Research article Special Issues

Cumulative entropy properties of consecutive systems

  • We investigated certain properties of cumulative entropy related to the lifetime of consecutive k-out-of-n:F systems. First, we presented a technique to compute the cumulative entropy of the lifetimes of these systems and studied their preservation properties using the established stochastic orders. Furthermore, we derived valuable bounds applicable in cases where the distribution function of component lifetimes is complex or when systems consist of numerous components. To facilitate practical applications, we introduced two nonparametric estimators for the cumulative entropy of these systems. The efficiency and reliability of these estimators were demonstrated using simulated analysis and subsequently validated using real data sets.

    Citation: Mashael A. Alshehri, Mohamed Kayid. Cumulative entropy properties of consecutive systems[J]. AIMS Mathematics, 2024, 9(11): 31770-31789. doi: 10.3934/math.20241527

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  • We investigated certain properties of cumulative entropy related to the lifetime of consecutive k-out-of-n:F systems. First, we presented a technique to compute the cumulative entropy of the lifetimes of these systems and studied their preservation properties using the established stochastic orders. Furthermore, we derived valuable bounds applicable in cases where the distribution function of component lifetimes is complex or when systems consist of numerous components. To facilitate practical applications, we introduced two nonparametric estimators for the cumulative entropy of these systems. The efficiency and reliability of these estimators were demonstrated using simulated analysis and subsequently validated using real data sets.



    This study investigates a specific variational inequality problem described by

    {min{Lu,uu0}=0,(x,t)ΩT,u(x,0)=u0(x),xΩ,u(x,t)=uν=0,(x,t)Ω×(0,T) (1)

    with a non-divergence parabolic operator

    Lu=utudiv(|u|p2u)γu|u|p. (2)

    Here, Ω denotes a bounded and open subset of Rn. We consider the case where p2, γ(0,1) and T>0 are positive constants, ΩT=Ω×(0,T), and u0 satisfies

    u0C(ˉΩ)W1,p0(Ω).

    From (1), we can infer that u>u0 and Lu0 in ΩT. It is easily observed that when u>u0, then Lu=0 in ΩT; conversely, when u=u0, it follows that Lu0. Therefore, in some literature [1,2,3], the variational inequality (1) is often stated in the following manner:

    {Lu0,(x,t)ΩT,uu0,(x,t)ΩT,Lu×(uu0)=0,(x,t)ΩT,u(0,x)=u0(x),xΩ,u(t,x)=uν=0,(x,t)Ω×(0,T).

    It is evident that the above formulation is not as concise as the model presented in (1). This paper adopts the statement of the model in (1) for clarity and simplicity.

    The study of variational inequality problems of the form (1) originated from the pricing problems of American contingent claims with the inclusion of early exercise provisions [1]. The inclusion of early exercise provisions results in a variational inequality model that is characterized by Eq (1). Further studies on this aspect can be found in [2,3], and necessary explanations are provided in Section 2. Therefore, we will not repeat them here.

    In recent years, there has been an increasing amount of theoretical research on variational inequalities under the framework of linear and quasilinear parabolic operators. In [4], the solvability and regularity of quasilinear parabolic obstacle problems were studied using a symmetric dual-wind discontinuous Galerkin (DG) method. Reference [5] investigated a new class of constrained abstract evolutionary variational inequalities in three-dimensional space. By utilizing mathematical analysis of the unsteady Oseen model for generalized Newtonian incompressible fluids, sufficient conditions for the existence of weak solutions were obtained. Reference [6] focused on studying the existence and stability of weak solutions to variational inequalities under fuzzy parameters. By introducing two parameters into the mappings and constraint sets involved, [6] established the existence results for weak solutions of parameter fuzzy fractional differential variational inequalities (PFFDVI) and further analyzed the compactness and continuous dependence on the initial values of PFFDVI. For more results on the existence of solutions, please refer to [7,8,9].

    There have been some novel results in theoretical research on variational inequalities as well. Reference [10] established the local upper bounds, Harnack inequalities, and Hölder continuity up to the boundary for solutions of variational equations defined by degenerate elliptic operators. Studies on the Hölder continuity of solutions to variational inequalities under parabolic operator structures and other regularity results can be found in [11,12]. Reference [13] applied regularization and penalization operator methods to prove the existence of solutions to nonlinear degenerate pseudo-parabolic variational inequalities defined in regions with microstructures, and derived a priori estimates for solutions to the microscale problem.

    Inspired by [10,11,12], this study investigates the inverse Hölder estimate for solutions of the variational inequality (1), which has not yet been addressed in the literature. First, we define the integral mean operator I(t) on a spherical region and analyze its uniform continuity with respect to the time variable. Second, using the integral mean operator I(t) and other inequality amplification techniques, we obtain a Sobolev estimate for the variational inequality (1). Then, by combining the Caccioppoli inequality for the variational inequality (1), we derive the inverse Hölder estimate for the gradient of solutions, which allows us to estimate the higher-order norms of the gradient of solutions using lower-order Lp norms. Such results play a key role in many regularity studies.

    The valuation of American options ultimately boils down to a well-posed problem of variational inequality similar to Eq (1). An American call option gives the investor the right to purchase an underlying asset at a predetermined price K at any time within the investment horizon [0,T]. It is known that the value of an American option on the maturity date T is given by

    C(S,T)=max{SK,0}.

    American options only grant the investor the right to exercise their option within the investment horizon [0,T] without imposing any obligations. Thus, we have

    C(S,t)max{SK,0},t[0,T].

    If the value of the option C(S,t) at time t exceeds max{SK,0}, the investor may consider exercising the option, thereby forfeiting the opportunity for higher past returns. In this case, it follows that

    L1CΔ=tC+12σ2S2SSC+rSSCrC=0 (3)

    as stated in [1]. Here, σ represents the volatility of the underlying asset linked to the American option, and r denotes the risk-free rate of return in the financial market. On the other hand, if the value C(S,t) of the option at time t is max{SK,0}, the investor can retain the option to capture higher returns, leading to

    L1C0 (4)

    as indicated in [1].

    This is a backward differential inequality. Let us define τ=Tt and x=lnS. By combining Eqs (3) and (4), we have

    {min{L2C,Cmax{exK,0}}=0,(x,t)(0,B)×(0,T),C(x,0)=max{exK,0},x(0,B),C(0,t)=u(B,t)=0,t(0,T), (5)

    where

    L2C=τC+12σ2xxC+(r12σ2)xCrC.

    It is worth noting for the reader that x is a one-dimensional variable here, as in this financial example, the American option is linked to only one risky asset. Model (5) represents a specific financial case of the main problem studied in model (1), and thus, in model (1), we set x as an n-dimensional variable. Additionally, the variational inequality suitable for American options shows a high degree of structural similarity with Eq (1).

    On the other hand, transaction costs are often associated with the exercise of options, which necessitates a modification of the volatility σ. For instance, Pars and Avellaneda provided a transaction cost model where the volatility σ satisfies [13]

    σ2=σ02(1+ψsign(x(|xC|p2xC))).

    Here, σ02 represents the long-term volatility level, and the constant ψ is determined by the trading frequency and cost ratio. This adjustment is also consistent with the parabolic operator structure of model (1).

    Lastly, the spatial gradient of solutions to the variational inequality (5) applicable to American options not only measures the change in option value with respect to the underlying asset, but it also allows Black and Scholes to construct a risk-free portfolio to hedge against risk.

    Based on this, we examine more general cases than variational inequality (5). This article mainly analyzes the Sobolev estimation of the solution to variational inequality (1) and the inverse Hölder estimation of the spatial gradient. Before that, let us give a few useful symbols.

    Expanding upon this framework, we broaden our analysis to encompass wider scenarios than those addressed in variational inequality (5). The main objective of this paper is to conduct a thorough investigation into the Sobolev estimation for solving variational inequality (1), along with exploring the inverse Hölder estimation of the spatial gradient. Before delving into these aspects, we introduce a set of relevant mathematical symbols. For a given non-negative constant ρ, and any (x0,t0)ΩT, we define

    Dρ=Dρ(x0)={yRn:|yx0|<ρ}

    to denote the ball in space Rn that is also contained within the bounded region Ω. Similarly, we use

    Qρ,s=Qρ,s(x0,t0)=Dρ(x0)×(t0s,t0+s)

    to denote the cylinder in space Rn+1 that is also contained within ΩT. Lastly, let |Dρ| represent the Lebesgue measure of the set Dρ in space Rn, then the averaging operator of u on Dρ is defined as Iρ(t), given by

    Iρ(t)=Dρudx=1|Dρ|Dρudx.

    With the help of the maximal monotone operator,

    ξ(x)={0,x>0,M0,x=0,

    References [8,11,12] analyzed the existence of generalized solutions, whose definition is as follows:

    Definition 2.1 A pair (u,ξ) is defined as a generalized solution of the variational inequality (1) if it satisfies the following conditions:

    (a) uL(0,T,W1,p(Ω)) and tuL(0,T,L2(Ω)),

    (b) u(x,t)u0(x),u(x,0)=u0(x)forany(x,t)ΩT,

    (c) For every test function φC1(ˉΩT) and for each t[0,T], the following equality holds:

    Ωttuφ+u|u|p2uφdxdt+(1γ)Ωt|u|pφdxdt=Ωtξφdxdt.

    In order to ensure the solvability of the problem, we still impose the restriction γ(0,1) in this study.

    Lemma 2.2 The solution u of variational inequality (1) is uniformly bounded in ΩT. That is, for any (x,t)ΩT, there exists a constant ϑ0, independent of (x,t), such that

    |u|ϑ0.

    Proof Note that from (1), we obtain Lu0 for any (x,t)ΩT. By multiplying both sides of Lu0 by ϕ=(uϑ0)+, we have

    Ωt(uϑ0)(uϑ0)+dx+Ωu|(uϑ0)|p2(uϑ0)(uϑ0)+dx+(1γ)Ωu|(uϑ0)|p(uϑ0)+dx0. (6)

    Note that when uϑ0, t(uϑ0)+=0 and (uϑ0)+=0; when uϑ0, t(uϑ0)+=tu and (uϑ0)+=u, thus

    Ωu|(uϑ0)|p2(uϑ0)(uϑ0)+dx=Ωu|(uϑ0)|pdx0. (7)

    By further removing the non-negative terms Ωu|(uϑ0)|pdx and Ωu|(uϑ0)|p(uϑ0)+dx from (6), we obtain

    Ωt(uϑ0)2+dx0.

    Clearly, when ϑ0 is sufficiently large, Ω(u0ϑ0)2+dx=0 holds. Therefore, for any t(0,T),

    12Ω(uϑ0)2+dx0.

    This demonstrates uϑ0a.e.inΩT □.

    Note that from (1), it is easy to see that Lu0 in Q2ρ,2s. By choosing the test function ϕ=ψ2(uλ)+, and then integrating ϕLu0 over Q2ρ,2s,

    Q2ρ,2stuudxdt+Q2ρ,2s|u|p2uudxdt+(1γ)Q2ρ,2su|u|pdxdt0.

    From Eq (13) in [11] or Theorem 2.1 in [12], it is easy to see that when γ(0,1), for any t(0,T),

    uLp(Ω). (8)

    This section examines the Sobolev estimates for the solution u. A Sobolev estimate on a local spherical region Qρ,s(x,t) is constructed using the lower-order Wp1 norm of the solution u. Initially, we investigate the time continuity results of an operator Iρ(t).

    Lemma 3.1 For any Q4ρ,sΩT, there exists a constant C, which depends solely on p and ϑ0, such that

    |Iρ(t1)Iρ(t2)|CsρQ2ρ,s|u|p1dxdt.

    Proof Assume t1<t2, choose η sufficiently small, and suppose the function ψ1C0(t1η,t2+η) satisfies

    ψ1=1in(t1,t2)and0ψ11in(t1η,t2+η).

    By applying integration by parts, it is easy to obtain

    Ωudiv(|u|p2u)dx=Ωu|u|p2udxΩ|u|pdx.

    Furthermore, due to |Dˉρ|×(Iˉρ(t1)Iˉρ(t2))=Dˉρ×(t1,t2)tudxdt, we have

    |Dˉρ|×(Iˉρ(t1)Iˉρ(t2))Dˉρ×(t1,t2)u|u|p2udxdt(1γ)Dˉρ×(t1,t2)|u|pdxdt. (9)

    Considering (1γ)Dˉρ×(t1,t2)|u|pdxdt is non-negative, by Lemma 2.2, we obtain

    |Dˉρ|×(Iˉρ(t1)Iˉρ(t2))ϑ0Dˉρ×(t1,t2)|u|p2udxdt. (10)

    Here, we choose ˉρ to satisfy ρ<ˉρ<2ρ. On the other hand, according to [14, p. 5, line 3] and Lemma 4.4 of [14], there exists a constant C that depends only on n and p such that

    Dˉρ×(t1,t2)|u|p1dxdtCρD2ρ×(t1,t2)|u|p1dxdt. (11)

    By combining (10) and (11) and substituting the result into (9), the proposition is established. □

    Theorem 3.1 Assume u is a solution to the variational inequality (1). If uLα(τ2s,τ+2s; W1,p(D2ρ(z))), then for any α(1,), we have

    Qρ,s|u(x,t)Iρ(t)|α(1+2/n)dxdtC(Qρ,s|u|αdxdt)(esssupt(τ2s,τ+2s)D2ρ|uIˆρ(t)|2dx)αn.

    Proof By selecting τ2s<t<τ+2s and 1<α<, we analyze the Sobolev-type estimates for the solution to the variational inequality problem (1) under the condition

    uLα(τ2s,τ+2s;W1,p(D2ρ(z))).

    Define

    v(x,t)=|u(x,t)Iρ(t)|ψ(x,t), (12)

    where ψ(x,t) is a cut-off function on Q2ρ,2s,

    ψ(x,t)=1inQρ,s,0ψ(x,t)1inQ2ρ,2s,|ψ(x,t)|C1ρ. (13)

    Evidently, when t(τs,τs), for any xD2ρ(z), we have ψ(x,τ)=0. When t(τs,τs), for any xD2ρ(z), we have

    ψ(x,t)0and|tψ(x,t)|Cs (14)

    For convenience, let ˆρ=2ρ, and define

    J=Dˆρvα(1+2/n)dx=Dˆρv2/nvα(1+2/n)2/ndx. (15)

    Thus, by the Hölder inequality,

    J(Dˆρv2dx)1/n(Dˆρv[α+(2/n)(α1)]n/(n1)dx)(n1)/n. (16)

    Further applying the Sobolev inequality to (Dˆρv[α+(2/n)(α1)]n/(n1)dx)(n1)/n, we get

    (Dˆρv[α+(2/n)(α1)]n/(n1)dx)(n1)/nC(n)Dˆρ|vα+(2/n)(α1)|dxC(n)Dˆρv(α1)(1+2/n)vdx. (17)

    The final inequality sign in the above expression holds because (α1)(1+2n)=α+2(α1)n1. Using the Hölder inequality again,

    Dˆρv(α1)(1+2/n)|v|dx(Dˆρ|v|αdx)1α(Dˆρvα(1+2/n)|v|dx)α1α. (18)

    Therefore, by combining inequalities (16)–(18), we obtain

    JC(n)Jα1α(Dˆρv2dx)1/n(Dˆρ|v|αdx)1α. (19)

    Thus, to estimate J, it suffices to analyze (Dˆρ|v|αdx)1α and (Dˆρv2dx)1/n and obtain their upper bounds with respect to u. Notice that the cut-off function ψ(x,t) satisfies 0ψ(x,t)1inQ2ρ,2s and |ψ(x,t)|C1ρ, thus

    (Dˆρ|v|αdx)1αCˆρ(Dˆρ|uIρ(t)|αdx)1α+(Dˆρ|u|αdx)1α. (20)

    Applying the Minkowski inequality again, we get

    (Dˆρ|uIρ(t)|αdx)1α(Dˆρ|uIˆρ(t)|αdx)1α+(Dˆρ|Iˆρ(t)Iρ(t)|αdx)1α(Dˆρ|uIˆρ(t)|αdx)1α+|Iˆρ(t)Iρ(t)|×|Dˆρ|1α. (21)

    Further analyzing |Iˆρ(t)Iρ(t)|×|Dˆρ|1α in (21), by Iρ(t), we have

    |Iˆρ(t)Iρ(t)|×|Dˆρ|1α|Dˆρ|1α|Dρ|1×Dρ|uIˆρ(t)|dx|Dˆρ|1α|Dρ|1×Dˆρ|uIˆρ(t)|dx. (22)

    Note that we previously assumed uLα(τ2s,τ+2s;W1,p(D2ρ(z))). Thus, combining inequalities (21) and (22), and using the Sobolev inequality, we obtain

    (Dˆρ|uIρ(t)|αdx)1αC(1+ρn/αˆρn)(Dˆρ|uIˆρ(t)|αdx)1αCˆρ(Dˆρ|u|αdx)1α. (23)

    Here, we have used the conditions α>1 and ˆρ>ρ>0. Substituting (23) into (20), we obtain an estimate for (Dˆρ|v|αdx)1α as

    (Dˆρ|v|αdx)1αC(Dˆρ|u|αdx)1α. (24)

    First, we analyze (Dˆρv2dx)1n. By estimating (Dˆρ|uIρ(t)|2dx)1n and applying Minkowski's inequality, we can derive

    (Dˆρ|uIρ(t)|2dx)1n((Dˆρ|uIˆρ(t)|2dx)2+(Dˆρ|Iρ(t)Iˆρ(t)|2dx)2)12n. (25)

    By utilizing the inequality (a+b)12n(2n)2n(a12n+b12n), the estimate for (Dˆρ|uIρ(t)|2dx)1n can be reformulated as

    (Dˆρ|uIρ(t)|2dx)1n(2n)2n((Dˆρ|uIˆρ(t)|2dx)1n+(Dˆρ|Iρ(t)Iˆρ(t)|2dx)1n). (26)

    Furthermore, by the definition of Iρ(t), we obtain

    (Dˆρ|Iρ(t)Iˆρ(t)|2dx)1n=|Iˆρ(t)Iρ(t)|2n×|Dˆρ|1nCρn/2ˆρn(Dˆρ|uIˆρ(t)|2dx)1n. (27)

    Thus, by combining inequalities (17) and (18), we derive

    (Dˆρ|uIρ(t)|2dx)1nCρn/2ˆρn(Dˆρ|uIˆρ(t)|2dx)1n. (28)

    The truncation function ψ(x,t) fulfills 0ψ(x,t)1inQ2ρ,2s, thereby yielding an estimate for (Dˆρv2dx)1n denoted as

    (Dˆρv2dx)1n(Dˆρ|uIρ(t)|2dx)1nCρn/2ˆρn(Dˆρ|uIˆρ(t)|2dx)1n. (29)

    In summary, by substituting the results from (24) and (29) into Eq (19), we obtain

    JC(n)Jα1αCρn/2ˆρn(Dˆρ|uIˆρ(t)|2dx)1nC(Dˆρ|u|αdx)1α.

    It is readily seen that Dρ|u(x,t)Iρ(t)|α(1+2/n)dxJ, and thus we obtain the result of Theorem 3.1. □

    This section presents an inverse Hölder inequality result. Before that, we introduce a Caccioppoli inequality, which is utilized in the proof.

    Lemma 4.1 (Caccioppoli's Inequality) Suppose u is a solution to the variational inequality (1). Then, for any non-negative constant λ, we have

    suptIρDρ(uλ)2dx+Qρ,su|u|pdxdtCsQ2ρ,2s(uλ)2dxdt+CρpQ2ρ,2s(uλ)pdxdt.

    Proof Note that from (1), it can be easily deduced that Lu0 in Q2ρ,2s. Let us choose a test function ϕ=ψ2×(uλ)+, and then integrate ϕ×Lu0 over Q2ρ,2s, resulting in

    Q2ρ,2stuψ2(uλ)dxdt+Q2ρ,2s|u|p2u[ψ2(uλ)]dxdt+(1γ)Q2ρ,2sψ2(uλ)+|u|pdxdt=0. (30)

    First, let us analyze Q2ρ,2stu×ψ2(uλ)+dxdt by employing the method of integration by parts, yielding

    Q2ρ,2st[ψ2(uλ)+2]dxdt=Q2ρ,2stuψ2(uλ)+dxdt+2Q2ρ,2sψψ(uλ)+2dxdt, (31)

    and

    Q2ρ,2su|u|p2u[ψ2(uλ)+]dxdt=Q2ρ,2sψ2u|u|pdxdt+2Q2ρ,2sψψ×u|u|p2u×(uλ)+dxdt. (32)

    Substituting Eqs (31) and (32) into Eq (30), we obtain

    QsRt[ψ2(uλ)+2]dxdt2QsRψψ(uλ)+2dxdt+QsRψ2|u|pdxdt+2QsRψψ×|u|p2u×(uλ)+dxdt=0,

    that is

    BRψ2(uλ)+2dx|t=s+QsRψ2|u|pdxdt=2QsRψψ(uλ)+2dxdt2QsRψψ×|u|p2u×(uλ)+dxdt. (33)

    Based on [11,12], by applying the Hölder's inequality and Young's inequality to Eq (4), we have

    suptIρDρ(uλ)+2dx+Qρ,su|u|pdxdtCsQ2ρ,2s(uλ)2dxdt+CρpQ2ρ,2s(uλ)pdxdt. (34)

    Moreover, by selecting ϕ=ψ2(uλ) and repeating the proof process of Eqs (30)–(34), we can easily obtain

    suptIρDρ(uλ)+2dx+Qρ,su|u|pdxdtCsQ2ρ,2s(uλ)2dxdt+CρpQ2ρ,2s(uλ)pdxdt. (35)

    By combining Eqs (34) and (35), the theorem is proven.□

    Theorem 4.1 Define q=max{p1,pn/(n+2)} and let u be a solution to the variational inequality (1). Then, we have

    Qρ,su|u|pdxdt(Q2ρ,2s|u|qdxdt)qp.

    Proof It is important to note that I2ρ(t) is not a constant over Q2ρ,2s, and thus, we choose

    λ=a(Q2ρ,2s)=1|Q2ρ,2s|Q2ρ,2sudxdt

    in the Caccioppoli inequality, resulting in

    Qρ,su|u|pdxdtCsQ2ρ,2s|ua(Q2ρ,2s)|2dxdt+CρpQ2ρ,2s|ua(Q2ρ,2s)|pdxdt. (36)

    By utilizing the Hölder and Young inequalities, we can obtain

    CsQ2ρ,2s|ua(Q2ρ,2s)|2dxdtCs|Q2ρ,2s|p2p(Q2ρ,2s|ua(Q2ρ,2s)|pdxdt)2pC(p)|QsR|(ρ2s)pp2+1ρpQ2ρ,2s|ua(Q2ρ,2s)|pdxdt. (37)

    Combining Eqs (36) and (37), we can estimate Qρ,su|u|pdxdt by analyzing only Q2ρ,2s|ua(Q2ρ,2s)|pdxdt. Using the Minkowski inequality, we have

    CpρpQ2ρ,2s|ua(Q2ρ,2s)|pdxdtCρpQ2ρ,2s|uI2ρ(t)|pdxdt+Cρp|Q2ρ|esssupt(τs,τ+s)|I2ρ(t)a(Q2ρ,2s)|p. (38)

    Next, we analyze the Q2ρ,2s|uI2ρ(t)|pdxdt and |I2ρ(t)a(Q2ρ,2s)|p in Eq (38). By the definition of Iρ(t) and Lemma 3.1, we have

    |I2ρ(t)a(Q2ρ,2s)|p(4s)p(τ+2sτ2s|I2ρ(t)I2ρ(ξ)|dξ)pCρp(Q2ρ,2s|u|p1dxdt)p. (39)

    Therefore, by utilizing the Hölder and Young inequalities, we can estimate the second term on the right-hand side of Eq (38) as follows:

    |I2ρ(t)a(Q2ρ,2s)|pCρp|Q2ρ,2s|1p(Q2ρ,2s|u|pdxdt)p1p. (40)

    Now, let us estimate the first term on the right-hand side of Eq (38). By considering Theorem 3.1, we have

    Q2ρ,2s|u(x,t)I2ρ(t)|pdxdtC(Q2ρ,2s|u|qdxdt)(esssupt(τ4s,τ+4s)D4ρ|uI4ρ(t)|2dx)qn. (41)

    Furthermore, by utilizing the Sobolev inequality on D4ρ, we obtain

    D4ρ|uI4ρ(t)|2dxCρ2D4ρ|u|2dxCρ2(D4ρ|u|pdx)2/p|D4ρ|(p2)/p=Cρ2(D4ρ|u|pdx)2/p|D4ρ|. (42)

    By substituting (42) into (41) and combining it with (40), we obtain

    Qρ,su|u|pdxdtC(p)|QsR|(ρ2s)pp2+Cρp(cρ2|D4ρ|)qn(Q2ρ,2s|u|qdxdt)+Cρ2p|Q2ρ,2s|1p(Q2ρ,2s|u|p1dxdt)p.

    Here, we make use of the fundamental result uLp(Ω), which is detailed in (8). With this, the proof of Theorem 4.1 is complete. □

    This study considers a type of variational inequality problem involving a non-divergence parabolic operator, as shown in (1) and (2). In other words, the Sobolev estimates and inverse Hölder estimates are examined for the solutions of variational inequality (1). First, we define the averaging operator of the variational inequality (1) on the local spatial region Dρ as Iρ(t) and prove the uniform continuity of the mean inequality Iρ(t) with respect to time t. Second, we establish a Sobolev inequality for the averaging operator Iρ(t), which serves as the cornerstone for proving the inverse Hölder estimates, as stated in Theorem 3.1. Finally, we examine the inverse Hölder estimate problem in local cylindrical regions. The proof relies on Lemma 4.1 and Theorem 3.1, as well as commonly used amplification techniques such as Minkowski inequality, Young's inequality, and Hölder's inequality.

    There are still some areas for improvement in the proofs presented in this paper. It is important to note that we make use of the condition γ(0,1), as when γ>1 holds, Eqs (6), (9), and (30) cannot be used as they are in this paper. In such cases, 1γ<0, and we cannot eliminate the non-negative terms containing 1γ. Furthermore, in [8], the existence of weak solutions to similar problems is discussed under the condition γ(0,1), which we also continue to adopt here. On the other hand, in order to employ Young's inequality and Hölder's inequality, we also restrict p2. It is worth noting that in the study of regularity theory for parabolic equations, the literature has considered the case p(1,2), and we also aim to explore this limitation in our future research.

    The author declare they have not used Artificial Intelligence (AI) tools in the creation of this article.

    The authors are very grateful to the anonymous referees for their insightful comments and constructive suggestions, which considerably improve our manuscript. This work is supported by Growth Project of Young Scientific and Technological Talents in Colleges and Universities of Guizhou Province (NO. KY[2022]191).

    The author declares there is no conflict of interest.



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