Tenenbaum, V. D. Silva, J. C. Langford, A global geometric framework for nonlinear dimensionality reduction, Science, 290 (2000), 2319-2323.
    [44] J. A. Tropp, User-friendly tools for random matrices: An introduction, NIPS version, 2012.
    [45] A. B. Tsybakov, Introduction to nonparametric estimation, Springer, 2009.
    [46] R. Vershynin, Introduction to the non-asymptotic analysis of random matrices, In: Compressed sensing, Cambridge University Press, 2012,210-268.
    [47] R. Vidal, Y. Ma, S. Sastry, Generalized principal component analysis (GPCA), IEEE T. Pattern Anal., 27 (2005), 1945-1959.
    [48] G. B. Ye, D. X. Zhou, Learning and approximation by Gaussians on Riemannian manifolds, Adv. Comput. Math., 29 (2008), 291-310. doi: 10.1007/s10444-007-9049-0
    [49] Z. Zhang, H. Zha, Principal manifolds and nonlinear dimension reduction via local tangent space alignment, SIAM J. Sci. Comput., 26 (2002), 313-338.
    [50] X. Zhou, N. Srebro, Error analysis of Laplacian eigenmaps for semi-supervised learning, In: Proceedings of the 14th International Conference on Artificial Intelligence and Statistics, 2011,901-908.
  • This article has been cited by:

    1. Mio Heinrich, Marcus Rosenblatt, Franz-Georg Wieland, Hans Stigter, Jens Timmer, On structural and practical identifiability: Current status and update of results, 2025, 24523100, 100546, 10.1016/j.coisb.2025.100546
  • Reader Comments
  • © 2022 the Author(s), licensee AIMS Press. This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0)
通讯作者: 陈斌, bchen63@163.com
  • 1. 

    沈阳化工大学材料科学与工程学院 沈阳 110142

  1. 本站搜索
  2. 百度学术搜索
  3. 万方数据库搜索
  4. CNKI搜索

Metrics

Article views(2586) PDF downloads(126) Cited by(3)

Figures and Tables

Tables(4)

Other Articles By Authors

/

DownLoad:  Full-Size Img  PowerPoint
Return
Return

Catalog