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Research Article

Sharp rates of decay of solutions to the nonlinear fast diffusion equation via functional inequalities

M. Bonforte, J. Dolbeault, G. Grillo, and J. L. Vázquez
  1. aDepartamento de Matemáticas, Universidad Autónoma de Madrid, Campus de Cantoblanco, 28049 Madrid, Spain;
  2. bCeremade, Unité Mixte de Recherche Centre National de la Recherche Scientifique 7534, Université Paris-Dauphine, Place de Lattre de Tassigny, 75775 Paris 16, France;
  3. cDipartimento di Matematica, Politecnico di Milano, Piazza Leonardo da Vinci 32, 20133 Milan, Italy; and
  4. dInstituto de Ciencias Matemáticas, Universidad Autónoma de Madrid, 28049 Madrid, Spain

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PNAS September 21, 2010 107 (38) 16459-16464; https://doi.org/10.1073/pnas.1003972107
M. Bonforte
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J. Dolbeault
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G. Grillo
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J. L. Vázquez
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  • For correspondence: juanluis.vazquez@uam.es
  1. Communicated by Luis A. Caffarelli, The University of Texas, Austin, TX, April 1, 2010 (received for review July 16, 2009)

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Abstract

The goal of this paper is to state the optimal decay rate for solutions of the nonlinear fast diffusion equation and, in self-similar variables, the optimal convergence rates to Barenblatt self-similar profiles and their generalizations. It relies on the identification of the optimal constants in some related Hardy–Poincaré inequalities and concludes a long series of papers devoted to generalized entropies, functional inequalities, and rates for nonlinear diffusion equations.

  • asymptotic expansion
  • intermediate asymptotics
  • porous media equation
  • Barenblatt solutions
  • large-time behavior

The evolution equation Embedded Image[1]with m ≠ 1 is a simple example of a nonlinear diffusion equation which generalizes the heat equation and appears in a wide number of applications. Solutions differ from the linear case in many respects, notably concerning existence, regularity, and large-time behavior. We consider positive solutions u(τ,y) of this equation posed for τ≥0 and Embedded Image, d≥1. The parameter m can be any real number. The equation makes sense even in the limit case m = 0, where um/m has to be replaced by log u, and is formally parabolic for all Embedded Image. Notice that [1] is degenerate at the level u = 0 when m > 1 and singular when m < 1. We consider the initial-value problem with nonnegative datum Embedded Image, where dx denotes Lebesgue’s measure on Embedded Image. Further assumptions on u0 are needed and will be specified later.

The description of the asymptotic behavior of the solutions of [1] as τ → ∞ is a classical and very active subject. If m = 1, the convergence of solutions of the heat equation with Embedded Image to the Gaussian kernel (up to a mass factor) is a cornerstone of the theory. In the case of Eq. 1 with m > 1, known in the literature as the porous medium equation, the study of asymptotic behavior goes back to ref. 1. The result extends to the exponents m∈(mc,1) with mc≔(d - 2)/d; see ref. 2. In these results, the Gaussian kernel is replaced by some special self-similar solutions UD,T known as the Barenblatt solutions (see ref. 3) given by Embedded Image[2]whenever m > mc and m ≠ 1, with Embedded Imagewhere T≥0 and D > 0 are free parameters. To some extent, these solutions play the role of the fundamental solution of the linear diffusion equations, because Embedded Image, where δ is the Dirac delta distribution, and M depends on D. Notice that the Barenblatt solutions converge as m → 1 to the fundamental solution of the heat equation, up to the mass factor M. The results of refs. 1 and 2 say that UD,T also describes the large-time asymptotics of the solutions of Eq. 1 as τ → ∞ provided Embedded Image is finite, a condition that uniquely determines D = D(M). Notice that in the range m≥mc, solutions of [1] with Embedded Image exist globally in time and mass is conserved: Embedded Image for any τ≥0.

On the other hand, when m < mc, a natural extension for the Barenblatt functions can be achieved by considering the same expression [2], but a different form for R, that is, Embedded ImageThe parameter T now denotes the extinction time, an important feature. The limit case m = mc is covered by R(τ) = eτ, UD,T(τ,y) = e-dτ(D + e-2τ|y|2/d)-d/2. See refs. 4 and 5 for more detailed considerations.

In this paper, we shall focus our attention on the case m < 1 which has been much less studied. In this regime, [1] is known as the fast diffusion equation. We do not even need to assume m > 0. We shall summarize and extend a series of recent results on the basin of attraction of the family of generalized Barenblatt solutions and establish the optimal rates of convergence of the solutions of [1] toward a unique attracting limit state in that family. Such basin of attraction is different according to m being above or below the value m∗≔(d - 4)/(d - 2), and for m = m∗ the long-time behavior of the solutions has specific features. To state our results, it is more convenient to rescale the flow and rewrite [1] in self-similar variables by introducing for m ≠ mc the time-dependent change of variables Embedded Image[3]with R as above. If m = mc, we take t = τ/d and Embedded Image. In these new variables, the generalized Barenblatt functions UD,T(τ,y) are transformed into generalized Barenblatt profiles VD(x), which are stationary: Embedded Image[4]If u is a solution to [1], the function Embedded Imagesolves the equation Embedded Image[5]with initial condition v(t = 0,x) = v0(x)≔R(0)-du0(y), where x and y are related according to [3] with τ = 0. This nonlinear Fokker–Planck equation can also be written as Embedded Image

Main Results

Our main result is concerned with the sharp rate at which a solution v of the rescaled Eq. 5 converges to the generalized Barenblatt profile VD given by formula 4 in the whole range m < 1. Convergence is measured in terms of the relative entropy given by the formula Embedded Imagefor all m ≠ 0 (modified as mentioned for m = 0). In order to get such convergence, we need the following assumptions on the initial datum v0 associated to [5]:

(H1) VD0 ≤ v0 ≤ VD1 for some D0 > D1 > 0,

(H2) if d≥3 and m ≤ m∗, (v0 - VD) is integrable for a suitable D∈[D1,D0].

The case m = m∗ will be discussed later. Besides, if m > m∗, we define D as the unique value in [D1,D0] such that Embedded Image.

Theorem 1.

Under the above assumptions, if m < 1 and m ≠ m∗, the entropy decays according to Embedded Image[6]The sharp decay rate Λ is equal to the best constant Λα,d > 0 in the Hardy–Poincaré inequality of Theorem 2 with α≔1/(m - 1) < 0. Moreover, the constant C > 0 depends only on m, d, D0, D1, D, and Embedded Image.

The precise meaning of what “sharp rate” means will be discussed at the end of this paper. As in ref. 4, we can deduce from Theorem 1 rates of convergence in more standard norms, namely, in Lq(dx) for q≥ max{1,2d(1 - m)/[2(2 - m) + d(1 - m)]}, or in Ck by interpolation. Moreover, by undoing the time-dependent change of variables [3], we can also deduce results on the intermediate asymptotics for the solution of Eq. 1; to be precise, we can get rates of decay of u(τ,y) - R(τ)-dUD,T(τ,y) as τ → +∞ if m∈[mc,1), or as τ → T if m∈(-∞,mc).

It is worth spending some words on the basin of attraction of the Barenblatt solutions UD,T given by [2]. Such profiles have two parameters: D corresponds to the mass while T has the meaning of the extinction time of the solution for m < mc and of a time-delay parameter otherwise. Fix T and D, and consider first the case m∗ < m < 1. The basin of attraction of UD,T contains all solutions corresponding to data which are trapped between two Barenblatt profiles UD0,T(0,·),UD1,T(0,·) for the same value of T and such that Embedded Image for some D∈[D1,D0]. If m < m∗, the basin of attraction of a Barenblatt solution contains all solutions corresponding to data which, besides being trapped between UD0,T and UD1,T, are integrable perturbations of UD,T(0,·).

Now, let us give an idea of the proof of Theorem 1. First assume that D = 1 (this entails no loss of generality). On Embedded Image, we shall therefore consider the measure dμα≔hαdx, where the weight hα is the Barenblatt profile, defined by hα(x)≔(1 + |x|2)α, with α = 1/(m - 1) < 0, and study on the weighted space L2(dμα) the operator Embedded Imagewhich is such that Embedded Image, e.g., on Embedded Image. This operator appears in the linearization of [5] if, at a formal level, we expand Embedded Imagein terms of ε, small, and only keep the first-order terms: Embedded ImageThe convergence result of Theorem 1 follows from the energy analysis of this equation based on the Hardy–Poincaré inequalities that are described below. Let us fix some notations. For d≥3, let us define α∗≔-(d - 2)/2 corresponding to m = m∗; two other exponents will appear in the analysis, namely, m1≔(d - 1)/d with corresponding α1 = -d, and m2≔d/(d + 2) with corresponding α2 = -(d + 2)/2. We have m∗ < mc < m2 < m1 < 1. Similar definitions for d = 2 give m∗ = -∞, so that α∗ = 0, as well as mc = 0, and m1 = m2 = 1/2. For the convenience of the reader, Table 1 summarizes the key values of the parameter m and the corresponding values of α.

Theorem 2. (Sharp Hardy-Poincaré inequalities)

Let d≥3. For any α∈(-∞,0)∖{α∗}, there is a positive constant Λα,d such that Embedded Image[7]under the additional condition Embedded Image if α < α∗. Moreover, the sharp constant Λα,d is given by Embedded ImageFor d = 2, inequality 7 holds for all α < 0, with the corresponding values of the best constant Λα,2 = α2 for α∈[-2,0) and Λα,2 = -2α for α∈(-∞,-2). For d = 1, [7] holds, but the values of Λα,1 are given by Λα,1 = -2α if α < -1/2 and Λα,1 = (α - 1/2)2 if α∈[-1/2,0).

The Hardy–Poincaré inequalities [7] share many properties with Hardy’s inequalities, because of homogeneity reasons. A simple scaling argument indeed shows that Embedded Imageholds for any f∈H1((D + |x|2)αdx) and any D≥0, under the additional conditions Embedded Image and D > 0 if α < α∗. In other words, the optimal constant, Λα,d, does not depend on D > 0 and the assumption D = 1 can be dropped without consequences. In the limit D → 0, they yield weighted Hardy-type inequalities (cf. refs. 6 and 7).

Theorem 2 has been proved in (4) for m < m∗. The main improvement of this report compared to refs. 4 and 8 is that we are able to give the value of the sharp constants also in the range (m∗,1). These constants are deduced from the spectrum of the operator Embedded Image, that we shall study below (see Fig. 1).

Fig. 1.
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Fig. 1.

Spectrum of Embedded Image as a function of α, for d = 5.

It is relatively easy to obtain the classical decay rates of the linear case in the limit m → 1 by a careful rescaling such that weights become proportional to powers of the modified expression (1 + (1 - m)|x|2)-1/(1-m). In the limit case, we obtain the Poincaré inequality for the Gaussian weight. As for the evolution equation, the time also has to be rescaled by a factor (1 - m). We leave the details to the reader. See ref. 9 for further considerations on associated functional inequalities.

Brief Historical Overview

The search for sharp decay rates in fast diffusion equations has been extremely active over the last three decades. Once plain convergence of the suitably rescaled flow toward an asymptotic profile is established (cf. refs. 1 and 2 for m > mc and refs. 4 and 10 for m ≤ mc), getting the rates is the next step in the asymptotic analysis. An important progress was achieved by Del Pino and Dolbeault (11) by identifying sharp rates of decay for the relative entropy, that had been introduced earlier by Newman (12) and Ralston (13). The analysis in ref. 11 uses the optimal constants in Gagliardo–Nirenberg inequalities, and these constants are computed. Carrillo and Toscani (14) gave a proof of decay based on the entropy/entropy-production method of Bakry and Emery, and established an analogue of the Csiszár–Kullback inequality which allows to control the convergence in L1(dx), in case m > 1. Otto (15) then made the link with gradient flows with respect to the Wasserstein distance, and Cordero-Erausquin et al. (16) gave a proof of Gagliardo–Nirenberg inequalities using mass transportation techniques.

The condition m≥(d - 1)/d≕m1 was definitely a strong limitation to these first approaches, except maybe for the entropy/entropy-production method. Gagliardo–Nirenberg inequalities degenerate into a critical Sobolev inequality for m = m1, whereas the displacement convexity condition requires m≥m1. It was a puzzling question to understand what was going on in the range mc < m < m1, and this has been the subject of many contributions. Because one is interested in understanding the convergence toward Barenblatt profiles, a key issue is the integrability of these profiles and their moments, in terms of m. To work with Wasserstein’s distance, it is crucial to have second moments bounded, which amounts to request m > d/(d + 2)≕m2 for the Barenblatt profiles. The contribution of Denzler and McCann (17, 18) enters in this context. Another, weaker, limitation appears when one only requires the integrability of the Barenblatt profiles, namely m > mc. Notice that the range [mc,1) is also the range for which L1(dx) initial data give rise to solutions which preserve the mass and globally exist; see, for instance, refs. 1 and 5.

It was therefore natural to investigate the range m∈(mc,1) with entropy estimates. This has been done first by linearizing around the Barenblatt profiles in refs. 19 and 20, and then a full proof for the nonlinear flow was done by Carrillo and Vázquez (21). A detailed account for these contributions and their motivations can be found in the survey paper, ref. 22. Compared to classical approaches based on comparison, as in the book in ref. 5, a major advantage of entropy techniques is that they combine very well with L1(dx) estimates if m > mc, or relative mass estimates otherwise; see ref. 4.

The picture for m ≤ mc turns out to be entirely different and more complicated, and it was not considered until quite recently. First of all, many classes of solutions vanish in finite time, which is a striking property that forces us to change the concept of asymptotic behavior from large-time behavior to behavior near the extinction time. On the other hand, L1(dx) solutions lose mass as time evolves. Moreover, the natural extensions of Barenblatt’s profiles make sense but these profiles have two interesting properties: They vanish in finite time and they do not have finite mass.

There is a large variety of possible behaviors and many results have been achieved, such as the ones described in ref. 5 for data which decay strongly as |x| → ∞. However, as long as one is interested in solutions converging toward Barenblatt profiles in self-similar variables, there were some recent results on plain convergence: a paper of Daskalopoulos and Sesum (10), using comparison techniques, and in two contributions involving the authors of this paper, using relative entropy methods, see refs. 4 and 8. This last approach proceeds further into the description of the convergence by identifying a suitable weighted linearization of the relative entropy. In the appropriate space, L2(dμα-1), with the notations of Theorem 2, it gives rise to an exponential convergence after rescaling. This justifies the heuristic computation which relates Theorems 1 and 2, and allows to identify the sharp rates of convergence. The point of this paper is to explicitly state and prove such rates in the whole range m < 1.

Relative Entropy and Linearization

The strategy developed in ref. 4 is based on the extension of the relative entropy of Ralston and Newmann (12, 13), which can be written in terms of w = v/VD as Embedded ImageFor simplicity, assume m ≠ 0. Notice that Embedded Image. Let Embedded Imagebe the generalized relative Fisher information. If v is a solution of [5], then Embedded Image[8]and, as a consequence, Embedded Image for all m < 1. The method is based on Theorem 2 and uniform estimates that relate linear and nonlinear quantities. Following refs. 4 and 23, we can first estimate from below and above the entropy Embedded Image in terms of its linearization, which appears in [7]: Embedded Image[9]where Embedded Image, Embedded Image, Embedded Image, and h≔ max{h2,1 = h1}. We notice that h(t) → 1 as t → +∞. Similarly, the generalized Fisher information satisfies the bounds Embedded Image[10]where Embedded Image and d(1 - m)[(h2/h1)2(2-m) - 1] ≤ d(1 - m)[h4(2-m) - 1]≕Y(h). Notice that X(1) = Y(1) = 0. Joining these inequalities with the Hardy–Poincaré inequality of Theorem 2 gives Embedded Image[11]as soon as 0 < h < h∗≔ min{h > 0: Λα,d - Y(h)≥0}. On the other hand, uniform relative estimates hold, according to ref. 23 [formula (5.33)]: for some Embedded Image, Embedded Image[12]Summarizing, we end up with a system of nonlinear differential inequalities, with h as above and, at least for any t > t∗, t∗ > 0 large enough, Embedded Image[13]Gronwall-type estimates then show that Embedded ImageThis completes the proof of Theorem 1 for m ≠ 0. The adaptation to the logarithmic nonlinearity is left to the reader. Results in ref. 4 are improved in two ways: a time-dependent estimate of h is used in place of h(0), and the precise expression of the rate is established. One can actually get a slightly more precise estimate by coupling [12] and [13].

Corollary 3.

Under the assumptions of Theorem 1, if h(0) < h∗, then Embedded Image for any t≥0, where G is the unique solution of the nonlinear ordinary differential equation Embedded Imageand initial condition Embedded Image.

Operator Equivalence: Spectrum of Embedded Image

An important point of this paper is the computation of the spectrum of Embedded Image for any α < 0. This spectrum was only partially understood in refs. 4 and 8. In particular, the existence of a spectral gap was established for all α ≠ α∗ = (2 - d)/2, but its value was not stated for all values of α.

Denzler and McCann (17, 18) formally linearized the fast diffusion flow (considered as a gradient flow of the entropy with respect to the Wasserstein distance) in the framework of mass transportation, in order to guess the asymptotic behavior of the solutions of [1]. This leads to a different functional setting, with a different linearized operator, Embedded Image. They performed the detailed analysis of its spectrum for all m∈(mc,1), but the justification of the nonlinear asymptotics could not be completed due to the difficulties of the functional setting, especially in the very fast diffusion range.

Our approach is based on relative entropy estimates and the Hardy–Poincaré inequalities of Theorem 2. The asymptotics have readily been justified in ref. 4. The operator Embedded Image can be initially defined on Embedded Image. To construct a self-adjoint extension of such an operator, one can consider the quadratic form Embedded Image, where (·,·) denotes the scalar product on L2(dμα-1). Standard results show that such a quadratic form is closable, so that its closure defines a unique self-adjoint operator, its Friedrich’s extension, still denoted by the same symbol for brevity. The operator Embedded Image is different: It is obtained by taking the operator closure of Embedded Image, initially defined on Embedded Image, in the Hilbert space Embedded Image. Because of the Hardy–Poincaré inequality, Embedded Image defines a norm. Denote by 〈·,·〉 the corresponding scalar product and notice that Embedded Image for any Embedded Image.

Proposition 4.

The operator Embedded Image on L2(dμα-1) has the same spectrum as the operator Embedded Image on H1,∗(dμα).

The proof is based on the construction of a suitable unitary operator U: H1,∗(dμα) → L2(dμα-1), such that Embedded Image. We claim that Embedded Image is the requested unitary operator. By definition, Embedded Image is a form core of Embedded Image, and, as a consequence, the identity has to be established only for functions Embedded Image. Because Embedded Image, we get Embedded Imagewhere we have used the properties U∗ = U-1 and Embedded Image. This unitary equivalence between Embedded Image and Embedded Image implies the identity of their spectra.

We may now proceed with the presentation of the actual values of the spectrum by extending the results of ref. 18. According to ref. 24, the spectrum of the Laplace–Beltrami operator on Sd-1 is described by Embedded Imagewith ℓ = 0,1,2,… and Embedded Image with the convention M0 = 1, and M1 = 1 if d = 1. Using spherical coordinates and separation of variables, the discrete spectrum of Embedded Image is therefore made of the values of λ for which Embedded Image[14]has a solution on Embedded Image, in the domain of Embedded Image. The change of variables v(r) = rℓw(-r2) allows to express w in terms of the hypergeometric function Embedded Image with c = ℓ + d/2, a + b + 1 = ℓ + α + d/2, and ab = (2ℓα + λ)/4, as the solution for s = -r2 of Embedded Image(see ref. 25). Based on refs. 8 and 18, we can state the following result.

Proposition 5.

The bottom of the continuous spectrum of the operator Embedded Image on L2(dμα-1) is Embedded Image. Moreover, Embedded Image has some discrete spectrum only for m > m2 = d/(d + 2). For d≥2, the discrete spectrum is made of the eigenvalues Embedded Image[15]with ℓ, k = 0,1,… provided (ℓ,k) ≠ (0,0) and ℓ + 2k - 1 < -(d + 2α)/2. If d = 1, the discrete spectrum is made of the eigenvalues λk = k(1 - 2α - k) with Embedded Image.

Using Persson’s characterization of the continuous spectrum, see refs. 8 and 26, one can indeed prove that Embedded Image is the optimal constant in the following inequality: For any Embedded Image, Embedded ImageThe condition that the solution of [14] is in the domain of Embedded Image determines the eigenvalues. A more complete discussion of this topic can be found in ref. 18, which justifies the expression of the discrete spectrum.

Because α = 1/(m - 1), we may notice that for d≥2, α = -d (corresponding to -2α = λ10 = λ01 = -4α - 2d) and α = -(d + 2)/2 (corresponding to Embedded Image), respectively, mean m = m1 = (d - 1)/d and m = m2 = d/(d + 2).

The above spectral results hold exactly in the same form when d = 2 (see ref. 18). Notice in particular that Embedded Image so that there is no equivalent of m∗ for d = 2. With the notations of Theorem 2, α∗ = 0. All results of Theorem 1 hold true under the sole assumption (H1).

In dimension d = 1, the spectral results are different (see ref. 18). The discrete spectrum is nonempty whenever α ≤ -1/2, that is m≥-1.

Critical Case

Because the spectral gap of Embedded Image tends to zero as m → m∗, the previous strategy fails when m = m∗ and one might expect a slower decay to equilibrium, sometimes referred as slow asymptotics. The following result has been proved in ref. 23.

Theorem 6.

Assume that d≥3, let v be a solution of [5] with m = m∗, and suppose that (H1)-(H2) hold. If |v0 - VD| is bounded a.e. by a radial L1(dx) function, then there exists a positive constant C∗ such that Embedded Image[16]where C∗ depends only on m, d, D0, D1, D, and Embedded Image.

Rates of convergence in Lq(dx), q∈(1,∞] follow. Notice that, in dimension d = 3 and 4, we have, respectively, m∗ = -1 and m∗ = 0. In the last case, Theorem 6 applies to the logarithmic diffusion.

The proof relies on identifying first the asymptotics of the linearized evolution. In this case, the bottom of the continuous spectrum of Embedded Image is zero. This difficulty is overcome by noticing that the operator Embedded Image on Embedded Image can be identified with the Laplace–Beltrami operator for a suitable conformally flat metric on Embedded Image, having positive Ricci curvature. Then the on-diagonal heat kernel of the linearized generator behaves like t-d/2 for small t and like t-1/2 for large t. The Hardy–Poincaré inequality is replaced by a weighted Nash inequality: There exists a positive continuous and monotone function Embedded Image on Embedded Image such that, for any nonnegative smooth function f with Embedded Image (recall that α∗ - 1 = -d/2), Embedded ImageThe function Embedded Image behaves as follows: Embedded Image and Embedded Image. Only the first limit matters for the asymptotic behavior. Up to technicalities, inequality 13 is replaced by Embedded Image for some K > 0, t≥t0 large enough, which allows to complete the proof.

Faster Convergence

A very natural issue is the question of improving the rates of convergence by imposing restrictions on the initial data. Results of this nature have been observed in ref. 19 in case of radially symmetric solutions, and are carefully commented in ref. 18. By locating the center of mass at zero, we are able to give an answer, which amounts to kill the λ10 mode, whose eigenspace is generated by x↦xi, i = 1,2,…,d. This is an improvement compared to the first result in this direction, which has been obtained by McCann and Slepčev (27), because we obtain an improved sharp rate of convergence of the solution of [5], as a consequence of the following improved Hardy–Poincaré inequality.

Lemma 1.

Let Embedded Image if α < -d and Embedded Image if α∈[-d,-d/2). If d≥2, for any α∈(-∞,-d), we have Embedded Imageunder the conditions Embedded Image and Embedded Image. The constant Embedded Image is sharp.

This covers the range m∈(m1,1) with m1 = (d - 1)/d.

Theorem 7.

Assume that m∈(m1,1), d≥3. Under Assumption (H1), if v is a solution of [5] with initial datum v0, such that Embedded Image, and if D is chosen so that Embedded Image, then there exists a positive constant Embedded Image depending only on m, d, D0, D1, D, and Embedded Image, such that the relative entropy decays like Embedded Image

Variational Approach to Sharpness

Recall that (d - 2)/d = mc < m1 = (d - 1)/d. The entropy / entropy-production inequality obtained in ref. 11 in the range m∈[m1,1) can be written as Embedded Image and it is known to be sharp as a consequence of the optimality case in Gagliardo–Nirenberg inequalities. Moreover, equality is achieved if and only if v = VD. The inequality has been extended into the range m∈(mc,1) using the Bakry–Emery method, with the same constant 1/2, and again equality is achieved if and only if v = VD, but sharpness of 1/2 is not as straightforward for m∈(mc,m1) as it is for m∈[m1,1). The question of the optimality of the constant can be reformulated as a variational problem, namely to identify the value of the positive constant Embedded Imagewhere the infimum is taken over the set of all functions such that Embedded Image and Embedded Image. Rephrasing the sharpness results, we know that Embedded Image if m∈(m1,1) and Embedded Image if m∈(mc,m1). By taking Embedded Image and letting n → ∞, we get Embedded ImageWith the optimal choice for f, the above limit is less or equal than two. Because we already know that Embedded Image, this shows that Embedded Image for any m > mc. It is quite enlightening to observe that optimality in the quotient gives rise to indetermination because both numerator and denominator are equal to zero when v = VD. This also explains why it is the first-order correction which determines the value of Embedded Image, and, as a consequence, why the optimal constant Embedded Image is determined by the linearized problem.

When m ≤ mc, the variational approach is less clear because the problem has to be constrained by a uniform estimate. Proving that any minimizing sequence Embedded Image is such that vn/VD - 1 converges, up to a rescaling factor, to a function f associated to the Hardy–Poincaré inequalities would be a significant step, except that one has to deal with compactness issues, test functions associated to the continuous spectrum, and a uniform constraint.

Sharp Rates of Convergence and Conjectures

In Theorem 1, we have obtained that the rate exp(-Λα,dt) is sharp. The precise meaning of this claim is that Embedded Imagewhere the infimum is taken on the set Embedded Image of smooth, nonnegative bounded functions w such that ||w - 1||L∞(dx) ≤ h and such that Embedded Image is zero if d = 1, 2, and m < 1, or if d≥3 and m∗ < m < 1, and it is finite if d≥3 and m < m∗. Because, for a solution v(t,x) = w(t,x)VD(x) of [5], [8] holds, by sharp rate we mean the best possible rate, which is uniform in t≥0. In other words, for any λ > Λα,d, one can find some initial datum in Embedded Image such that the estimate Embedded Image is wrong for some t > 0. We did not prove that the rate exp(-Λα,dt) is globally sharp in the sense that for some special initial data, Embedded Image decays exactly at this rate, or that Embedded Image, which is possibly less restrictive.

However, if m∈(m1,1), m1 = (d - 1)/d, then exp(-Λα,dt) is also a globally sharp rate, in the sense that the solution with initial datum u0(x) = VD(x + x0) for any Embedded Image is such that Embedded Image decays exactly like exp(-Λα,dt). This formally answers the dilation-persistence conjecture as formulated in ref. 18. The question is still open when m ≤ m1.

Another interesting issue is to understand if improved rates, that is, rates of the order of exp(-λℓkt) with (ℓ,k) ≠ (0,0), (0, 1), (1, 0) are sharp or globally sharp under additional moment-like conditions on the initial data. It is also open to decide whether Embedded Image is sharp or globally sharp under the extra condition Embedded Image.

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Table 1.

Table of correspondence

Acknowledgments

This work has been supported by the ANR-08-BLAN-0333-01 project CBDif-Fr and the exchange program of University Paris-Dauphine and Universidad Autónoma de Madrid. M.B. and J.L.V. were partially supported by Project MTM2008-06326-C02-01 (Spain). M.B., G.G., and J.L.V. were partially supported by HI2008-0178 (Italy/Spain).

Footnotes

  • 1To whom correspondence should be addressed. E-mail: juanluis.vazquez{at}uam.es.
  • Author contributions: M.B., J.D., G.G., and J.L.V. performed research and wrote the paper.

  • The authors declare no conflict of interest.

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    Sharp rates of decay of solutions to the nonlinear fast diffusion equation via functional inequalities
    M. Bonforte, J. Dolbeault, G. Grillo, J. L. Vázquez
    Proceedings of the National Academy of Sciences Sep 2010, 107 (38) 16459-16464; DOI: 10.1073/pnas.1003972107

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    Sharp rates of decay of solutions to the nonlinear fast diffusion equation via functional inequalities
    M. Bonforte, J. Dolbeault, G. Grillo, J. L. Vázquez
    Proceedings of the National Academy of Sciences Sep 2010, 107 (38) 16459-16464; DOI: 10.1073/pnas.1003972107
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      • Abstract
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      • Brief Historical Overview
      • Relative Entropy and Linearization
      • Operator Equivalence: Spectrum of
      • Critical Case
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      • Variational Approach to Sharpness
      • Sharp Rates of Convergence and Conjectures
      • Acknowledgments
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