
In this paper, we proposed a novel binary classification framework named adaptive robust AdaBoost-based kernel-free quadratic surface support vector machine with Universum data (A-R-U-SQSSVM). First, we developed R-U-SQSSVM by integrating the capped L2,p-norm distance metric and the generalized Welsch adaptive loss function to improve the model's robustness and adaptability. Furthermore, we introduced Universum data points into R-U-SQSSVM to enhance the model's generalization performance by incorporating valuable prior knowledge for the classifier. Additionally, we utilized R-U-SQSSVM as a weak classifier and embedded the AdaBoost algorithm within it to obtain a strong classifier, A-R-U-SQSSVM. To effectively solve our model, we transformed it into a quadratic programming problem using the half-quadratic (HQ) optimization algorithm and concave duality. This transformed problem can be solved using convex optimization methods, such as the sequential minimal optimization (SMO) algorithm. Experimental results on University of California, Irvine (UCI) datasets demonstrated the superior classification performance of our method. In large datasets, A-R-U-SQSSVM was hundreds or even a thousand times faster than traditional capped twin support vector machine (CTSVM), SQSSVM.
Citation: Bao Ma, Yanrong Ma, Jun Ma. Adaptive robust AdaBoost-based kernel-free quadratic surface support vector machine with Universum data[J]. AIMS Mathematics, 2025, 10(4): 8036-8065. doi: 10.3934/math.2025369
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In this paper, we proposed a novel binary classification framework named adaptive robust AdaBoost-based kernel-free quadratic surface support vector machine with Universum data (A-R-U-SQSSVM). First, we developed R-U-SQSSVM by integrating the capped L2,p-norm distance metric and the generalized Welsch adaptive loss function to improve the model's robustness and adaptability. Furthermore, we introduced Universum data points into R-U-SQSSVM to enhance the model's generalization performance by incorporating valuable prior knowledge for the classifier. Additionally, we utilized R-U-SQSSVM as a weak classifier and embedded the AdaBoost algorithm within it to obtain a strong classifier, A-R-U-SQSSVM. To effectively solve our model, we transformed it into a quadratic programming problem using the half-quadratic (HQ) optimization algorithm and concave duality. This transformed problem can be solved using convex optimization methods, such as the sequential minimal optimization (SMO) algorithm. Experimental results on University of California, Irvine (UCI) datasets demonstrated the superior classification performance of our method. In large datasets, A-R-U-SQSSVM was hundreds or even a thousand times faster than traditional capped twin support vector machine (CTSVM), SQSSVM.
Our main goal of the present work is to investigate the following semilinear wave equations with damping term and mass term, namely
{utt−Δu+b1(t)ut−b2(t)u=f(u,ut),x∈Ωc,t>0,u(x,0)=εf(x),ut(x,0)=εg(x),x∈Ωc,∂u∂n|∂B1(0)=0 | (1.1) |
and
{utt−Δu+μ1+tut+ν2(1+t)2u=f(u,ut),x∈Ωc,t>0,u(x,0)=εf(x),ut(x,0)=εg(x),x∈Ωc,∂u∂n|∂B1(0)=0, | (1.2) |
where Δ=3∑i=1∂2∂x2i. The coefficients b1(t)∈C([0,∞))∩L1([0,∞)), b2(t)=ν0(1+t)β+1(ν0>0,β>1) are non-negative functions. μ,ν≥0. We set f(u,ut)=|u|p, |ut|p, |ut|p+|u|q in problem (1.1) and f(u,ut)=|ut|p+|u|q in problem (1.2), respectively. The exponents of nonlinear terms satisfy 1<p,q<∞. Let Ω=B1(0)={x∈R3||x|≤1} and Ωc=R3∖B1(0). Ωc and ∂Ωc are smooth and compact. Initial values satisfy f(x),g(x)∈C∞(Ωc) and supp(f(x),g(x))⊂Ωc∩BR(0), where BR(0)={x||x|≤R}, R>2. The small parameter ε>0 describes the size of initial values. ∂u∂n stands for the derivative of external normal direction. It is well known that a solution u has compact support when the initial values have compact supports. As a consequence, we directly suppose that the solution has compact support set.
We briefly review several previous results concerning problem (1.1) with b1(t)=b2(t)=0. It is worth pointing out that the Cauchy problem with f(u,ut)=|u|p asserts the Strauss exponent pc(n) (see [31,40,41,42]), which is the positive root of quadratic equation
r(n,p)=−(n−1)p2+(n+1)p+2=0. |
The Cauchy problem with f(u,ut)=|ut|p admits the Glassey exponent pG(n)=n+1n−1, which has been investigated in [14,19]. Ikeda et al. [15] establish blow-up dynamic and lifespan estimate of solution to the semilinear wave equation and related weakly coupled system by using a framework of test function approach. The Cauchy problem with f(u,ut)=|ut|p+|u|q is discussed in Han et al. [13]. Upper bound lifespan estimate of solution is illustrated by making use of test function method and the Kato lemma.
Recently, many researchers have been devoted to the study of Cauchy problem for semilinear wave equation
{utt−Δu+g(ut)=f(u,ut),x∈Rn,t>0,u(x,0)=εu0(x),ut(x,0)=εu1(x),x∈Rn, | (1.3) |
where f(u,ut)=|u|p,|ut|p,|ut|p+|u|q. Problem (1.3) with damping term g(ut)=ut,μ1+tut,μ(1+t)βut(β>1),(−Δ)δut(δ∈(0,12]),a(x)ut(a(x)∈C(Rn)) and power nonlinear term f(u,ut)=|u|p is considered in [6,9,18,24,27,30,38]. Lai et al. [27] derive upper bound lifespan estimate of solution to problem (1.3) with damping term g(ut)=μ1+tut by exploiting the Kato lemma. Imai et al. [18] investigate problem (1.3) with scale invariant damping in two dimensions. Blow-up result and lifespan estimate of solution are discussed under certain restriction on the constant μ. Applying test function approach and imposing certain integral sign conditions on the initial values, Georgiev et al. [9] illustrate blow-up result of solution to problem (1.3) with g(ut)=ut on the Heisenberg group when 1<p<pF(n). Wakasa et al. [38] consider formation of singularity of solution to problem (1.3) with scattering damping μ(1+t)βut(β>1). Lifespan estimate of solution to the variable coefficient wave equation in the critical case is analyzed by employing rescaled test function method and iteration technique, which has been utilized in [39]. Problem (1.3) with damping term g(ut)=μ(1+t)βut(β>1),μ(1+|x|)βut(β>2),μ(−Δ)σ2ut(μ>0,0<σ≤2) and derivative type nonlinear term f(u,ut)=|ut|p is considered in [7,25,28]. Lai et al. [25] derive upper bound lifespan estimate of solution to problem (1.3) with scattering damping term g(ut)=μ(1+t)βut(β>1) in the sub-critical and critical cases by introducing a bounded multiplier. Lai et al. [28] verify blow-up and lifespan estimate of solutions to problem (1.3) with space dependent damping term g(ut)=μ(1+|x|)βut(β>2) in the case 1<p≤pG(n)=n+1n−1 by utilizing test function method (Ψ=∂tψ=∂t(−η2p′M(t)e−tϕ1(x))). Dao et al. [7] investigate formation of singularity of solution to problem (1.3) with structural damping term g(ut)=μ(−Δ)σ2ut(μ>0,0<σ≤2) and derivative nonlinearity. Problem (1.3) with damping term g(ut)=μ1+tut,μ(1+t)βut(β>1) and combined nonlinearities f(u,ut)=|ut|p+|u|q is illustrated in [12,26,32,33]. Applying the rescaled test function approach and iterative method, Ming et al. [33] establish upper bound lifespan estimate of solution to problem (1.3) with scattering damping and divergence form nonlinearity in the sub-critical and critical cases. Hamouda et al. [12] illustrate influence of scale invariant damping on the formation of singularity of solution. Lifespan estimate of solution is derived by imposing certain assumptions on the parameter μ. Liu and Wang [32] consider blow-up of solution to the semilinear wave equation with combined nonlinearities on asymptotically Euclidean manifolds in the case n=2,μ=0.
Scholars focus widespread attention on the Cauchy problem for semilinear wave equation with damping term and mass term (see detailed illustrations in [1,4,11,17,22,36,37]). Taking advantage of the iteration method, Lai et al. [22] establish blow-up result of solution to the semilinear wave equation with scattering damping term and negative mass term, where the nonlinearity is |u|p. Ikeda et al. [17] investigate lifespan estimate of solution to the semilinear wave equation with damping term, mass term as well as power nonlinearity in the sub-critical and critical cases by utilizing test function approach (ψ(x,t)=ρ(t)ϕ1(x)), which is inspired by [36]. Lai et al. [23] derive upper bound lifespan estimate of solution to the semilinear wave equation with damping term and mass term by employing the Kato lemma and iteration approach. Blow-up phenomenon and lifespan estimate of solution to the semilinear wave equation with scale invariant damping, non-negative mass term and power type of nonlinear term are documented in [36], where the iteration method is performed. Hamouda et al. [11] show blow-up dynamic of solution to the semilinear wave equation with scale invariant damping, mass term and combined nonlinearities. The proof is based on the multiplier technique and solving the ordinary differential inequality. We refer readers to the works in [2,3,5,8,10,16,20,21,29,34,35] for more details.
Enlightened by the works in [11,17,22,24,25,26,36], our interest is to show blow-up results of solutions to problems (1.1) and (1.2) with Neumann boundary conditions on exterior domain in three dimensions. It is worth pointing out that upper bound lifespan estimates of solutions to the Cauchy problem of semilinear wave equation with scattering damping term μ(1+t)βut(μ>0,β>1) and nonlinear terms |u|p, |ut|p, |ut|p+|u|q are discussed in [24,25,26]. Lai et al. [22] derive blow-up and lifespan estimate of solution to the semilinear wave equation with scattering damping and negative mass term by exploiting the test function technique and iterative approach, where the nonlinear term is |u|p. However, there is no related result about blow-up dynamic of solution to problem (1.1). Thus, we extend the Cauchy problem studied in [24,25,26] to problem (1.1) with damping term, negative mass term and Neumann boundary condition on exterior domain in three dimensions. Upper bound lifespan estimate of solution to problem (1.1) is established by making use of a radial symmetry test function ψ(x,t)=e−t1rer with r=√x21+x22+x23 (see Theorems 1.1, 1.3–1.5). The Cauchy problem investigated in [23] is extended to problem (1.1) by utilizing the test function method (ψ(x,t)=e−t1rer) and the Kato lemma (see Theorem 1.2). We derive lifespan estimate of solution to problem (1.1) with f(u,ut)=|u|p (see Theorem 1.6) by taking advantage of the test function approach (ψ1(x,t)=ρ(t)1rer), which is inspired by the work [17]. Making use of a multiplier, Hamouda et al. [11] verify blow-up phenomenon of solution to the semilinear wave equation with scale invariant damping and mass term as well as combined nonlinearities. We extend the problem discussed in [11] to problem (1.2). Upper bound lifespan estimate of solution to problem (1.2) with combined nonlinearities f(u,ut)=|ut|p+|u|q is acquired by applying the test function technique (ψ2(x,t)=ρ1(t)1rer) and iterative method (see Theorem 1.7). To the best of our knowledge, the results in Theorems 1.1–1.7 are new. Moreover, we characterize the variation of wave by utilizing numerical simulation.
Definitions of weak solutions and the main results in this paper are illustrated as follows.
Definition 1.1. A function u is called a weak solution of problem (1.1) on [0,T) if u∈C([0,T),H1(Ωc))∩C1([0,T),L2(Ωc))∩Lploc((0,T)×Ωc) when f(u,ut)=|u|p, u∈C([0,T),H1(Ωc))∩C1([0,T),L2(Ωc))∩C1((0,T),Lp(Ωc)) when f(u,ut)=|ut|p, u∈∩1i=0Ci([0,T),H1−i(Ωc))∩C1((0,T),Lp(Ωc))∩Lqloc((0,T)×Ωc) when f(u,ut)=|ut|p+|u|q and
∫Ωcut(x,t)ϕ(x,t)dx−∫Ωcεg(x)ϕ(x,0)dx+∫t0ds∫Ωc{−ut(x,s)ϕt(x,s)−Δu(x,s)ϕ(x,s)}dx+∫t0ds∫Ωcb1(s)ut(x,s)ϕ(x,s)dx−∫t0ds∫Ωcb2(s)u(x,s)ϕ(x,s)dx=∫t0ds∫Ωcf(u,ut)(x,s)ϕ(x,s)dx, | (1.4) |
where ϕ∈C∞0([0,T)×Ωc) and t∈[0,T).
Definition 1.2. A function u is called a weak solution of problem (1.2) on [0,T) if u∈C([0,T),H1(Ωc))∩C1([0,T),L2(Ωc)), u∈Lqloc((0,T)×Ωc), ut∈Lploc((0,T)×Ωc) when f(u,ut)=|ut|p+|u|q and
∫Ωcut(x,t)ϕ(x,t)dx−∫Ωcut(x,0)ϕ(x,0)dx−∫t0∫Ωcut(x,s)ϕt(x,s)dxds+∫t0∫Ωc∇u(x,s)∇ϕ(x,s)dxds+∫t0∫Ωcμ1+sut(x,s)ϕ(x,s)dxds+∫t0∫Ωcν2(1+s)2u(x,s)ϕ(x,s)dxds=∫t0∫Ωc(|ut(x,s)|p+|u(x,s)|q)ϕ(x,s)dxds, | (1.5) |
where ϕ∈C∞0([0,T)×Ωc) and t∈[0,T).
Setting
m(t)=(1+t)μ, |
we rewrite Definition 1.2 by choosing m(t)ϕ(x,t) as a test function.
Definition 1.3. A function u is called a weak solution of problem (1.2) on [0,T) if u∈C([0,T),H1(Ωc))∩C1([0,T),L2(Ωc)), u∈Lqloc((0,T)×Ωc), ut∈Lploc((0,T)×Ωc) when f(u,ut)=|ut|p+|u|q and
m(t)∫Ωcut(x,t)ϕ(x,t)dx−∫Ωcut(x,0)ϕ(x,0)dx−∫t0m(s)∫Ωcut(x,s)ϕt(x,s)dxds+∫t0m(s)∫Ωc∇u(x,s)∇ϕ(x,s)dxds+∫t0∫Ωcν2m(s)(1+s)2u(x,s)ϕ(x,s)dxds=∫t0m(s)∫Ωc(|ut(x,s)|p+|u(x,s)|q)ϕ(x,s)dxds, | (1.6) |
where ϕ∈C∞0([0,T)×Ωc) and t∈[0,T).
Theorem 1.1. Let 1<p<pc(3). Assume that (f,g)∈H1(Ωc)×L2(Ωc) are non-negative functions and f does not vanish identically. If a solution u to problem (1.1) with f(u,ut)=|u|p satisfies supp(u,ut)⊂{(x,t)∈Ωc×[0,T)||x|≤t+R}, then u blows up in finite time. Moreover, there exists a constant ε0=ε0(f,g,R,p,b1(t),b2(t))>0 such that the lifespan estimate T(ε) satisfies
T(ε)≤Cε−2p(p−1)r(p,3), | (1.7) |
where 0<ε≤ε0, C>0 is independent of ε.
Theorem 1.2. Assume b1(t)=ν1(1+t)β, b2(t)=ν2(1+t)2, ν1≥0, β>1, ν2>0. Let δ=1+4ν2eν11−β>1, d∗(3)=2√2−2∈[0,2), 1<p<pδ(3) and
pδ(3)=max{pF(5−√δ2),pc(3)}={pc(3),√δ≤3−d∗(3),pF(5−√δ2),3−d∗(3)<√δ<5,+∞,√δ≥5. |
Here, pF(n)=1+2n is the solution of equation rF(p,n)=2−n(p−1)=0. Suppose that (f,g)∈H1(Ωc)×L2(Ωc) are non-negative functions and do not vanish identically. If a solution u to problem (1.1) with f(u,ut)=|u|p satisfies supp(u,ut)⊂{(x,t)∈Ωc×[0,T)||x|≤t+R}, then u blows up in finite time. Moreover, the lifespan estimate T(ε) satisfies
T(ε)≤{Cε−2p(p−1)r(p,3),√δ≤1,Cε−(p−1)rF(p,3−1+√δ2),1<√δ<3−d∗(3),1<p≤23−√δ,Cε−2p(p−1)r(p,3),1<√δ<3−d∗(3),23−√δ<p<pδ(3),Cε−(2p−1−3+1+√δ2)−1,√δ≥3−d∗(3), |
where C>0 is independent of ε.
Theorem 1.3. Let 1<p≤pG(3)=2. Assume that (f,g)∈H1(Ωc)×L2(Ωc) are non-negative functions and g does not vanish identically. If the solution u to problem (1.1) with f(u,ut)=|ut|p satisfies supp(u,ut)⊂{(x,t)∈Ωc×[0,T)||x|≤t+R}, then u blows up in finite time. Moreover, the lifespan estimate T(ε) satisfies
T(ε)≤{Cε−p−12−p,1<p<pG(3),exp(Cε−1),p=pG(3), |
where C>0 is independent of ε.
Theorem 1.4. Let p>1 and 1<q<min{1+2p−1,6}. Assume that f and g satisfy the conditions in Theorem 1.3. If a solution u to problem (1.1) with f(u,ut)=|ut|p+|u|q satisfies supp(u,ut)⊂{(x,t)∈Ωc×[0,T)||x|≤t+R}, then u blows up in finite time. Moreover, the lifespan estimate T(ε) satisfies
T(ε)≤Cε−p(q−1)q+1−p(q−1), |
where C>0 is independent of ε.
Theorem 1.5. Let p>3 and 1<q<2. Assume that f and g satisfy the conditions in Theorem 1.3. If the solution u to problem (1.1) with f(u,ut)=|ut|p+|u|q satisfies supp(u,ut)⊂{(x,t)∈Ωc×[0,T)||x|≤t+R}, then u blows up in finite time. Moreover, the lifespan estimate T(ε) satisfies
T(ε)≤Cε−q−12(2−q), |
where C>0 is independent of ε.
Theorem 1.6. Let 1<p<pc(3). Let f and g satisfy the conditions in Theorem 1.1. Suppose that b1(t)∈C1([0,∞)) and r2(t)∈L1([0,∞)) satisfy
{r′2(t)+b1(t)r2(t)−r22(t)=−b2(t),r2(t)|t=0=r2(0). |
ρ′(0) is the initial value of ρ′(t), where ρ(t) is the solution to problem (5.1). It holds that
{g(x)+r2(0)f(x)≥0,g(x)+(b1(0)−ρ′(0))f(x)≥0. |
There is no sign requirement for b1(t) and b2(t). If a solution u to problem (1.1) with f(u,ut)=|u|p satisfies supp(u,ut)⊂{(x,t)∈Ωc×[0,T)||x|≤t+R}, then u blows up in finite time. Moreover, the lifespan estimate T(ε) satisfies
T(ε)≤Cε−2p(p−1)r(p,3), |
where C>0 is independent of ε.
Theorem 1.7. Let p>pG(3+μ), q>qS(3+μ), μ,ν2≥0 and δ=(μ−1)2−4ν2≥0. Assume that λ(p,q,3+μ)<4, where λ(p,q,n)=(q−1)((n−1)p−2)<4. The initial values (f,g)∈H1(Ωc)×L2(Ωc) are non-negative functions which do not vanish identically and satisfy
μ−1−√δ2f(x)+g(x)>0. | (1.8) |
If a solution u to problem (1.2) with f(u,ut)=|ut|p+|u|q satisfies supp(u,ut)⊂{(x,t)∈Ωc×[0,T)||x|≤t+R}, then u blows up in finite time. Moreover, there exists a constant ε0=ε0(f,g,R,p,q,μ,ν)>0 such that the lifespan estimate T(ε) satisfies
T(ε)≤Cε−2p(q−1)4−λ(p,q,3+μ), | (1.9) |
where C>0 is independent of ε.
Remark 1.1. Utilizing the Sobolev embedding theorem yields H1(Ωc)↪Lq(Ωc) when n=3,q<6 in Theorems 1.4 and 1.5. Consequently, the nonlinear term |u|q in problem (1.1) is integrable in the domain Ωc⊂R3.
Remark 1.2. Taking advantage of the Poincare's inequality, we conclude
∫Ωc|∇u|pψdx≥1(t+R)p∫Ωc|u|pψdx≥C∫Ωc|u|pψdx. |
Similar to the proof of Theorem 1.1, we obtain the same result in (1.7) when nonlinear term is f(u,ut)=|∇u|p.
Remark 1.3 We call that u is a global solution of problems (1.1) and (1.2) if the maximal existence time of solution Tmax=∞. While in the case Tmax<∞, we call that u blows up in finite time.
Lemma 2.1. [35] Let b1(t)∈C([0,∞))∩L1([0,∞)) be a non-negative function, which satisfies
m1(t)=exp(−∫∞tb1(τ)dτ), |
m1(0)≤m1(t)≤1, m′1(t)m1(t)=b1(t) for t≥0.
Lemma 2.2. Let ϕ1(x)=ϕ1(r)=1rer, where x=(x1,x2,x3) and r=√x21+x22+x23. It holds that
Δϕ1=(∂rr+2r∂r)ϕ1=ϕ1 |
and ∂ϕ1∂r|r=1=0. Setting ψ=e−tϕ1(x), it satisfies
∫Ωc∩{|x|≤t+R}ψpp−1dx≤C(R+t)2−pp−1,Δψ=ψ, |
where C is a positive constant.
Proof of Lemma 2.2. Direct calculation shows
∂ϕ1∂xi=∂ϕ1∂rxir,∂2ϕ1∂x2i=∂2ϕ1∂r2x2ir2+r2−x2ir3∂ϕ1∂r, |
where i=1,2,3. Thus, we obtain
Δϕ1=∂2ϕ1∂x21+∂2ϕ1∂x22+∂2ϕ1∂x23=∂2ϕ1∂r2(x21r2+x22r2+x23r2)+∂ϕ1∂r(r2−x21r3+r2−x22r3+r2−x23r3)=(∂rr+2r∂r)ϕ1=ϕ1. |
Employing ψ=e−t1rer gives rise to
∫Ωc∩{|x|≤t+R}ψpp−1dx=∫S2dw∫t+R1[e−t1rer]pp−1r2dr≤C∫t+R0[e−(t−r)]pp−1(R+r)2−pp−1dr≤C(R+t)2−pp−1. |
We complete the proof of Lemma 2.2.
Let us set three functions
{F0(t)=∫Ωcu(x,t)dx,F1(t)=∫Ωcu(x,t)ψ(x,t)dx,F2(t)=∫Ωcut(x,t)ψ(x,t)dx, |
where ψ(x,t)=e−tϕ1(x)=e−t1rer. It holds that
◻ψ=0,Δψ=ψ,(ψ)t=−ψ,(ψ)tt=ψ. | (2.1) |
By straightforward computation, we achieve
∫ΩcΔudx=∫∂Ωc1∂u∂ndS−∫Ωc∇1⋅∇udx=0. | (2.2) |
Choosing the test function ϕ(x,s)≡1 on (x,s)∈{Ωc×[0,t]||x|≤s+R} in (1.4) with f(u,ut)=|u|p and utilizing (2.2) yield
F″0(t)+b1(t)F′0(t)=b2(t)F0(t)+∫Ωc|u(x,t)|pdx. | (2.3) |
Multiplying (2.3) with m1(t) and integrating on [0,t], we deduce
F′0(t)≥m1(0)∫t0∫Ωc|u(x,s)|pdxds, | (2.4) |
where we have used the fact F′0(0)≥0 and F0(t)>0.
We are in the position to establish the lower bound of F1(t). Elementary computation leads to
∂u∂n|∂Ωc=∂u∂n|r=1=0,∂ψ∂n|∂Ωc=∂ψ∂n|r=1=0. | (2.5) |
Applying (2.5) and the Green formula yields
∫Ωc(Δuψ−uΔψ)dx=∫∂Ωc(∂u∂nψ−u∂ψ∂n)dS=0. |
Thus, we have
∫ΩcΔuψdx=∫ΩcuΔψdx=∫Ωcuψdx. | (2.6) |
Utilizing (2.1), (2.6) and replacing ϕ(x,s) in (1.4) with f(u,ut)=|u|p by ψ(x,s), we obtain
m1(t)∫Ωcut(x,t)ψ(x,t)dx−m1(0)ε∫Ωcg(x)ψ(x,0)dx−m1(t)∫Ωcu(x,t)ψt(x,t)dx+m1(0)ε∫Ωcf(x)ψt(x,0)dx+∫t0∫Ωcm1(s)b1(s)u(x,s)ψt(x,s)dxds=∫t0∫Ωcm1(s)b2(s)u(x,s)ψ(x,s)dxds+∫t0∫Ωcm1(s)|u(x,s)|pψ(x,s)dxds. |
That is
m1(t){F′1(t)+2F1(t)}=m1(0)ε∫Ωc{f(x)+g(x)}ϕ1(x)dx+∫t0m1(s){b1(s)+b2(s)}F1(s)ds+∫t0∫Ωcm1(s)|u(x,s)|pψ(x,s)dxds, |
which leads to
F′1(t)+2F1(t)≥m1(0)Cf,gε+∫t0m1(s){b1(s)+b2(s)}F1(s)ds, |
where Cf,g=∫Ωc{f(x)+g(x)}ϕ1(x)dx>0.
Thanks to the positivity of F1(t) and F1(0), we deduce
F1(t)>1−e−2t2m1(0)Cf,gε, | (2.7) |
where t>2. Employing (2.4) and the Holder inequality yields
F0(t)>C1m1(0)∫t0ds∫s0(r+R)−3(p−1)Fp0(r)dr. | (2.8) |
Making use of the Holder inequality and Lemma 2.2 gives rise to
∫Ωc|u(x,t)|pdx≥(∫Ωc∩{|x|≤t+R}(ψ(x,t))pp−1dx)−(p−1)|F1(t)|p≥C(t+R)2−p|F1(t)|p. | (2.9) |
Taking advantage of (2.4), (2.7) and (2.9), we acquire
F0(t)>C2εp12(R+t)−pt4. |
We denote
F0(t)>Dj(R+t)−ajtbj, | (2.10) |
where
D1=C2εp12,a1=p,b1=4. | (2.11) |
Combining (2.8) with (2.10), we derive
F0(t)>C1m1(0)Dpj(pbj+2)2(R+t)−3(p−1)−pajtpbj+2. |
Thus, we define the sequences {Dj}j∈N,{aj}j∈N,{bj}j∈N by
Dj+1≥C1m1(0)Dpj(pbj+2)2,aj+1=paj+3(p−1),bj+1=pbj+2. | (2.12) |
Exploiting (2.11), (2.12) and iterative argument gives rise to
aj=pj−1(p+3)−3,bj=pj−1(4+2p−1)−2p−1,Dj≥C3Dpj−1p2(j−1)≥exp{pj−1(logD1−Sp(∞))}, |
where Sp(∞) is obtained by using the d'Alembert's criterion. Moreover, Sp(j)=j−1∑k=12klogp−logC3pk converges to Sp(∞) as j→∞. As a consequence, making use of (2.10) yields
F0(t)≥(t+R)3t−2p−1exp(pj−1J(t)) | (2.13) |
and
J(t)=−(p+3)log(t+R)+(4+2p−1)logt+logD1−Sp(∞)≥log(D1tr(p,3)2(p−1))−C4, |
where C4=(p+3)log2+Sp(∞)>0 and t≥R>2. Utilizing the condition p<pc(3), we arrive at J(t)>1 when t≥C5ε−2p(p−1)r(p,3). Sending j→∞ in (2.13) yields F0(t)→∞. Therefore, we derive the lifespan estimate
T(ε)≤C5ε−2p(p−1)r(p,3). |
The proof of Theorem 1.1 is finished.
Integrating (2.3) on [0,t], we acquire
F0(t)=F0(0)+m1(0)F′0(0)∫t01m1(s)ds+∫t01m1(s)ds∫s0m1(r)b2(r)F0(r)dr+∫t01m1(s)ds∫s0m1(r)dr∫Ωc|u(x,s)|pdx. | (2.14) |
Let us define two functions
˜F0(t)=12F0(0)+m1(0)2F′0(0)t+m1(0)∫t0ds∫s0b2(r)˜F0(r)dr+m1(0)∫t0ds∫s0dr∫Ωc|u(x,r)|pdx | (2.15) |
and
G(t)=(1+t)k+λF0(t). |
Thanks to m1(0)<m1(t)<1 and ν2>0, we achieve
F0(t)−˜F0(t)≥12F0(0)+m1(0)2F′0(0)t+m1(0)∫t0ds∫s0b2(r)[F0(r)−˜F0(r)]dr. |
Applying comparison argument, we conclude F0(t)≥~F0(t). Employing (2.15) and the formula (4.2) with μ1=0,μ2=−m1(0)ν2 in [23] gives rise to
˜F″0(t)−b2(t)m1(0)˜F0(t)=m1(0)∫Ωc|u(x,t)|pdx. | (2.16) |
Similar to the derivation in the proof of Theorem 5 in [23], we derive
˜F0(t)=˜F0(0)(1+t)−k+[k˜F0(0)+˜F′0(0)](1+t)−k∫t0(1+s)−λds+(1+t)−k∫t0(1+s)−λds∫s0(1+r)k+λdr×∫Ωc|u(x,r)|pdx, | (2.17) |
G(t)≳ | (2.18) |
and
\begin{eqnarray} G(t)\gtrsim \varepsilon t^{\lambda}. \end{eqnarray} | (2.19) |
Here, A\gtrsim B means that there exists a positive constant C such that A\geq CB . Taking into account (2.7) and the Holder inequality, we obtain
\begin{eqnarray} \int_{\Omega^{c}}|u(x,t)|^{p}\gtrsim \varepsilon^{p}(t+R)^{2-p}, \end{eqnarray} |
which together with (2.17) results in
\begin{eqnarray} \widetilde{F}_{0}(t)\gtrsim \varepsilon^{p}(1+t)^{-k}\int^{t}_{T_{1}}(1+s)^{-\lambda}ds\int^{s}_{T_{1}}(1+r)^{q+\sqrt{\delta}-1}dr, \end{eqnarray} |
where t\geq T_{1} > 0 , q = -\frac{1+\sqrt{\delta}}{2}-p+4 . Therefore, we arrive at
\begin{equation} G(t)\gtrsim \varepsilon^{p}\left\{ \begin{aligned} & t^{\lambda+q}, \quad q > 0,\\ & t^{\lambda} \ln(1+t), \quad q = 0,\\ & t^{\lambda} , \quad q < 0.\\ \end{aligned} \right. \end{equation} | (2.20) |
Utilizing (2.18)–(2.20) and the Kato lemma in Sub-section 4.3 in [23], we finishes the proof of Theorem 1.2.
Direct computation gives rise to
\begin{eqnarray} &&\; \; \; \; \; \frac{d}{dt}[m_{1}(t)\int_{\Omega^{c}}\{u_{t}(x,t)+u(x,t)\}\psi(x,t)dx]\\ && = b_{1}(t)m_{1}(t)\int_{\Omega^{c}}\{u_{t}(x,t)+u(x,t)\}\psi(x,t)dx +m_{1}(t)\frac{d}{dt}\int_{\Omega^{c}}\{u_{t}(x,t)+u(x,t)\}\psi(x,t)dx. \end{eqnarray} | (3.1) |
Making use of (1.4) and (2.6), we acquire
\begin{eqnarray} &&\; \; \; \; \frac{d}{dt}\int_{\Omega^{c}}\{u_{t}(x,t)+u(x,t)\}\psi(x,t)dx\\ && = \int_{\Omega^{c}}|u_{t}(x,t)|^{p}\psi(x,t)dx-b_{1}(t)\int_{\Omega^{c}}u_{t}(x,t)\psi(x,t)dx \\ &&\; \; \; \; +b_{2}(t)\int_{\Omega^{c}}u(x,t)\psi(x,t)dx. \end{eqnarray} | (3.2) |
Plugging (3.2) into (3.1) yields
\begin{eqnarray} &&\frac{d}{dt}[m_{1}(t)\int_{\Omega^{c}}\{u_{t}(x,t)+u(x,t)\}\psi(x,t)dx]\\ && = b_{1}(t)m_{1}(t)\int_{\Omega^{c}}u(x,t)\psi(x,t)dx +b_{2}(t)m_{1}(t)\int_{\Omega^{c}}u(x,t)\psi(x,t)dx\\ &&\quad+m_{1}(t)\int_{\Omega^{c}}|u_{t}(x,t)|^{p}\psi(x,t)dx, \end{eqnarray} | (3.3) |
which together with (2.7) results in
\begin{eqnarray} &&m_{1}(t)\int_{\Omega^{c}}\{u_{t}(x,t)+u(x,t)\}\psi(x,t)dx\\ &&\geq m_{1}(0)\varepsilon \int_{\Omega^{c}}\{f(x)+g(x)\}\phi_{1}(x)dx\\ &&\quad+\int^{t}_{0}ds\int_{\Omega^{c}}m_{1}(s)|u_{t}(x,s)|^{p}\psi(x,s)dx. \end{eqnarray} | (3.4) |
Combining (1.4), (2.1) and (2.6), we have
\begin{eqnarray} &&\; \; \; \; \frac{d}{dt}[m_{1}(t)\int_{\Omega^{c}}u_{t}(x,t)\psi(x,t)dx] +m_{1}(t)\int_{\Omega^{c}}\{u_{t}(x,t)-u(x,t)\}\psi(x,t)dx\\ && = m_{1}(t)\int_{\Omega^{c}}|u_{t}(x,t)|^{p}\psi(x,t)dx+m_{1}(t)b_{2}(t)\int_{\Omega^{c}}u(x,t)\psi(x,t)dx. \end{eqnarray} | (3.5) |
An application of (3.4) and (3.5) gives rise to
\begin{eqnarray} &&\frac{d}{dt}[m_{1}(t)\int_{\Omega^{c}}u_{t}(x,t)\psi(x,t)dx]+2m_{1}(t)\int_{\Omega^{c}}u_{t}(x,t)\psi(x,t)dx\\ &&\geq m_{1}(0)\varepsilon \int_{\Omega^{c}}\{f(x)+g(x)\}\phi_{1}(x)dx +m_{1}(t)\int_{\Omega^{c}}|u_{t}(x,t)|^{p}\psi(x,t)dx\\ &&\quad+\int^{t}_{0}ds\int_{\Omega^{c}}m_{1}(s)|u_{t}(x,s)|^{p}\psi(x,s)dx. \end{eqnarray} | (3.6) |
We set
\begin{eqnarray} &&G(t) = m_{1}(t)\int_{\Omega^{c}}u_{t}(x,t)\psi(x,t)dx -\frac{m_{1}(0)}{2}\varepsilon\int_{\Omega^{c}}g(x)\phi_{1}(x)dx\\ &&\quad\quad\quad\,-\frac{1}{2}\int^{t}_{0}m_{1}(s)ds\int_{\Omega^{c}}|u_{t}(x,s)|^{p}\psi(x,s)dx, \end{eqnarray} | (3.7) |
where G(0) = \frac{m_{1}(0)\varepsilon}{2}\int_{\Omega^{c}}g(x)\phi_{1}(x)dx > 0 . Taking into account (3.6), we acquire
\begin{eqnarray} G'(t)+2G(t)\geq\frac{m_{1}(t)}{2}\int_{\Omega^{c}}|u_{t}(x,t)|^{p}\psi(x,t)dx+m_{1}(0)\varepsilon\int_{\Omega^{c}} f(x)\phi_{1}(x)dx\geq0. \end{eqnarray} |
It follows that G(t)\geq e^{-2t}G(0) > 0 for t\geq0 . Thus, we conclude
\begin{eqnarray} \int_{\Omega^{c}}u_{t}(x,t)\psi(x,t)dx \geq\frac{m_{1}(0)\varepsilon}{2}\int_{\Omega^{c}}g(x)\phi_{1}(x)dx. \end{eqnarray} | (3.8) |
We define
\begin{eqnarray} H(t) = \frac{1}{2}\int^{t}_{0}m_{1}(s)ds\int_{\Omega^{c}}|u_{t}(x,s)|^{p} \psi(x,s)dx+\frac{m_{1}(0)}{2}\varepsilon\int_{\Omega^{c}}g(x)\phi_{1}(x)dx. \end{eqnarray} |
Applying the Holder inequality and (3.8) yields
\begin{eqnarray} H'(t)\geq \frac{C^{1-p}}{2(R+t)^{p-1}}H^{p}(t). \end{eqnarray} |
As a direct consequence, we have
\begin{eqnarray} -\frac{d}{dt}[H^{-p+1}(t)]\geq \frac{C^{1-p}}{2(R+t)^{p-1}}. \end{eqnarray} |
It is worth noticing that H(0) = \frac{m_{1}(0)}{2}\varepsilon\int_{\Omega^{c}}g(x)\phi_{1}(x)dx . Therefore, employing the assumption 1 < p\leq2 , we derive the lifespan estimate in Theorem 1.3. The proof of Theorem 1.3 is finished.
We are in the position to establish the estimate of F_{0}(t) . Choosing the test function \phi(x, t) = 1 in (1.4) yields
\begin{eqnarray} F''_{0}(t)+b_{1}(t)F'_{0}(t) = \int_{\Omega^{c}}\{|u_{t}(x,t)|^{p}+|u(x,t)|^{q}\}dx+b_{2}(t)F_{0}(t). \end{eqnarray} | (4.1) |
Multiplying (4.1) with m_{1}(t) and integrating on [0, t] yield
\begin{eqnarray} F'_{0}(t)\geq m_{1}(0)\int^{t}_{0}\int_{\Omega^{c}}\{|u_{t}(x,s)|^{p}+|u(x,s)|^{q}\}dxds, \end{eqnarray} | (4.2) |
where we have applied the fact F'_{0}(0)\geq0 and F_{0}(t) > 0 .
Similar to the estimates in (2.7) and (3.8), we obtain the estimates
F_{1}(t)\geq\frac{m_{1}(0)\varepsilon}{2}\int_{\Omega^{c}}f(x)\phi_{1}(x)dx\geq0,\; \; F_{2}(t)\geq\frac{m_{1}(0)\varepsilon}{2}\int_{\Omega^{c}}g(x)\phi_{1}(x)dx\geq0 |
when nonlinear term is f(u, \, u_{t}) = |u_{t}|^{p}+|u|^{q} . Taking advantage of Lemma 2.2 and (3.8), we derive
\begin{eqnarray} \int_{\Omega^{c}}|u_{t}(x,t)|^{p}dx\geq\frac{|F_{2}(t)|^p}{(\int_{\Omega^{c}\cap\{|x|\leq t+R\}}(\psi(x,t))^{\frac{p}{p-1}}dx)^{p-1}}\geq \overline{C}_{1}\varepsilon^p(t+R)^{2-p}, \end{eqnarray} | (4.3) |
where \overline{C}_1 = C^{1-p}(\frac{m_{1}(0)}{2}\int_{\Omega^{c}}g(x)\phi_{1}(x)dx)^p . Plugging (4.3) into (4.2) leads to
\begin{eqnarray} F_{0}(t)\geq m_{1}(0)\overline{C}_{1}\varepsilon^p\int^{t}_{0}\int^{s}_{0}(r+R)^{2-p}drds \geq \overline{C}_{2}\varepsilon^{p}(t+R)^{-p}t^{4}. \end{eqnarray} | (4.4) |
Recalling (4.2), we acquire
\begin{eqnarray} F_{0}(t)\geq \overline{C}_{3}m_{1}(0)\int^{t}_{0}\int^{s}_{0}(r+R)^{-3(q-1)}F_{0}^{q}(r)drds. \end{eqnarray} | (4.5) |
We set
\begin{eqnarray} F_{0}(t)\geq D_{j}(t+R)^{-a_{j}}t^{b_{j}}, \end{eqnarray} | (4.6) |
where
\begin{eqnarray} D_{1} = \overline{C}_{2}\varepsilon^p,\,\; \; a_{1} = p,\,\; \; b_{1} = 4. \end{eqnarray} | (4.7) |
Inserting (4.6) into (4.5), we come to the estimate
\begin{eqnarray} F_{0}(t) \geq\frac{\overline{C}_{3}m_{1}(0)D_{j}^{q}}{(qb_{j}+2)^{2}}(t+R )^{-3(q-1)-qa_{j}}t^{qb_{j}+2}. \end{eqnarray} |
Therefore, we denote the sequences \{D_{j}\}_{j\in\mathbb{N}}, \, \{a_{j}\}_{j\in\mathbb{N}}, \, \{b_{j}\}_{j\in\mathbb{N}} by
\begin{eqnarray} D_{j+1}\geq\frac{\overline{C}_{3}m_{1}(0)D_{j}^{q}}{(qb_{j}+2)^{2}},\; \; \,a_{j+1} = 3(q-1)+qa_{j},\; \; \,b_{j+1} = qb_{j}+2. \end{eqnarray} | (4.8) |
Taking advantage of (4.7), (4.8) and iterative argument gives rise to
\begin{eqnarray} &&a_{j} = q^{j-1}(p+3)-3,\,\; \; b_{j} = q^{j-1}(4+\frac{2}{q-1})-\frac{2}{q-1},\\ &&D_{j}\geq \overline{C}_{4}\frac{D^{q}_{j-1}}{q^{2(j-1)}}\geq \exp\{q^{j-1}(\log D_{1}-S(\infty))\}, \end{eqnarray} |
where S(j) = \sum\limits^{j-1}_{k = 1}\frac{2k\log q-\log \overline{C}_{4}}{q^{k}} converges to S(\infty) as j\rightarrow \infty .
From (4.6), we have
\begin{eqnarray} F_{0}(t)\geq (t+R)^{3}t^{\frac{-2}{q-1}}\exp\{q^{j-1}J(t)\} \end{eqnarray} | (4.9) |
and
\begin{eqnarray} &&J(t)\geq -(p+3)\log(2t)+(4+\frac{2}{q-1})\log t+\log D_{1}-S(\infty)\\ &&\quad\,\,\,\,\, = \log(t^{1+\frac{2}{q-1}-p}D_{1})-\overline{C}_{5}, \end{eqnarray} |
where \overline{C}_{5} = (p+3)\log2+S(\infty) > 0, \, \, t\geq R > 2 . Recalling the assumption q < 1+\frac{2}{p-1} , we deduce that J(t) > 1 when t > \overline{C}_{6}\varepsilon^{\frac{-p(q-1)}{q+1-p(q-1)}} . Sending j\rightarrow \infty in (4.9) yields F_{0}(t)\rightarrow \infty . Therefore, we achieve the lifespan estimate
\begin{eqnarray} T\leq \overline{C}_{7}\varepsilon^{\frac{-p(q-1)}{q+1-p(q-1)}}. \end{eqnarray} |
The proof of Theorem 1.4 is finished.
We set I[f] = \int_{\Omega^{c}}f(x)dx . Utilizing (4.4) gives rise to
\begin{eqnarray} F_{0}(t)\geq C\varepsilon^{p}t^{4-p} \end{eqnarray} |
for sufficiently large t , where C > 0 is independent of \varepsilon . Thus, we deduce that (4.4) is weaker than the linear growth when p > 3 . An application of (4.2) leads to
\begin{eqnarray} F'_{0}(t)\geq\frac{m_{1}(0)}{m_{1}(t)}F'_{0}(0)\geq m_{1}(0)\varepsilon\int_{\Omega^{c}}g(x)dx. \end{eqnarray} | (4.10) |
That is
\begin{eqnarray} F_{0}(t)\geq \overline{C}_{8}\varepsilon t. \end{eqnarray} | (4.11) |
It is deduced from (4.5) and (4.11) that
\begin{eqnarray} F_{0}(t)\geq\overline{C}_{9}\varepsilon^{q}\int^{t}_{0}\int^{s}_{0}(R+r)^{-3(q-1)}r^{q}drds \geq \overline{C}_{10}\varepsilon^{q}(R+t)^{-3(q-1)}t^{q+2}. \end{eqnarray} |
We assume
\begin{eqnarray} F_{0}(t)\geq \overline{D}_j(R+t)^{-\overline{a}_{j}}t^{\overline{b}_{j}}, \end{eqnarray} | (4.12) |
where
\begin{eqnarray} \overline{D}_{1} = \overline{C}_{10}\varepsilon^{q},\; \; \overline{a}_{1} = 3(q-1),\; \; \overline{b}_{1} = q+2. \end{eqnarray} | (4.13) |
Plugging (4.12) into (4.5), we derive
\begin{eqnarray} F_{0}(t)\geq \overline{D}_{j+1}(R+t)^{-q\overline{a}_{j}-3(q-1)}t^{q\overline{b}_{j}+2} \end{eqnarray} | (4.14) |
with
\begin{eqnarray} \overline{D}_{j+1}\geq\frac{\overline{C}_{11}m_{1}(0)\overline{D}^{q}_{j}}{(q\overline{b}_{j}+2)^{2}},\; \; \overline{a}_{j+1} = 3(q-1)+q\overline{a}_{j},\; \; \overline{b}_{j+1} = q\overline{b}_{j}+2. \end{eqnarray} | (4.15) |
Making use of (4.13) and (4.15), we conclude
\begin{eqnarray} \label{1196jgdd} &&\overline{a}_{j} = 3q^{j}-3,\; \; \overline{b}_{j} = q^{j-1}(q+2+\frac{2}{q-1})-\frac{2}{q-1},\\ &&\overline{D}_{j}\geq \overline{C}_{12}\frac{\overline{D}^{q}_{j-1}}{q^{2(j-1)}}\geq\exp\{q^{j-1}(\log \overline{D}_{1}-\overline{S}_{q}(\infty))\}. \end{eqnarray} |
Applying (4.12) gives rise to
\begin{eqnarray} F_{0}(t)\geq(R+t)^{3}t^{-\frac{2}{q-1}}\exp(q^{j-1}\overline{J}(t)) \end{eqnarray} |
and
\begin{eqnarray} \overline{J}(t) = -3q\log(R+t)+(q+2+\frac{2}{q-1})\log t+\log\overline{D}_{1}-\overline{S}_{q}(\infty). \end{eqnarray} |
Bearing in mind 1 < q < 2 , we arrive at the lifespan estimate in Theorem 1.5. This completes the proof of Theorem 1.5.
To outline the proof of Theorem 1.6, we recall the following Lemmas.
Lemma 5.1. [17] Let \rho(t) be a solution of the second order ODE
\begin{equation} \left\{ \begin{aligned} & \rho''(t)- b_{1}(t)\rho'(t)+(-b_{2}(t)-1-b'_{1}(t))\rho(t) = 0,\\ & \rho(0) = 1,\quad \rho(\infty) = 0, \end{aligned} \right. \end{equation} | (5.1) |
where \rho(t) decays as e^{-t} for large t .
Lemma 5.2. Let \phi_1(x) = \phi_1(r) = \frac{1}{r}e^r , where x = (x_1, x_2, x_3) and r = \sqrt{x_1^2+x_2^2+x_3^2} . Setting \psi_{1}(x, t) = \rho(t)\phi_{1}(x) , it holds that
\begin{equation} \begin{aligned} &\int_{\Omega ^c\cap\{|x|\leq t+R\}}(\psi_{1}(x,t))^{\frac{p}{p-1}}dx\leq C (R+t)^{2-\frac{p}{p-1}},\quad\quad \Delta\psi_{1} = \psi_{1}, \end{aligned}\nonumber \end{equation} |
where \rho(t)\sim e^{-t} , C is a positive constant.
Proof of Lemma 5.2. Taking into account \psi_{1} = \rho(t)\frac{1}{r}e^r , we obtain
\begin{equation} \begin{aligned} \int_{\Omega ^c\cap\{|x|\leq t+R\}}(\psi_{1})^{\frac{p}{p-1}}dx = &\int_{\mathbb S^2}dw\int_1^{t+R} [ \rho(t)\frac{1}{r}e^r]^{\frac{p}{p-1}} r^2 dr\\ \leq &C\int_0^{t+R} [ \rho(t-r) ]^{\frac{p}{p-1}} (R+r)^{2-\frac{p}{p-1}}dr \leq C (R+t)^{2-\frac{p}{p-1}}. \end{aligned}\nonumber \end{equation} |
We finish the proof of Lemma 5.2.
Proof of Theorem 1.6. Let us define the functions
\begin{equation} \left\{ \begin{aligned} & F_{0}(t) = \int_{\Omega^{c}}u(x,t)dx,\\ & F_{1}(t) = \int_{\Omega^{c}}u(x,t)\psi_{1}(x,t)dx, \end{aligned}\nonumber \right. \end{equation} |
where \psi_{1}(x, t) = \rho(t)\phi_{1}(x) .
Choosing the test function \phi(x, s)\equiv1 on \{(x, s)\in\Omega^{c}\times[0, t]\big|\, |x|\leq s+R\} in (1.4) with f(u, \, u_{t}) = |u|^{p} , we have
\begin{eqnarray} F''_{0}(t)+b_{1}(t)F'_{0}(t)-b_{2}(t)F_{0}(t) = \int_{\Omega^{c}}|u(x,t)|^{p}dx. \end{eqnarray} | (5.2) |
We rewrite (5.2) into the form
\begin{eqnarray} &&F''_{0}(t)+b_{1}(t)F'_{0}(t)-b_{2}(t)F_{0}(t)\\ && = [F'_{0}(t)+r_{2}(t)F_{0}(t)]'+r_{1}(t)[F'_{0}(t)+r_{2}(t)F_{0}(t)], \end{eqnarray} | (5.3) |
where r_{1}(t) and r_{2}(t) satisfy
\begin{equation} \label {118183} \left\{ \begin{aligned} & r_{1}(t)+r_{2}(t) = b_{1}(t),\\ & r'_{2}(t)+r_{1}(t)r_{2}(t) = -b_{2}(t). \end{aligned}\nonumber \right. \end{equation} |
Multiplying both sides of (5.3) by \exp \int^{s_{1}}_{s_{2}}r_{1}(\tau)d\tau , integrating over [0, s_{2}] and applying g(x)+r_{2}(0)f(x)\geq 0 yield
\begin{eqnarray} F'_{0}(s_{2})+r_{2}(s_{2})F_{0}(s_{2})\geq \int_{0}^{s_{2}}e^{\int_{s_{2}}^{s_{1}}r_{1}(\tau)d\tau}\int_{\Omega^{c}}|u(x,s_{1})|^{p}dxds_{1}. \end{eqnarray} | (5.4) |
Multiplying (5.4) by \exp \int^{s_{2}}_{t}r_{2}(\tau)d\tau leads to
\begin{eqnarray} F_{0}(t)\geq \int^{t}_{0}e^{\int^{s_{2}}_{t}r_{2}(\tau)d\tau}\int_{0}^{s_{2}}e^{\int_{s_{2}}^{s_{1}}r_{1}(\tau)d\tau}\int_{\Omega^{c}}|u(x,s_{1})|^{p}dxds_{1}ds_{2}. \end{eqnarray} | (5.5) |
Replacing \phi(x, s) with \psi_{1}(x, s) in (1.4) in the case f(u, \, u_{t}) = |u|^{p} and employing (2.6), we derive
\begin{eqnarray} &&\int^{t}_{0}\int_{\Omega^{c}}u_{tt}(x,s)\psi_{1}(x,s) dxds-\int^{t}_{0}\int_{\Omega^{c}}u(x,s)\psi_{1}(x,s) dxds\\ &&+\int^{t}_{0}\int_{\Omega^{c}}\partial_{s}(b_{1}(s)\psi_{1}(x,s) u(x,s))-\partial_{s}(b_{1}(s)\psi_{1}(x,s))u(x,s)dxds\\ &&-\int^{t}_{0}\int_{\Omega^{c}}b_{2}(s)\psi_{1}(x,s) u(x,s)dxds\\ && = \int^{t}_{0}\int_{\Omega^{c}}|u(x,s)|^{p}\psi_{1}(x,s)dxds. \end{eqnarray} | (5.6) |
Employing Lemma 5.1 and (5.6), we deduce
\begin{eqnarray} F'_{1}(t)+(b_{1}(t)-2\frac{\rho'(t)}{\rho(t)})F_{1}(t)\geq \varepsilon C_{f,\,g}, \end{eqnarray} | (5.7) |
where C_{f, \, g} = \int_{\Omega^{c}}(g(x)+(b_{1}(0)-\rho'(0))f(x))\phi_{1}(x)dx > 0 .
Multiplying (5.7) with \frac{1}{\rho^{2}(t)}e^{\int^{t}_{0}b_{1}(\tau)d\tau} yields
\begin{eqnarray} F_{1}(t)\geq\varepsilon C_{f,\,g,\,b_{1}(t)}\int^{t}_{0}\frac{\rho^{2}(t)}{\rho^{2}(s)}ds. \end{eqnarray} | (5.8) |
Utilizing Lemma 5.2 gives rise to
\begin{eqnarray} \int_{\Omega^{c}}|u(x,t)|^{p}dx\geq \frac{|F_{1}(t)|^{p}}{(\int_{\Omega^{c}\cap\{|x|\leq t+R\}}(\psi_{1}(x,t))^{\frac{p}{p-1}}dx)^{p-1}} \geq C\varepsilon^{p}\langle t\rangle ^{2-p}, \end{eqnarray} | (5.9) |
where \langle t\rangle = 3+|t| . Taking advantage of (5.5) and Lemma 2.1 in [17] leads to
\begin{eqnarray} F_{0}(t) \geq C_{r_{1},\,r_{2}}\int^{t}_{0}\int^{s_{2}}_{0}F^{p}_{0}(s_{1}) (s_{1}+R)^{3(1-p)}ds_{1}ds_{2}. \end{eqnarray} | (5.10) |
Similar to the iteration argument in Theorem 1.1, we derive the lifespan estimate in Theorem 1.6. The proof of Theorem 1.6 is finished.
Lemma 6.1. [11] Assume that \rho_{1}(t) is solution of
\begin{eqnarray} \frac{d^{2}\rho_{1}(t)}{dt^{2}}-\rho_{1}(t)-\frac{d}{dt}(\frac{\mu}{1+t}\rho_{1}(t))+\frac{\nu^{2}}{(1+t)^{2}}\rho_{1}(t) = 0. \end{eqnarray} | (6.1) |
The expression of \rho_{1}(t) is
\rho_{1}(t) = (1+t)^{\frac{\mu+1}{2}}K_{\frac{\sqrt{\delta}}{2}}(1+t), |
where K_{\xi}(t) = \sqrt{\frac{\pi}{2t}}e^{-t}(1+O(t^{-1})) as t\rightarrow \infty and K'_{\xi}(t) = -K_{\xi+1}(t)+\frac{\xi}{t}K_{\xi}(t) . It holds that
\begin{eqnarray} \frac{\rho'_{1}(t)}{\rho_{1}(t)} = -1+O(t^{-1}), \quad t\rightarrow \infty. \end{eqnarray} | (6.2) |
Let \phi_1(x) = \phi_1(r) = \frac{1}{r}e^r , where x = (x_1, x_2, x_3) and r = \sqrt{x_1^2+x_2^2+x_3^2} . Setting \psi_{2}(x, t) = \rho_{1}(t)\phi_{1}(x) , it holds that
\begin{eqnarray} \partial_{t}^{2}\psi_{2}(x,t)-\Delta \psi_{2}(x,t)-\frac{\partial}{\partial t}(\frac{\mu}{1+t}\psi_{2}(x,t))+\frac{\nu^{2}}{(1+t)^{2}}\psi_{2}(x,t) = 0 \end{eqnarray} | (6.3) |
and
\begin{eqnarray} \int_{\Omega^{c}\cap\{|x|\leq t+R\}}(\psi_{2}(x,t))^{\frac{p}{p-1}} dx\leq C \rho_{1}^{\frac{p}{p-1}}e^{\frac{pt}{p-1}}(t+R)^{2-\frac{p}{p-1}} \end{eqnarray} | (6.4) |
for some positive constant C .
Proof of Lemma 6.1. Applying \psi_{2} = \rho_{1}(t)\frac{1}{r}e^r gives rise to
\begin{equation} \begin{aligned} \int_{\Omega ^c\cap\{|x|\leq t+R\}}(\psi_{2})^{\frac{p}{p-1}}dx = &\int_{\mathbb S^2}dw\int_1^{t+R} [ \rho_{1}(t)\frac{1}{r}e^r]^{\frac{p}{p-1}} r^2 dr\\ \leq& C\int_0^{t+R} [\rho_{1}(t)e^{r} ]^{\frac{p}{p-1}} r^{2-\frac{p}{p-1}}dr\\ \leq& C \rho_{1}^{\frac{p}{p-1}}e^{\frac{pt}{p-1}}(t+R)^{2-\frac{p}{p-1}}. \end{aligned}\nonumber \end{equation} |
We complete the proof of Lemma 6.1.
We denote two functions
\begin{equation} \left\{ \begin{aligned} & G_{1}(t) = \int_{\Omega^{c}}u(x,t)\psi_{2}(x,t)dx,\\ & G_{2}(t) = \int_{\Omega^{c}}u_{t}(x,t)\psi_{2}(x,t)dx.\\ \end{aligned}\nonumber \right. \end{equation} |
Lemma 6.2. Let u be a weak solution of problem (1.2). If (p, \, q) and (f(x), \, g(x)) satisfy the conditions in Theorem 1.7, then there exists T_{0} = T_{0}(\mu, \nu) > 1 such that
\begin{eqnarray} G_{1}(t)\geq C_{G_{1}}\varepsilon, \end{eqnarray} | (6.5) |
where t\geq T_{0} , C_{G_{1}} is a positive constant which depends on f, \, g, \, \mu, \, \nu .
Proof of Lemma 6.2. Replacing \phi(x, t) in (1.5) by \psi_{2}(x, t) = \rho_{1}(t)\phi_{1}(x) and employing (6.3), we derive
\begin{eqnarray} &&\int_{\Omega^{c}}\big(u_{t}(x,t)\psi_{2} (x,t)-u(x,t)\partial_{t}\psi_{2}(x,t)+\frac{\mu}{1+t}u(x,t)\psi_{2}(x,t) \big)dx\\ && = \int_{0}^{t}\int_{\Omega^{c}}\big(|u_{t}(x,s)|^{p}+|u(x,s)|^{q} \big)\psi_{2}(x,s)dxds+\varepsilon C(f,g), \end{eqnarray} | (6.6) |
where
\begin{eqnarray} \label{lz71.2} &&C(f,g) = K_{\frac{\sqrt{\delta}}{2}}(1)\int_{\Omega^{c}}\big(( \frac{\mu-1-\sqrt{\delta}}{2}f(x)+g(x) \big)\phi_{1}(x)dx\\ &&\qquad\quad\quad \,\,+K_{\frac{\sqrt{\delta}}{2}+1}(1)\int_{\Omega^{c}}g(x)\phi_{1}(x)dx > 0. \end{eqnarray} |
Thus, we obtain
\begin{eqnarray} &&G_{1}'(t)+ (\frac{\mu}{1+t}-2\frac{\rho'_{1}(t)}{\rho_{1}(t)})G_{1} (t) \\ && = \int_{0}^{t}\int_{\Omega^{c}}\big(|u_{t}(x,s)|^{p}+|u(x,s)|^{q} \big)\psi_{2}(x,s)dxds+\varepsilon C(f,g). \end{eqnarray} | (6.7) |
Multiplying (6.7) by \frac{1}{\rho_{1}^{2}(t)}(1+t)^{\mu} , integrating over (0, t) and exploiting Lemma 6.1 yield
\begin{eqnarray} &&G_{1}(t)\geq G_{1}(0)\frac{\rho_{1}^{2}(t)}{(1+t)^{\mu}}+\varepsilon C(f,g)\frac{\rho_{1}^{2}(t)}{(1+t)^{\mu}}\int_{0}^{t} \frac{(1+s)^{\mu}}{\rho^{2}_{1}(s)}ds\\ &&\qquad\,\,\geq \varepsilon C(f,g)(1+t)K^{2}_{\frac{\sqrt{\delta}}{2}}(1+t)\int_{\frac{t}{2}}^{t}\frac{1}{K^{2}_{\frac{\sqrt{\delta}}{2}}(1+s)}ds\\ &&\qquad\,\,\geq \frac{\varepsilon}{4}C(f,g)e^{-2t}\int_{\frac{t}{2}}^{t}e^{2s}ds\\ &&\qquad\,\,\geq \frac{\varepsilon}{16}C(f,g) \end{eqnarray} | (6.8) |
for t > T_{0}(\mu, \nu) > 1 , where G_{1}(0) = \varepsilon K_{\frac{\sqrt{\delta}}{2}}(1)\int_{\Omega^{c}}f(x)\phi_{1}(x)dx > 0 . This finishes the proof of Lemma 6.2.
Lemma 6.3. Let u be a weak solution of problem (1.2). If (p, \, q) and (f(x), \, g(x)) satisfy the conditions in Theorem 1.7, it holds that
\begin{eqnarray} G_{2}(t)+C\nu^{2}\big(1+\nu^{\frac{2}{p-1}}e^{\frac{p}{p-1}t}(1+t) \big)\geq0, \end{eqnarray} | (6.9) |
where C is a positive constant which depends on p, \, f, \, g, \, R, \, \varepsilon_{0}, \, \mu but not on \varepsilon, \, \nu .
Proof of Lemma 6.3. We define two functions
\begin{equation} \left\{ \begin{aligned} & F_{1}(t) = \int_{\Omega^{c}}u(x,t)\psi (x,t)dx,\\ & F_{2}(t) = \int_{\Omega^{c}}u_{t}(x,t)\psi (x,t)dx.\\ \end{aligned}\nonumber \right. \end{equation} |
Replacing \phi(x, s) in (1.6) by \psi(x, t) and using the fact F'_{1}(t)+F_{1}(t) = F_{2}(t) lead to
\begin{eqnarray} &&m(t)(F_{1}(t)+F_{2}(t))-\varepsilon C (f,g)+\int_{0}^{t}\frac{\nu^{2}m(s)}{(1+s)^{2}}F_{1}(s)ds\\ && = \int_{0}^{t}m(s)\int_{\Omega^{c}}\big(|u_{t}(x,s)|^{p}+|u(x,s)|^{q} \big)\psi(x,s)dxds +\int_{0}^{t}m'(s)F_{1}(s)ds, \end{eqnarray} | (6.10) |
where C (f, g) = \int_{\Omega^{c}}\big(f(x)+g(x)\big)\phi_{1}(x)dx .
Therefore, we arrive at
\begin{eqnarray} &&\frac{d}{dt}(F_{2}(t)m(t))+2m(t)F_{2}(t)\\ && = m(t)(F_{1}(t)+F_{2}(t))-\frac{\nu^{2}m(t)}{(1+t)^{2}}F_{1}(t)\\ &&\quad+m(t)\int_{\Omega^{c}}\big(|u_{t}(x,t)|^{p}+|u(x,t)|^{q} \big)\psi(x,t)dx. \end{eqnarray} | (6.11) |
Combining (6.8), (6.10) and (6.11), we deduce
\begin{eqnarray} &&\frac{d}{dt}(F_{2}(t)m(t))+2m(t)F_{2}(t)\\ && = \varepsilon C (f,g)+\int_{0}^{t}m(s)\int_{\Omega^{c}}\big(|u_{t}(x,s)|^{p}+|u(x,s)|^{q} \big)\psi(x,s)dxds\\ &&\quad +m(t)\int_{\Omega^{c}}\big(|u_{t}(x,t)|^{p}+|u(x,t)|^{q} \big)\psi(x,t)dx\\ &&\quad +\int_{0}^{t}m'(s)F_{1}(s)ds -\nu^{2}\int_{0}^{t}\frac{m(t)}{(1+s)^{2}}F_{1}(s)ds\\ &&\quad-\nu^{2}\frac{m(t)}{(1+t)^{2}}F_{1}(t)\\ &&\geq \int_{0}^{t}\int_{\Omega^{c}}|u_{t}(x,s)|^{p}\psi(x,s)dxds-C\varepsilon\nu^{2}-C\nu^{2}\int_{0}^{t}e^{s}|F_{2}(s)|ds, \end{eqnarray} | (6.12) |
where C(f, g) = \int_{\Omega^{c}}\big(f(x)+g(x) \big)\phi_{1}(x)dx , we have applied the facts G_{1}(t) = e^{t}\rho_{1}(t)F_{1}(t) , F'_{1}(t)+F_{1}(t) = F_{2}(t) and m(t)\geq1 .
Taking advantage of the Holder inequality and Lemma 2.2 yields
\begin{eqnarray} C\nu^{2}\int_{0}^{t} e^{s}|F_{2}(s)|ds \leq \int_{0}^{t}\int_{\Omega^{c}}|u_{t}(x,s)|^{p}\psi(x,s)dxds+C\nu^{\frac{2p}{p-1}}e^{\frac{p}{p-1}t}(1+t). \end{eqnarray} | (6.13) |
Making use of (6.12) and (6.13), we have
\begin{eqnarray} \frac{d}{dt}\big(e^{2t}F_{2}(t)m(t)\big)+C\nu^{2}e^{2t}+C\nu^{\frac{2p}{p-1}}e^{\frac{3p-2}{p-1}t}(1+t) \geq 0. \end{eqnarray} | (6.14) |
As a consequent, it holds that
\begin{eqnarray} G_{2}(t)+C\nu^{2}e^{t}\rho_{1}(t)(1+t)^{-\mu}+C\nu^{\frac{2p}{p-1}}e^{t}\rho_{1}(t)e^{\frac{p}{p-1}t}(1+t)^{1-\mu}\geq0, \end{eqnarray} | (6.15) |
where we have used G_{2}(t) = e^{t}\rho_{1}(t)F_{2}(t) .
An application of (6.15) and the fact \rho_{1}(t)e^{t}\leq C(1+t)^{\frac{\mu}{2}} gives rise to
\begin{eqnarray} G_{2}(t)+C\nu^{2}\big(1+\nu^{\frac{2}{p-1}}e^{\frac{p}{p-1}t}(1+t) \big)\geq0. \end{eqnarray} | (6.16) |
This ends the proof of Lemma 6.3.
Lemma 6.4. Let u be a weak solution of problem (1.2). If (p, \, q) and (f(x), \, g(x)) satisfy the conditions in Theorem 1.7, then there exists T_{1} > 0 such that
\begin{eqnarray} G_{2}(t)\geq C_{G_{2}}\varepsilon,\quad t\geq T_{1} = -\ln(\varepsilon), \end{eqnarray} | (6.17) |
where C_{G_{2}} is a positive constant which depends on p, \, f, \, g, \, R, \, \varepsilon_{0}, \, \nu, \, \mu .
Proof of Lemma 6.4. Applying (6.7) and the fact G'_{1}(t)-\frac{\rho_{1}'(t)}{\rho_{1}(t)}G_{1}(t) = G_{2}(t) leads to
\begin{eqnarray} &&G_{2}(t)+(\frac{\mu}{1+t}-\frac{\rho'_{1}(t)}{\rho_{1}(t)})G_{1}(t)\\ && = \int_{0}^{t}\int_{\Omega^{c}}\big(|u_{t}(x,s)|^{p}+|u(x,s)|^{q} \big)\psi_{2}(x,s)dxds+\varepsilon C(f,g). \end{eqnarray} | (6.18) |
Taking into account (6.1), (6.2), (6.18) and Lemma 6.2, we derive
\begin{eqnarray} &&G'_{2}(t)+\frac{3}{4}(\frac{\mu}{1+t}-2\frac{\rho'_{1}(t)}{\rho_{1}(t)})G_{2}(t)\\ &&\geq I_{4}(t)+I_{5}(t)+\int_{\Omega^{c}}\big(|u_{t}(x,t)|^{p}+|u(x,t)|^{q} \big)\psi_{2}(x,t)dx\\ &&\geq C\varepsilon, \end{eqnarray} | (6.19) |
where
\begin{eqnarray} \label{lz74.4} &&I_{4}(t) = \big(-\frac{\rho'_{1}(t)}{2\rho_{1}(t)}-\frac{\mu}{4(1+t)} \big)\big( G_{2}(t)+(\frac{\mu}{1+t}-\frac{\rho'_{1}(t)}{\rho_{1}(t)})G_{1}(t)\big)\\ &&\quad\; \; \,\,\geq C\varepsilon+\frac{1}{4}\int_{0}^{t}\int_{\Omega^{c}}\big(|u_{t}(x,s)|^{p}+|u(x,s)|^{q} \big)\psi_{2}(x,s)dxds \end{eqnarray} |
for t > \widetilde{T}_{1}(\mu, \nu)\geq T_{0} ,
\begin{eqnarray} \label{lz74.5} I_{5}(t) = \big(1-\frac{\nu^{2}}{(1+t)^{2}}+(\frac{\rho'_{1}(t)}{2\rho_{1}(t)} +\frac{\mu}{4(1+t)})(\frac{\mu}{1+t} -\frac{\rho'_{1}(t)}{\rho_{1}(t)}) \big)G_{1}(t)\geq0 \end{eqnarray} |
for t > \widetilde{T}_{2}(\mu, \nu)\geq \widetilde{T}_{1}(\mu, \nu) .
Utilizing (6.19) and Lemma 6.3, we conclude
\begin{eqnarray} G_{2}(t)\geq C_{G_{2}}\varepsilon \end{eqnarray} | (6.20) |
for t\geq T_{1} = -\ln\varepsilon . This completes the proof of Lemma 6.4.
We define the function
\begin{eqnarray} F(t) = \int_{\Omega^{c}}u(x,t)dx. \end{eqnarray} | (6.21) |
Choosing the test function \phi(x, t)\equiv 1 in (1.5) yields
\begin{eqnarray} F''(t)+\frac{\mu}{1+t}F'(t)+\frac{\nu^{2}}{(1+t)^{2}}F(t) = \int_{\Omega^{c}}\big(|u_{t}(x,t)|^{p}+|u(x,t)|^{q} \big) dx. \end{eqnarray} | (6.22) |
Therefore, we obtain
\begin{eqnarray} \big(F'(t)+\frac{r_{1}}{1+t}F(t)\big)'+\frac{r_{2}+1}{1+t}\big(F'(t)+\frac{r_{1}}{1+t}F(t) \big) = \int_{\Omega^{c}}\big(|u_{t}(x,t)|^{p}+|u(x,t)|^{q} \big)dx, \end{eqnarray} | (6.23) |
where r_{1} = \frac{\mu-1-\sqrt{\delta}}{2} and r_{2} = \frac{\mu-1+\sqrt{\delta}}{2} are real roots of the quadratic equation r^{2}-(\mu-1)r+\nu^{2} = 0 .
It is deduced from (1.8) and (6.23) that
\begin{eqnarray} F(t)\geq \int_{0}^{t}(\frac{1+\tau}{1+t})^{r_{1}}d\tau \int_{0}^{\tau}(\frac{1+s}{1+\tau})^{r_{2}+1}ds\int_{\Omega^{c}}\big(|u_{t}(x,s)|^{p}+|u(x,s)|^{q} \big)dx. \end{eqnarray} | (6.24) |
Making use of the Holder inequality and (6.24), we acquire
\begin{eqnarray} F(t)\geq C\int_{0}^{t}(\frac{1+\tau}{1+t})^{r_{1}}d\tau\int_{0}^{\tau}(\frac{1+s}{1+\tau})^{r_{2}+1}(1+s)^{-3(q-1)}|F(s)|^{q}ds, \end{eqnarray} | (6.25) |
where C = (means(B_{1}))^{1-q}R^{-3(q-1)} > 0 .
Employing Lemma 6.4, (6.4) and the fact \rho_{1}(t)e^{t}\leq C(1+t)^{\frac{\mu}{2}} gives rise to
\begin{eqnarray} &&\int_{\Omega^{c}}|u_{t}(x,t)|^{p}dx\geq G_{2}^{p}(t)(\int_{\Omega^{c}\cap\{|x|\leq t+R\}}(\psi_{2}(x,t))^{\frac{p}{p-1}}dx)^{-(p-1)}\\ &&\qquad\qquad\quad\; \; \; \,\,\geq \widetilde{C}_{1}\varepsilon^{p}(t+R)^{ -\frac{\mu p+2(p-2)}{2}}. \end{eqnarray} | (6.26) |
Plugging (6.26) into (6.24), we deduce
\begin{eqnarray} &&F(t)\geq \widetilde{C}_{1}\varepsilon^{p} \int_{0}^{t}(\frac{1+\tau}{1+t})^{r_{1}}d\tau \int_{0}^{\tau}(\frac{1+s}{1+\tau})^{r_{2}+1}(s+R)^{-\frac{\mu p+2(p-2)}{2}}ds\\ &&\quad \; \,\,\geq \widetilde{C}_{1}\varepsilon^{p}(1+t)^{-r_{1}}\int_{T_{0}}^{t}(1+\tau)^{r_{1}-r_{2}-1-(2+\mu)\frac{p}{2}}d\tau \int_{T_{0}}^{\tau} (1+s)^{3+r_{2}}ds\\ &&\quad \; \,\,\geq \widetilde{C}_{1}\varepsilon^{p}(1+t)^{-r_{2}-1-(2+\mu)\frac{p}{2}}\int_{T_{0}}^{t}d\tau \int_{T_{0}}^{\tau}(s-T_{0})^{3+r_{2}}ds\\ &&\quad \; \,\, \geq \frac{\widetilde{C}_{1}}{(4+r_{2})(5+r_{2})}\varepsilon^{p}(t+R)^{-r_{2}-1-(2+\mu)\frac{p}{2}}(t-T_{0})^{5+r_{2}} \end{eqnarray} | (6.27) |
for t > T_{0} .
We set
\begin{eqnarray} F(t)\geq D_{j}(t+R)^{-a_{j}}(t-T_{0})^{b_{j}}, \end{eqnarray} | (6.28) |
where
\begin{eqnarray} D_{1} = \frac{\widetilde{C}_{1}}{(4+r_{2})(5+r_{2})} ,\; \; a_{1} = r_{2}+1+(2+\mu)\frac{p}{2},\; \; b_{1} = 5+r_{2}. \end{eqnarray} | (6.29) |
Utilizing (6.25) and (6.28), we have
\begin{eqnarray} &&F(t) \geq CD_{j}^{q}(1+t)^{-r_{2}-1-3(q-1)-qa_{j}}\int_{T_{0}}^{t}\int_{T_{0}}^{\tau}(s-T_{0})^{r_{2}+1+qb_{j}}dsd\tau\\ &&\quad\; \; \,\geq \frac{CD_{j}^{q}}{(r_{2}+qb_{j}+2)(r_{2}+qb_{j}+3)}( t+R)^{-r_{2}-1-3(q-1)-qa_{j}}\\ &&\quad\; \; \,\quad\times (t-T_{0})^{r_{2}+qb_{j}+3}. \end{eqnarray} | (6.30) |
We denote the sequences \{D_{j}\}_{j\in\mathbb{N}} , \{a_{j}\}_{j\in\mathbb{N}} , \{b_{j}\}_{j\in\mathbb{N}} by
\begin{eqnarray} D_{j+1}\geq \frac{CD_{j}^{q}}{(r_{2}+qb_{j}+2)(r_{2}+qb_{j}+3)}, \end{eqnarray} | (6.31) |
\begin{eqnarray} a_{j+1} = r_{2}+1+3(q-1)+qa_{j} ,\; \; \; \; b_{j+1} = r_{2}+qb_{j}+3. \end{eqnarray} | (6.32) |
Taking advantage of (6.29), (6.31) and (6.32) leads to
\begin{eqnarray} a_{j} = q^{j-1}(a_{1}+3+\frac{r_{2}+1}{q-1})-(3+\frac{r_{2}+1}{q-1}), \end{eqnarray} | (6.33) |
\begin{eqnarray} b_{j} = q^{j-1} (b_{1}+\frac{r_{2}+3}{q-1})-\frac{r_{2}+3}{q-1}, \end{eqnarray} | (6.34) |
\begin{eqnarray} D_{j}\geq \exp\{q^{j-1}(\log D_{1}-S_{q}(\infty))\}, \end{eqnarray} | (6.35) |
where S_{q}(j) = \frac{2q\log q}{(q-1)^{2}}-\frac{q\log C}{q-1} converges to S_{q}(\infty) as j\rightarrow \infty .
Employing (6.28), (6.29) and (6.33)–(6.35), we achieve
\begin{eqnarray} F(t)\geq \exp(q^{j-1}J(t))(t+R)^{3+\frac{r_{2}+1}{q-1}}(t-T_{0})^{\frac{r_{2}+3}{q-1}} \end{eqnarray} | (6.36) |
and
\begin{eqnarray} &&J(t) = \log D_{1}-S_{q}(\infty)-\big(a_{1}+3+\frac{r_{2}+1}{q-1} \big)\log (t+R)\\ &&\qquad\quad\,+\big(b_{1}+\frac{r_{2}+3}{q-1} \big)\log(t-T_{0})\\ &&\quad\; \; \geq \log \big(D_{1} (t-T_{0})^{\frac{4-((2+\mu)p-2)(q-1)}{2(q-1)}}\big)-S_{q}(\infty)\\ &&\qquad\quad\,-\big(a_{1}+3+\frac{r_{2}+1}{q-1}\big) \log2 \end{eqnarray} | (6.37) |
for t > 2T_{0}+1 . Recalling p > p_{G}(3+\mu) , q > q_{S}(3+\mu) and \lambda(p, q, 3+\mu) < 4 , we conclude lifespan estimate (1.9) in Theorem 1.7. This completes the proof of Theorem 1.7.
We are in the position to show variation of wave for the Cauchy problem of semilinear wave equation in two dimensions. All codes are written and run with Matlab2014a on Windows 10 (64bite), RAM:8G and CPU 3.60 GHz. That is,
\begin{equation} \left\{ \begin{aligned} & \frac{\partial u}{\partial t} = v,\\ & \frac{\partial v}{\partial t} = (\frac{\partial ^{2}}{\partial x^{2}}+\frac{\partial ^{2}}{\partial y^{2}})u +|u|^{3}, \end{aligned} \right. \end{equation} | (7.1) |
\begin{equation} \; \; \; \; \; \; \; \; \; \; \; \; \; \; \left\{ \begin{aligned} & \frac{\partial u}{\partial t} = v,\\ & \frac{\partial v}{\partial t} = (\frac{\partial ^{2}}{\partial x^{2}}+\frac{\partial ^{2}}{\partial y^{2}})u-u_{t}+u+|u|^{3}. \end{aligned} \right. \end{equation} | (7.2) |
Suppose that the initial values satisfy
u|_{t = 0} = e^{-20[(x-0.4)^{2}+(y+0.4)^{2}]}+e^{-20[(x+0.4)^{2}+(y-0.4)^{2}]},\; \; \frac{\partial u}{\partial t}|_{t = 0} = 0. |
The following two group figures indicate the propagation of wave in two dimensions.
Figure 1 represents the trend of wave from t = 0 s to t = 1 s when nonlinear term is |u|^{3} in problem (7.1). It indicates that there are two peaks of wave when t = 0 s. With the increase of time, two peaks of wave move downward until they disappear. Then, two new wave peaks appear at different positions when t = 0.8 s. From t = 0.8 s to t = 0.9 s, the new wave amplitudes decrease gradually. However, when t = 0.9 s \sim 1 s, the old wave amplitudes increase constantly. When t = 1 s, the wave peaks appear again and the position of wave peaks is same as the position of t = 0 s.
Figure 2 stands for the trend of wave from t = 0 s to t = 2.9 s when nonlinear term is |u|^{3} in problem (7.2). When t = 0 s, it shows the initial state of wave with two peaks. From t = 0 s to t = 0.5 s, the wave peaks continue to drop and begin to stack when they meet. When t = 0.5 s \sim 1 s, two new waves appear at different positions and the amplitude increases continuously to form two new wave peaks. When t = 1 s \sim 1.1 s, the amplitudes of wave decreases gradually. When t = 2.9 s, two wave peaks appears again and the position is same as t = 0 s.
From our observation of the above two groups of figures, we obtain that the frictional damping and negative mass terms have an effect on the wave propagation and wave amplitude.
This article is dedicated to investigating blow-up results and lifespan estimates of solutions to the initial boundary value problems of semilinear wave equations with damping term and mass term as well as Neumann boundary conditions on exterior domain in three dimensions. Our main new contribution is that upper bound lifespan estimates of solutions are related to the Strauss exponent and Glassey exponent. We extend the Cauchy problem investigated in the related papers to problems (1.1) and (1.2) with damping term, mass term and Neumann boundary condition on exterior domain in three dimensions. Applying test function technique ( \psi_{2}(x, t) = \rho_{1}(t)\frac{1}{r}e^{r} with r = \sqrt{x_{1}^{2}+x_{2}^{2}+x_{3}^{2}} ) and iterative approach, upper bound lifespan estimates of solutions to problems (1.1) and (1.2) are deduced (see Theorems 1.1–1.7). In addition, we characterize the variation of wave by employing numerical simulation.
The authors declare they have not used Artificial Intelligence (AI) tools in the creation of this article.
The author Sen Ming would like to express his sincere thanks to Professor Yi Zhou for his guidance and encouragement during the postdoctoral study in Fudan University. The author Sen Ming also would like to express his sincere thanks to Professors Han Yang and Ning-An Lai for their helpful suggestions and discussions. The project is supported by the Fundamental Research Program of Shanxi Province (No. 20210302123021, No. 20210302123045, No. 20210302124657, No. 20210302123182), the Program for the Innovative Talents of Higher Education Institutions of Shanxi Province, the Innovative Research Team of North University of China (No. TD201901), National Natural Science Foundation of China (No. 11601446).
This work has no conflict of interest.
[1] |
Y. Bhattarai, S. Duwal, S. Sharma, R. Talchabhadel, Leveraging machine learning and open-source spatial datasets to enhance flood susceptibility mapping in transboundary river basin, Int. J. Digit. Earth, 17 (2024), 2313857. https://doi.org/10.1080/17538947.2024.2313857 doi: 10.1080/17538947.2024.2313857
![]() |
[2] |
W. Liu, D. Tao, Multiview hessian regularization for image annotation, IEEE T. Image Process., 22 (2013), 2676–2687. https://doi.org/10.1109/TIP.2013.2255302 doi: 10.1109/TIP.2013.2255302
![]() |
[3] |
Q. Yuan, H. Shen, T. Li, Z. Li, S. Li, Y. Jiang, et al., Deep learning in environmental remote sensing: Achievements and challenges, Remote Sens. Environ., 241 (2020), 111716. https://doi.org/10.1016/j.rse.2020.111716 doi: 10.1016/j.rse.2020.111716
![]() |
[4] |
C. Cortes, V. Vapnik, Support-vector networks, Mach. Learn., 20 (1995), 273–297. https://doi.org/10.1007/BF00994018 doi: 10.1007/BF00994018
![]() |
[5] | J. Platt, Sequential minimal optimization: A fast algorithm for training support vector machines, Published by Microsoft, 1998. |
[6] | R. E. Fan, P. H. Chen, C. J. Lin, T. Joachims, Working set selection using second order information for training support vector machines, J. Mach. Learn. Res., 6 (2005). |
[7] |
Y. Wu, Y. Liu, Robust truncated hinge loss support vector machines, J. Am. Stat. Assoc., 102 (2007), 974–983. https://doi.org/10.1198/016214507000000617 doi: 10.1198/016214507000000617
![]() |
[8] |
L. Wang, H. Jia, J. Li, Training robust support vector machine with smooth ramp loss in the primal space, Neurocomputing, 71 (2008), 3020–3025. https://doi.org/10.1016/j.neucom.2007.12.032 doi: 10.1016/j.neucom.2007.12.032
![]() |
[9] |
X. Huang, L. Shi, J. A. Suykens, Support vector machine classifier with pinball loss, IEEE T. Pattern Anal., 36 (2013), 984–997. https://doi.org/10.1109/TPAMI.2013.178 doi: 10.1109/TPAMI.2013.178
![]() |
[10] |
X. Shen, L. Niu, Z. Qi, Y. Tian, Support vector machine classifier with truncated pinball loss, Pattern Recogn., 68 (2017), 199–210. https://doi.org/10.1016/j.patcog.2017.03.011 doi: 10.1016/j.patcog.2017.03.011
![]() |
[11] |
C. Yuan, L. Yang, Capped L_{2, P}-norm metric based robust least squares twin support vector machine for pattern classification, Neural Networks, 142 (2021), 457–478. https://doi.org/10.1016/j.neunet.2021.06.028 doi: 10.1016/j.neunet.2021.06.028
![]() |
[12] |
J. Zhang, Z. Lai, H. Kong, L. Shen, Robust twin bounded support vector classifier with manifold regularization, IEEE T. Cybernetics, 53 (2022), 5135–5150. https://doi.org/10.1109/TCYB.2022.3160013 doi: 10.1109/TCYB.2022.3160013
![]() |
[13] |
J. Ke, C. Gong, T. Liu, L. Zhao, J. Yang, D. Tao, Laplacian Welsch regularization for robust semisupervised learning, IEEE T. Cybernetics, 52 (2020), 164–177. https://doi.org/10.1109/TCYB.2019.2953337 doi: 10.1109/TCYB.2019.2953337
![]() |
[14] |
J. Ma, L. Yang, Q. Sun, Capped L_{1}-norm distance metric-based fast robust twin bounded support vector machine, Neurocomputing, 412 (2020), 295–311. https://doi.org/10.1016/j.neucom.2020.06.053 doi: 10.1016/j.neucom.2020.06.053
![]() |
[15] |
Z. Y. Wang, H. C. So, A. M. Zoubir, Low-rank tensor completion via novel sparsity-inducing regularizers, IEEE T. Signal Process., 72 (2024), 3519–3534. https://doi.org/10.1109/TSP.2024.3424272 doi: 10.1109/TSP.2024.3424272
![]() |
[16] | Z. Y. Wang, H. C. So, Robust matrix completion via novel M-estimator functions, In: 2024 International Conference on Electrical, Computer and Energy Technologies (ICECET), Australia: IEEE, 2024. https://doi.org/10.1109/ICECET61485.2024.10698047 |
[17] |
Z. Y. Wang, H. C. So, A. M. Zoubir, Robust low-rank matrix recovery via hybrid ordinary-Welsch function, IEEE T. Signal Process., 71 (2023), 2548–2563. https://doi.org/10.1109/TSP.2023.3290353 doi: 10.1109/TSP.2023.3290353
![]() |
[18] |
Z. Y. Wang, X. P. Li, H. C. So, Robust matrix completion based on factorization and truncated-quadratic loss function, IEEE T. Circ. Syst. Vid., 33 (2022), 1521–1534. https://doi.org/10.1109/TCSVT.2022.3214583 doi: 10.1109/TCSVT.2022.3214583
![]() |
[19] | A. Eslam, M. G. Abdelfattah, E. S. M. El-Kenawy, H. El-Din Moustafa, Classification of monkeypox using Greylag Goose Optimization (GGO) algorithm, Fusion Pract. Appl., 16 (2024). |
[20] |
E. S. M. El-Kenawy, F. H. Rizk, A. M. Zaki, M. E. Mohamed, A. Ibrahim, A. A. Abdelhamid, et al., Football optimization algorithm (fboa): A novel metaheuristic inspired by team strategy dynamics, J. Artif. Intell. Metaheuristics, 8 (2024), 21–38. https://doi.org/10.54216/JAIM.080103 doi: 10.54216/JAIM.080103
![]() |
[21] | V. Vijayalakshmi, M. S. Babu, R. P. Lakshmi, Kfcm algorithm for effective brain stroke detection through SVM classifier, In: 2018 IEEE International Conference on System, Computation, Automation and Networking (ICSCA), India: IEEE, 2018. https://doi.org/10.1109/ICSCAN.2018.8541179 |
[22] |
A. Kumar, G. Sharma, R. Pareek, S. Sharma, P. Dadheech, M. K. Gupta, Performance optimisation of face recognition based on LBP with SVM and random forest classifier, Int. J. Biometrics, 15 (2023), 389–408. https://doi.org/10.1504/IJBM.2023.130644 doi: 10.1504/IJBM.2023.130644
![]() |
[23] | V. Vapnik, Estimation of dependences based on empirical data, Springer Science and Business Media, 2006. |
[24] | J. Weston, R. Collobert, F. Sinz, L. Bottou, V. Vapnik, Inference with the universum, In: Proceedings of the 23rd international conference on Machine learning, 2006, 1009–1016. https://doi.org/10.1145/1143844.1143971 |
[25] |
C. Shen, P. Wang, F. Shen, H. Wang, U Boost: Boosting with the Universum, IEEE T. Pattern Anal., 34 (2011), 825–832. https://doi.org/10.1109/TPAMI.2011.240 doi: 10.1109/TPAMI.2011.240
![]() |
[26] | X. Yan, H. Zhu, A kernel-free fuzzy support vector machine with Universum, J. Ind. Manag. Optim., 19 (2023). https://doi.org/10.3934/jimo.2021184 |
[27] |
H. Moosaei, A. Mousavi, M. Hladík, Z. Gao, Sparse L_{1}-norm quadratic surface support vector machine with Universum data, Soft Comput., 27 (2023), 5567–5586. https://doi.org/10.1007/s00500-023-07860-3 doi: 10.1007/s00500-023-07860-3
![]() |
[28] | X. Bai, V. Cherkassky, Gender classification of human faces using inference through contradictions, In: 2008 IEEE International Joint Conference on Neural Networks (IEEE World Congress on Computational Intelligence), Hong Kong: IEEE, 2008,746–750. https://doi.org/10.1109/IJCNN.2008.4633879 |
[29] |
C. L. Liu, W. H. Hsaio, C. H. Lee, T. H. Chang, T. H. Kuo, Semi-supervised text classification with universum learning, IEEE T. Cybernetics, 46 (2015), 462–473. https://doi.org/10.1109/TCYB.2015.2403573 doi: 10.1109/TCYB.2015.2403573
![]() |
[30] |
B. Richhariya, M. Tanveer, A. H. Rashid, Alzheimer's disease neuroimaging initiative, diagnosis of Alzheimer's disease using universum support vector machine based recursive feature elimination (USVM-RFE), Biomed. Signal Proces., 59 (2020), 101903. https://doi.org/10.1016/j.bspc.2020.101903 doi: 10.1016/j.bspc.2020.101903
![]() |
[31] |
I. Dagher, Quadratic kernel-free non-linear support vector machine, J. Global Optim., 41 (2008), 15–30. https://doi.org/10.1007/s10898-007-9162-0 doi: 10.1007/s10898-007-9162-0
![]() |
[32] |
J. Luo, S. C. Fang, Z. Deng, X. Guo, Soft quadratic surface support vector machine for binary classification, Asia Pac. J. Oper. Res., 33 (2016), 1650046. https://doi.org/10.1142/S0217595916500469 doi: 10.1142/S0217595916500469
![]() |
[33] |
Y. Bai, X. Han, T. Chen, H. Yu, Quadratic kernel-free least squares support vector machine for target diseases classification, J. Comb. Optim., 30 (2015), 850–870. https://doi.org/10.1007/s10878-015-9848-z doi: 10.1007/s10878-015-9848-z
![]() |
[34] |
X. Yan, H. Zhu, A novel robust support vector machine classifier with feature mapping, Knowl.-Based Syst., 257 (2022), 109928. https://doi.org/10.1016/j.knosys.2022.109928 doi: 10.1016/j.knosys.2022.109928
![]() |
[35] |
Z. Gao, S. C. Fang, J. Luo, N. Medhin, A kernel-free double well potential support vector machine with applications, Eur. J. Oper. Res., 290 (2021), 248–262. https://doi.org/10.1016/j.ejor.2020.10.040 doi: 10.1016/j.ejor.2020.10.040
![]() |
[36] |
Z. Y. Chen, Z. P. Fan, M. Sun, A multi-kernel support tensor machine for classification with multitype multiway data and an application to cross-selling recommendations, Eur. J. Oper. Res., 255 (2016), 110–120. https://doi.org/10.1016/j.ejor.2016.05.020 doi: 10.1016/j.ejor.2016.05.020
![]() |
[37] |
J. Zhou, Y. Tian, J. Luo, Q. Zhai, A kernel-free Laplacian quadratic surface optimal margin distribution machine with application to credit risk assessment, Appl. Soft Comput., 133 (2023), 109931. https://doi.org/10.1016/j.asoc.2022.109931 doi: 10.1016/j.asoc.2022.109931
![]() |
[38] |
Z. Gao, Y. Wang, M. Huang, J. Luo, S. Tang, A kernel-free fuzzy reduced quadratic surface v-support vector machine with applications, Appl. Soft Comput., 127 (2022), 109390. https://doi.org/10.1016/j.asoc.2022.109390 doi: 10.1016/j.asoc.2022.109390
![]() |
[39] |
J. R. Magnus, H. Neudecker, The elimination matrix: Some lemmas and applications, SIAM J. Algebr. Discrete Meth., 1 (1980), 422–449. https://doi.org/10.1137/0601049 doi: 10.1137/0601049
![]() |
[40] |
Z. Qi, Y. Tian, Y. Shi, Twin support vector machine with universum data, Neural Networks, 36 (2012), 112–119. https://doi.org/10.1016/j.neunet.2012.09.004 doi: 10.1016/j.neunet.2012.09.004
![]() |
[41] | S. Dhar, V. Cherkassky, Cost-sensitive Universum-svm, In: 2012 11th International Conference on Machine Learning and Applications, USA: IEEE, 2012. https://doi.org/10.1109/ICMLA.2012.45 |
[42] | V. Cherkassky, F. M. Mulier, Learning from data: Concepts, theory, and methods, John Wiley and Sons, 2007. https://doi.org/10.1002/9780470140529 |
[43] | R. He, B. Hu, X. Yuan, L. Wang, M-estimators and half-quadratic minimization, Robust Recognition via Information Theoretic Learning, Springer, Cham, 2014, 3–11. https://doi.org/10.1007/978-3-319-07416-0_2 |
[44] |
B. Ma, J. Ma, G. Yu, A novel robust metric distance optimization-driven manifold learning framework for semi-supervised pattern classification, Axioms, 12 (2023), 737. https://doi.org/10.3390/axioms12080737 doi: 10.3390/axioms12080737
![]() |
[45] | T. Zhang, Analysis of multi-stage convex relaxation for sparse regularization, J. Mach. Learn. Res., 11 (2010). |
[46] |
R. E. Schapire, The strength of weak learnability, Mach. Learn., 5 (1990), 197–227. https://doi.org/10.1007/BF00116037 doi: 10.1007/BF00116037
![]() |
[47] | Y. Freund, R. E. Schapire, A desicion-theoretic generalization of on-line learning and an application to boosting, In: European conference on computational learning theory, Berlin, Heidelberg: Springer, 1995, 23–37. https://doi.org/10.1007/3-540-59119-2_166 |
[48] |
J. Wang, P. Li, R. Ran, Y. Che, Y. Zhou, A short-term photovoltaic power prediction model based on the gradient boost decision tree, Appl. Sci., 8 (2018), 689. https://doi.org/10.3390/app8050689 doi: 10.3390/app8050689
![]() |
[49] | Z. Chen, F. Jiang, Y. Cheng, X. Gu, W. Liu, J. Peng, XGBoost classifier for DDoS attack detection and analysis in SDN-based cloud, In: 2018 IEEE international conference on big data and smart computing (bigcomp), IEEE, 2018,251–256. |
[50] |
J. Xu, Q. Wu, J. Zhang, Z. Tang, Exploiting Universum data in AdaBoost using gradient descent, Image Vision Comput., 32 (2014), 550–557. https://doi.org/10.1016/j.imavis.2014.04.009 doi: 10.1016/j.imavis.2014.04.009
![]() |
[51] |
C. L. Liu, W. H. Hsaio, C. H. Lee, T. H. Chang, T. H. Kuo, Semi-supervised text classification with universum learning, IEEE T. Cybernetics, 46 (2015), 462–473. https://doi.org/10.1109/TCYB.2015.2403573 doi: 10.1109/TCYB.2015.2403573
![]() |
[52] |
B. Liu, R. Huang, Y. Xiao, J. Liu, K. Wang, L. Li, et al., Adaptive robust Adaboost-based twin support vector machine with universum data, Inform. Sciences, 609 (2022), 1334–1352. https://doi.org/10.1016/j.ins.2022.07.155 doi: 10.1016/j.ins.2022.07.155
![]() |
[53] |
J. Ma, G. Yu, W. Xiong, X. Zhu, Safe semi-supervised learning for pattern classification, Eng. Appl. Artif. Intel., 121 (2023), 106021. https://doi.org/10.1016/j.engappai.2023.106021 doi: 10.1016/j.engappai.2023.106021
![]() |
[54] |
L. Li, Q. Hu, X. Wu, D. Yu, Exploration of classification confidence in ensemble learning, Pattern Recogn., 47 (2014), 3120–3131. https://doi.org/10.1016/j.patcog.2014.03.021 doi: 10.1016/j.patcog.2014.03.021
![]() |
[55] |
I. E. Livieris, L. Iliadis, P. Pintelas, On ensemble techniques of weight-constrained neural networks, Evol. Syst., 12 (2021), 155–167. https://doi.org/10.1007/s12530-019-09324-2 doi: 10.1007/s12530-019-09324-2
![]() |
[56] | J. Demi\check{s}ar, Statistical comparisons of classifiers over multiple data sets, J. Mach. Learn. Res., 7 (2006), 1–30. |