NONLOCAL PROBLEMS WITH NEUMANN BOUNDARY CONDITIONS

Size: px
Start display at page:

Download "NONLOCAL PROBLEMS WITH NEUMANN BOUNDARY CONDITIONS"

Transcription

1 NONLOCAL PROBLEMS WITH NEUMANN BOUNDARY CONDITIONS SERENA DIPIERRO, XAVIER ROS-OTON, AND ENRICO VALDINOCI Abstract. We introduce a new Neumann problem for the fractional Laplacian arising from a simple probabilistic consideration, and we discuss the basic properties of this model. We can consider both elliptic and parabolic equations in any domain. In addition, we formulate problems with nonhomogeneous Neumann conditions, and also with mixed Dirichlet and Neumann conditions, all of them having a clear probabilistic interpretation. We prove that solutions to the fractional heat equation with homogeneous Neumann conditions have the following natural properties: conservation of mass inside, decreasing energy, and convergence to a constant as t. Moreover, for the elliptic case we give the variational formulation of the problem, and establish existence of solutions. We also stu the limit properties and the boundary behavior induced by this nonlocal Neumann condition. For concreteness, one may think that our nonlocal analogue of the classical Neumann condition νu = 0 on consists in the nonlocal prescription u(x) u(y) x y = 0 for x n+2s Rn \. 1. Introduction and results The aim of this paper is to introduce the following Neumann problem for the fractional Laplacian { ( ) s u = f in N s u = 0 in R n (1.1) \. Here, N s is a new nonlocal normal derivative, given by u(x) u(y) N s u(x) := c n,s, x Rn \. (1.2) The normalization constant c n,s is the one appearing in the definition the fractional Laplacian ( ) s u(x) u(y) u(x) = c n,s PV. (1.3) R n See [12, 20] for the basic properties of this operator (and for further details on the normalization constant c n,s, whose explicit value only plays a minor role in this paper) Mathematics Subject Classification. 35R11, 60G22. Key words and phrases. Nonlocal operators, fractional Laplacian, Neumann problem. The first author was supported by grant EPSRC EP/K024566/1 (Scotland). The second author was supported by grants MTM C04-01 (Spain) and 2009SGR345 (Catalunya). The third author was supported by grants ERC (Europe) and PRIN FYK7 (Italy). We thank Gerd Grubb for her very interesting comments on a previous version of this manuscript. 1

2 2 SERENA DIPIERRO, XAVIER ROS-OTON, AND ENRICO VALDINOCI As we will see below, the corresponding heat equation with homogeneous Neumann conditions u t + ( ) s u = 0 in, t > 0 N s u = 0 in R n \, t > 0 (1.4) u(x, 0) = u 0 (x) in, t = 0 possesses natural properties like conservation of mass inside or convergence to a constant as t + (see Section 4). The probabilistic interpretation of the Neumann problem (1.4) may be summarized as follows: (1) u(x, t) is the probability distribution of the position of a particle moving randomly inside. (2) When the particle exits, it immediately comes back into. (3) The way in which it comes back inside is the following: If the particle has gone to x R n \, it may come back to any point y, the probability of jumping from x to y being proportional to x y n 2s. These three properties lead to the equation (1.4), being u 0 the initial probability distribution of the position of the particle. A variation of formula (1.2) consists in renormalizing N s u according to the underlying probability law induced by the Lévy process. This leads to the definition Ñ s u(x) := c n,s N s u(x). (1.5) x y n+2s Other Neumann problems for the fractional Laplacian (or other nonlocal operators) were introduced in [4, 8], [1, 3], [9, 10, 11], and [15]. All these different Neumann problems for nonlocal operators recover the classical Neumann problem as a limit case, and most of them has clear probabilistic interpretations as well. We postpone to Section 7 a comparison between these different models and ours. An advantage of our approach is that the problem has a variational structure. In particular, we show that the classical integration by parts formulae u = ν u and u v = v ( )u + v ν u are replaced in our setting by ( ) s u dx = and c n,s 2 R 2n \(C) 2 R n \ N s u dx ( )( ) u(x) u(y) v(x) v(y) dx = Also, the classical Neumann problem { u = f in ν u = g on v ( ) s u + v N s u. R n \ (1.6)

3 NONLOCAL PROBLEMS WITH NEUMANN BOUNDARY CONDITIONS 3 comes from critical points of the energy functional 1 u 2 fu g u, 2 without trace conditions. In analogy with this, we show that our nonlocal Neumann condition { ( ) s u = f in N s u = g in R n (1.7) \ follows from free critical points of the energy functional c R2n\(C)2 n,s u(x) u(y) 2 4 dx f u g u, R n \ see Proposition 3.7. Moreover, as well known, the theory of existence and uniqueness of solutions for the classical Neumann problem (1.6) relies on the compatibility condition f = g. We provide the analogue of this compatibility condition in our framework, that is f = g, see Theorem 3.9. Also, we give a description of the spectral properties of our nonlocal problem, which are in analogy with the classical case. The paper is organized in this way. In Section 2 we give a probabilistic interpretation of our Neumann condition, as a random reflection of a particle inside the domain, according to a Lévy flight. This also allows us to consider mixed Dirichlet and Neumann conditions and to get a suitable heat equation from the stochastic process. In Section 3 we consider the variational structure of the associated nonlocal elliptic problem, we show an existence and uniqueness result (namely Theorem 3.9), as follows: Let R n be a bounded Lipschitz domain, f L 2 (), and g L 1 (R n \ ). Suppose that there exists a C 2 function ψ such that N s ψ = g in R n \. Then, problem (1.7) admits a weak solution if and only if f = g. Moreover, if such a compatibility condition holds, the solution is unique up to an additive constant. Also, we give a description of a sort of generalized eigenvalues of ( ) s with zero Neumann boundary conditions (see Theorem 3.11): Let R n be a bounded Lipschitz domain. Then, there exist a sequence of nonnegative values 0 = λ 1 < λ 2 λ 3, and a sequence of functions u i : R n R such that { ( ) s u i (x) = λ i u i (x) for any x N s u i (x) = 0 for any x R n \. R n \ R n \

4 4 SERENA DIPIERRO, XAVIER ROS-OTON, AND ENRICO VALDINOCI Also, the functions u i (when restricted to ) provide a complete orthogonal system in L 2 (). By similarity with the classical case, we are tempted to consider the above λ i and u i as generalized eigenvalues and eigenfunctions. Though the word generalized will be omitted from now on for the sake of shortness, we remark that this spectral notion is not completely standard, since our eigenfunctions u i are defined in the whole of R n but satisfy the equation ( ) s u i = λ i u i only in the domain (indeed, outside they verify our nonlocal Neumann condition). Moreover, the orthogonality and density properties of u i also refer to their restriction in. In Section 4 we discuss the associated heat equation. As it happens in the classical case, we show that such equation preserves the mass, it has decreasing energy, and the solutions approach a constant as t +. In particular, by the results in Propositions 4.1, 4.2 and 4.3 we have: Assume that u(x, t) is a classical solution to u t + ( ) s u = 0 in, t > 0 N s u = 0 in R n \, t > 0 u(x, 0) = u 0 (x) in, t = 0. Then the total mass is conserved, i.e. for all t > 0 u(x, t) dx = u 0 (x)dx. Moreover, the energy E(t) = R2n\(C)2 u(x, t) u(y, t) 2 dx is decreasing in time t > 0. Finally, the solution approaches a constant for large times: more precisely u 1 u 0 in L 2 () as t +. In Section 5 we compute some limits when s 1, showing that we can recover the classical case. In particular, we show in Proposition 5.1 that: Let R n be any bounded Lipschitz domain. Let u and v be C0 2(Rn ) functions. Then, u lim N s u v = s 1 ν v. R n \ Also, we prove that nice functions can be extended continuously outside in order to satisfy a homogeneous nonlocal Neumann condition, and we characterize the boundary behavior of the nonlocal Neumann function. More precisely, in Proposition 5.2 we show that:

5 NONLOCAL PROBLEMS WITH NEUMANN BOUNDARY CONDITIONS 5 Let R n be a domain with C 1 boundary. Let u be continuous in, with N s u = 0 in R n \. Then u is continuous in the whole of R n. The boundary behavior of the nonolcal Neumann condition is also addressed in Proposition 5.4: Let R n be a C 1 domain, and u C(R n ). Then, for all s (0, 1), lim Ñ s u(x) = 0, x x R n \ where Ñ is defined by (1.5). Also, if s > 1 2 and u C1,α (R n ) for some α > 0, then Ñ s u(x + ɛν) ν Ñ s u(x) := lim ɛ 0 + ɛ for some constant κ > 0. = κ ν u for any x, Later on, in Section 6 we deal with an overdetermined problem and we show that it is not possible to prescribe both nonlocal Neumann and Dirichlet conditions for a continuous function. Finally, in Section 7 we recall the various nonlocal Neumann conditions alrea appeared in the literature, and we compare them with our model. All the arguments presented are of elementary 1 nature. Moreover, all our considerations work for any general Lévy measure ν satisfying R n min(1, y 2 )dν(y) < +. However, for sake of clarity of presentation, we have done everything for the most canonical case of the fractional Laplacian. 2. Heuristic probabilistic interpretation Let us consider the Lévy process in R n whose infinitesimal generator is the fractional Laplacian ( ) s. Heuristically, we may think that this process represents the (random) movement of a particle along time t > 0. As it is well known, the probability density u(x, t) of the position of the particle solves the fractional heat equation u t + ( ) s u = 0 in R n ; see [21] for a simple illustration of this fact. Recall that when the particle is situated at x R n, it may jump to any other point y R n, the probability of jumping to y being proportional to x y n 2s. Similarly, one may consider the random movement of a particle inside a bounded domain R n, but in this case one has to decide what happens when the particle leaves. In the classical case s = 1 (when the Lévy process is the Brownian motion), we have the following: 1 To keep the notation as simple as possible, given functions f and g and an operator T, we will often write idendities like f = g in or T f = g in to mean f(x) = g(x) for every x or T f = g for every x, respectively. Also, if u : R n R, we often denote the restriction of u to again by u. We hope that this slight abuse of notation creates no problem to the reader, but for the sake of clarity we also include an appendix at the end of the paper in which Theorems 3.9 and 3.11 are proved using a functional analysis notation that distinguishes between a function and its restriction.

6 6 SERENA DIPIERRO, XAVIER ROS-OTON, AND ENRICO VALDINOCI (1) If the particle is killed when it reaches the boundary, then the probability distribution solves the heat equation with homogeneous Dirichlet conditions. (2) If, instead, when the particle reaches the boundary it immediately comes back into (i.e., it bounces on ), then the probability distribution solves the heat equation with homogeneous Neumann conditions. In the nonlocal case s (0, 1), in which the process has jumps, case (1) corresponds to the following: The particle is killed when it exits. In this case, the probability distribution u of the process solves the heat equation with homogeneous Dirichlet conditions u = 0 in R n \, and solutions to this problem are well understood; see for example [18, 14, 13, 2]. The analogue of case (2) is the following: When the particle exits, it immediately comes back into. Of course, one has to decide how the particle comes back into the domain. In [1, 3] the idea was to find a deterministic reflection or projection which describes the way in which the particle comes back into. The alternative that we propose here is the following: If the particle has gone to x R n \, then it may come back to any point y, the probability of jumping from x to y being proportional to x y n 2s. Notice that this is exactly the (random) way as the particle is moving all the time, here we just add the restriction that it has to immediately come back into every time it goes outside. Let us finally illustrate how this random process leads to problems (1.1) or (1.4). In fact, to make the exposition easier, we will explain the case of mixed Neumann and Dirichlet conditions, which, we think, is very natural Mixed Dirichlet and Neumann conditions. Assume that we have some domain R n, and that its complement R n \ is splitted into two parts: N (with Neumann conditions), and D (with Dirichlet conditions). Consider a particle moving randomly, starting inside. When the particle reaches D, it obtains a payoff φ(x), which depends on the point x D where the particle arrived. Instead, when the particle reaches N it immediately comes back to as described before. If we denote u(x) the expected payoff, then we clearly have and ( ) s u = 0 u = φ in D, where φ : D R is a given function. Moreover, recall that when the particle is in x N then it goes back to some point y, with probability proportional to x y n 2s. Hence, we have that u(x) = κ in u(y) for x N, for some constant κ, possibly depending on the point x N, that has been fixed. In order to normalize the probability measure, the value of the constant κ is so that κ = 1.

7 NONLOCAL PROBLEMS WITH NEUMANN BOUNDARY CONDITIONS 7 Finally, the previous identity can be written as u(x) u(y) N s u(x) = c n,s = 0 for x N, and therefore u solves ( ) s u = 0 in N s u = 0 in N u = φ in D, which is a nonlocal problem with mixed Neumann and Dirichlet conditions. Note that the previous problem is the nonlocal analogue of u = 0 in ν u = 0 in Γ N u = φ in Γ D, being Γ D and Γ N two disjoint subsets of, in which classical Dirichlet and Neumann boundary conditions are prescribed. More generally, the classical Robin condition a ν u + bu = c on some Γ R may be replaced in our nonlocal framework by an s u + bu = c on some R R n \. Nonlinear boundary conditions may be considered in a similar way Fractional heat equation, nonhomogeneous Neumann conditions. Let us consider now the random movement of the particle inside, with our new Neumann conditions in R n \. Denoting u(x, t) the probability density of the position of the particle at time t > 0, with a similar discretization argument as in [21], one can see that u solves the fractional heat equation with u t + ( ) s u = 0 in for t > 0, N s u = 0 in R n \ for t > 0. Thus, if u 0 is the initial probability density, then u solves problem (1.4). Of course, one can now see that with this probabilistic interpretation there is no problem in considering a right hand side f or nonhomogeneous Neumann conditions { ut + ( ) s u = f(x, t, u) in N s u = g(x, t) in R n \. In this case, g represents a nonlocal flux of new particles coming from outside, and f would represent a reaction term. 3. The elliptic problem Given g L 1 (R n \ ) and measurable functions u, v : R n R, we set u H s := u 2L2() + g 1/2 u 2L2(Rn\) + u(x) u(y) 2 R 2n \(C) 2 dx (3.1)

8 8 SERENA DIPIERRO, XAVIER ROS-OTON, AND ENRICO VALDINOCI and (u, v) H s := Then, we define the space u v dx + R n \ g u v dx (u(x) u(y))(v(x) v(y)) + R 2n \(C) 2 dx. H s := { u : R n R measurable : u H s < + }. (3.2) Notice that H s and its norm depend on g, but we omit this dependence in the notation for the sake of simplicity. Proposition 3.1. H s is a Hilbert space with the scalar product defined in (3.2). Proof. We point out that (3.2) is a bilinear form and u H s = (u, u) H s. Also, if u H s = 0, it follows that u L 2 () = 0, hence u = 0 a.e. in, and that R2n\(C)2 u(x) u(y) 2 dx = 0, which in turn implies that u(x) u(y) = 0 for any (x, y) R 2n \ (C) 2. In particular, a.e. x C and y we have that u(x) = u(x) u(y) = 0. This shows that u = 0 a.e. in R n, so it remains to prove that H s is complete. For this, we take a Cauchy sequence u k with respect to the norm in (3.1). In particular, u k is a Cauchy sequence in L 2 () and therefore, up to a subsequence, we suppose that u k converges to some u in L 2 () and a.e. in. More explicitly, there exists Z 1 R n such that Z 1 = 0 and u k (x) u(x) for every x \ Z 1. (3.3) Also, given any U : R n R, for any (x, y) R 2n we define ( ) U(x) U(y) χ R 2n \(C) 2(x, y) E U (x, y) :=. (3.4) 2 Notice that E uk (x, y) E uh (x, y) = ( ) u k (x) u h (x) u k (y) + u h (y) χ R 2n \(C) 2(x, y) 2 Accordingly, since u k is a Cauchy sequence in H s, for any ɛ > 0 there exists N ɛ N such that if h, k N ɛ then R2n\(C)2 ɛ 2 (u k u h )(x) (u k u h )(y) 2 dx = E uk E uh 2 L 2 (R 2n )..

9 NONLOCAL PROBLEMS WITH NEUMANN BOUNDARY CONDITIONS 9 That is, E uk is a Cauchy sequence in L 2 (R 2n ) and thus, up to a subsequence, we assume that E uk converges to some E in L 2 (R 2n ) and a.e. in R 2n. More explicitly, there exists Z 2 R 2n such that Now, for any x, we set We remark that Z 2 = 0 and E uk (x, y) E(x, y) for every (x, y) R 2n \ Z 2. (3.5) S x := {y R n : (x, y) R 2n \ Z 2 }, W := {(x, y) R 2n : x and y R n \ S x } and V := {x : R n \ S x = 0}. W Z 2. (3.6) Indeed, if (x, y) W we have that y R n \S x, that is (x, y) R 2n \Z 2, and so (x, y) Z 2, which gives (3.6). Using (3.5) and (3.6), we obtain that W = 0, hence by the Fubini s Theorem we have that 0 = W = R n \ S x dx, which implies that R n \ S x = 0 for a.e. x. As a consequence, we conclude that \ V = 0. This and (3.3) imply that \ (V \ Z 1 ) = ( \ V ) Z 1 \ V + Z 1 = 0. In particular, we have that V \ Z 1, so we can fix x 0 V \ Z 1. Since x 0 \ Z 1, we know from (3.3) that lim u k(x 0 ) = u(x 0 ). k + Furthermore, since x 0 V we have that R n \ S x0 = 0. That is, a.e. y R n (namely, for every y S x0 ), we have that (x 0, y) R 2n \ Z 2 and so lim E u k (x 0, y) = E(x 0, y), k + thanks to (3.5). Notice also that (C) R 2n \ (C) 2 and so, recalling (3.4), we have that E uk (x 0, y) := u k(x 0 ) u k (y), x 0 y n+2s 2 for a.e. y C. Thus, we obtain } lim u k(y) = lim {u k (x 0 ) x 0 y n+2s 2 E uk (x 0, y) k + k + = u(x 0 ) x 0 y n+2s 2 E(x 0, y), a.e. y C. This and (3.3) say that u k converges a.e. in R n. Up to a change of notation, we will say that u k converges a.e. in R n to some u. So, using that u k is a Cauchy sequence in H s,

10 10 SERENA DIPIERRO, XAVIER ROS-OTON, AND ENRICO VALDINOCI fixed any ɛ > 0 there exists N ɛ N such that, for any h N ɛ, ɛ 2 lim inf u h u k 2 H k + s lim inf (u h u k ) 2 + lim inf g (u h u k ) 2 k + k + C R2n\(C)2 (u h u k )(x) (u h u k )(y) 2 + lim inf k + dx R2n\(C)2 (u h u) 2 + g (u h u) 2 (u h u)(x) (u h u)(y) 2 + dx = u h u 2 H s, C where Fatou s Lemma was used. This says that u h converges to u in H s, showing that Hs is complete Some integration by parts formulas. The following is a nonlocal analogue of the divergence theorem. Lemma 3.2. Let u be any bounded C 2 function in R n. Then, ( ) s u = N s u. R n \ Proof. Note that u(x) u(y) u(y) u(x) dx = dx = 0, since the role of x and y in the integrals above is symmetric. Hence, we have that ( ) s u(x) u(y) u dx = c n,s R n dx = c n,s u(x) u(y) = c n,s dx = as desired. R n \ R n \ R n \ u(x) u(y) dx N s u(y), More generally, we have the following integration by parts formula. Lemma 3.3. Let u and v be bounded C 2 functions in R n. Then, ( )( ) c n,s u(x) u(y) v(x) v(y) 2 dx = v ( ) s u + R 2n \(C) 2 where c n,s is the constant in (1.3). Proof. Notice that ( )( ) 1 u(x) u(y) v(x) v(y) 2 R 2n \(C) 2 dx u(x) u(y) = v(x) dx + R n R n \ Thus, using (1.3) and (1.2), the identity follows. v(x) R n \ v N s u, u(x) u(y) dx.

11 NONLOCAL PROBLEMS WITH NEUMANN BOUNDARY CONDITIONS 11 Remark 3.4. We recall that if one takes ν u = 1, then one can obtain the perimeter of by integrating this Neumann condition over. Indeed, = dx = ν u dx. (3.7) Analogously, we can define Ñsu, by renormalizing N s u by a factor w s, (x) := c n,s, that is Ñ s u(x) := N su(x) w s, (x) for x Rn \. (3.8) Now, we observe that if Ñsu(x) = 1 for any x R n \, then we find the fractional perimeter (see [6] where this object was introduced) by integrating such nonlocal Neumann condition over R n \, that is: Per s () := c n,s = = = R n \ R n \ R n \ R n \ w s, (x) dx dx w s, (x) Ñsu(x) dx N s u(x) dx, that can be seen as the nonlocal counterpart of (3.7). Remark 3.5. The renormalized Neumann condition in (3.8) can also be framed into the probabilistic interpretation of Section 2. Indeed suppose that C is partitioned into a Dirichlet part D and a Neumann part N and that: our Lévy process receives a final payoff φ(x) when it leaves the domain by landing at the point x in D, if the Lévy process leaves by landing at the point x in N, then it receives an additional payoff ψ(x) and is forced to come back to and keep running by following the same probability law (the case discussed in Section 2 is the model situation in which ψ 0). In this setting, the expected payoff u(x) obtained by starting the process at the point x satisfies ( ) s u = 0 in and u = φ in D. Also, for any x N, the expected payoff landing at x must be equal to the additional payoff ψ(x) plus the average payoff u(y) obtained by jumping from x to y, that is: u(y) for any x N, u(x) = ψ(x) +, which corresponds to Ñsu(x) = ψ(x).

12 12 SERENA DIPIERRO, XAVIER ROS-OTON, AND ENRICO VALDINOCI 3.2. Weak solutions with Neumann conditions. The integration by parts formula from Lemma 3.3 leads to the following: Definition 3.6. Let f L 2 () and g L 1 (R n \ ). Let u H s. We say that u is a weak solution of { ( ) s u = f in N s u = g in R n (3.9) \ whenever c n,s 2 R 2n \(C) 2 for all test functions v H s. ( )( ) u(x) u(y) v(x) v(y) dx = With this definition, we can prove the following. f v + g v (3.10) R n \ Proposition 3.7. Let f L 2 () and g L 1 (R n \ ). Let I : H s R be the functional defined as I[u] := c R2n\(C)2 n,s u(x) u(y) 2 4 dx f u g u R n \ for every u H s. Then any critical point of I is a weak solution of (3.9). Proof. First of all, we observe that the functional I is well defined on H s. Indeed, if u Hs then f u f L 2 () u L 2 () C u H s, and g u g 1/2 g 1/2 u g 1/2 L 1 (R n \) g 1/2 u L 2 (R n \) C u H s. R n \ R n \ Therefore, if u H s we have that I[u] C u H s < +. Now, we compute the first variation of I. For this, we take ɛ < 1 and v H s. Then the function u + ɛv H s, and so we can compute I[u + ɛv] = c n,s 4 = I(u) + ɛ R2n\(C)2 (u + ɛv)(x) (u + ɛv)(y) 2 dx f(u + ɛv) g(u + ɛv) ( c n,s 2 R n \ R 2n \(C) 2 (u(x) u(y))(v(x) v(y)) dx + c n,s 4 ɛ2 R2n\(C)2 v(x) v(y) 2 dx. ) f v g v R n \

13 Hence, NONLOCAL PROBLEMS WITH NEUMANN BOUNDARY CONDITIONS 13 I[u + ɛv] I[u] lim ɛ 0 ɛ = c n,s 2 which means that I [u](v) = c n,s 2 R 2n \(C) 2 (u(x) u(y))(v(x) v(y)) dx R 2n \(C) 2 (u(x) u(y))(v(x) v(y)) dx f v g v, R n \ f v g v. R n \ Therefore, if u is a critical point of I, then u is a weak solution to (3.9), according to Definition 3.6. Next result is a sort of maximum principle and it is auxiliary towards the existence and uniqueness theory provided in the subsequent Theorem 3.9. Lemma 3.8. Let f L 2 () and g L 1 (R n \ ). Let u be any H s function satisfying in the weak sense { ( ) s u = f in N s u = g in R n \, with f 0 and g 0. Then, u is constant. Proof. First, we observe that the function v 1 belongs to H s, and therefore we can use it as a test function in (3.10), obtaining that 0 f = g 0. This implies that R n \ f = 0 a.e. in and g = 0 a.e. in R n \. Therefore, taking v = u as a test function in (3.10), we deduce that and hence u must be constant. R2n\(C)2 u(x) u(y) 2 dx = 0, We can now give the following existence and uniqueness result (we observe that its statement is in complete analogy 2 with the classical case, see e.g. page 294 in [16]). Theorem 3.9. Let R n be a bounded Lipschitz domain, f L 2 (), and g L 1 (R n \). Suppose that there exists a C 2 function ψ such that N s ψ = g in R n \. Then, problem (3.9) admits a solution in H s if and only if f = g. (3.11) R n \ Moreover, in case that (3.11) holds, the solution is unique up to an additive constant. 2 The only difference with the classical case is that in Theorem 3.9 it is not necessary to suppose that the domain is connected in order to obtain the uniqueness result.

14 14 SERENA DIPIERRO, XAVIER ROS-OTON, AND ENRICO VALDINOCI Proof. Case 1. We do first the case g 0, i.e., with homogeneous nonlocal Neumann conditions. We also assume that f 0, otherwise there is nothing to prove. Given h L 2 (), we look for a solution v H s of the problem (v(x) v(y))(ϕ(x) ϕ(y)) v ϕ + R 2n \(C) 2 dx = h ϕ, (3.12) for any ϕ H s, with homogeneous Neumann conditions N sv = 0 in R n \. We consider the functional F : H s R defined as F(ϕ) := h ϕ for any ϕ H. s It is easy to see that F is linear. Moreover, it is continuous on H s : F(ϕ) h ϕ h L 2 () ϕ L 2 () h L 2 () ϕ H s. Therefore, from the Riesz representation theorem it follows that problem (3.12) admits a unique solution v H s for any given h L2 (). Furthermore, taking ϕ := v in (3.12), one obtain that v H s () C h L 2 (). (3.13) Now, we define the operator T o : L 2 () H s as T oh = v. We also define by T the restriction operator in, that is T h = T o h. That is, the function T o h is defined in the whole of R n, then we take T h to be its restriction in. In this way, we see that T : L 2 () L 2 (). We have that T is compact. Indeed, we take a sequence {h k } k N bounded in L 2 (). Hence, from (3.13) we deduce that the sequence of T h k is bounded in H s (), which is compactly embedded in L 2 () (see e.g. [12]). Therefore, there exists a subsequence that converges in L 2 (). Now, we show that T is self-adjoint. For this, we take h 1, h 2 C0 () and we use the weak formulation in (3.12) to say that, for every ϕ, φ H s, we have and T o h 1 ϕ + R 2n \(C) 2 (T o h 1 (x) T o h 1 (y))(ϕ(x) ϕ(y)) dx = h 1 ϕ, (3.14) (T o h 2 (x) T o h 2 (y))(φ(x) φ(y)) T o h 2 φ + R 2n \(C) 2 dx = h 2 φ, (3.15) Now we take ϕ := T o h 2 and φ := T o h 1 in (3.14) and (3.15) respectively and we obtain that h 1 T o h 2 = T o h 1 h 2 for any h 1, h 2 C0 (). Accordingly, since T oh 1 = T h 1 and T o h 2 = T h 2 in, we conclude that h 1 T h 2 = T h 1 h 2 (3.16)

15 NONLOCAL PROBLEMS WITH NEUMANN BOUNDARY CONDITIONS 15 for any h 1, h 2 C0 (). If h 1, h 2 L 2 (), there exist sequences of functions in C0 (), say h 1,k and h 2,k, such that h 1,k h 1 and h 2,k h 2 in L 2 () as k +. From (3.16) we have that h 1,k T h 2,k = T h 1,k h 2,k. (3.17) Moreover, from (3.13) we deduce that T h 1,k T h 1 and T h 2,k T h 2 in H s () as k +, and so h 1,k T h 2,k h 1 T h 2 as k + and T h 1,k h 2,k T h 1 h 2 as k +. The last two formulas and (3.17) imply that h 1 T h 2 = T h 1 h 2 for any h 1, h 2 L 2 (), which says that T is self-adjoint. Now we prove that Ker(Id T ) consists of constant functions. (3.18) First of all, we check that the constants are in Ker(Id T ). We take a function constantly equal to c and we observe that ( ) s c = 0 in (hence ( ) s c + c = c) and N s c = 0 in R n \. This shows that T o c = c in R n, and so T c = c in, which implies that c Ker(Id T ). Viceversa, now we show that if v Ker(Id T ) L 2 (), then v is constant. For this, we consider T o v H s. By construction ( ) s (T o v) + (T o v) = v in, (3.19) in the weak sense, and N s (T o v) = 0 in R n \. (3.20) On the other hand, since v Ker(Id T ), we have that Hence, by (3.19), we have that v = T v = T o v in. (3.21) ( ) s (T o v) = 0 in. Using this, (3.20) and Lemma 3.8, we obtain that T o v is constant. Thus, by (3.21), we obtain that v is constant in and this completes the proof of (3.18). From (3.18) and the Fredholm Alternative, we conclude that Im(Id T ) = Ker(Id T ) = {constant functions}, where the orthogonality notion is in L 2 (). More explicitly, { } Im(Id T ) = f L 2 () s.t. f = 0. (3.22) Now, let us take f such that f = 0. By (3.22), we know that there exists w L2 () such that f = w T w. Let us define u := T o w. By construction, we have that N s u = 0 in R n \, and that ( ) s (T o w) + (T o w) = w in.

16 16 SERENA DIPIERRO, XAVIER ROS-OTON, AND ENRICO VALDINOCI Consequently, in, f = w T w = w T o w = ( ) s (T o w) = ( ) s u, and we found the desired solution in this case. Viceversa, if we have a solution u H s of ( )s u = f in with N s u = 0 in R n \, we set w := f + u and we observe that ( ) s u + u = f + u = w in. Accordingly, we have that u = T o w in R n, hence u = T w in. This says that (Id T )w = w u = f in and so f Im(Id T ). Thus, by (3.22), we obtain that f = 0. This establishes the validity of Theorem 3.9 when g 0. Case 2. Let us now consider the nonhomogeneous case (3.9). By the hypotheses, there exists a C 2 function ψ satisfying N s ψ = g in R n \. Let ū = u ψ. Then, ū solves { ( )sū = f in with N s u = 0 in R n \, f = f ( ) s ψ. Then, as we alrea proved, this problem admits a solution if and only if f = 0, i.e., if 0 = f = f ( ) s ψ. (3.23) But, by Lemma 3.2, we have that ( ) s ψ = R n \ N s ψ = g. R n \ From this and (3.23) we conclude that a solution exists if and only if (3.11) holds. Finally, the solution is unique up to an additive constant thanks to Lemma Eigenvalues and eigenfunctions. Here we discuss the spectral properties of problem (1.1). For it, we will need the following classical tool. Lemma 3.10 (Poincaré inequality). Let R n be any bounded Lipschitz domain, and let s (0, 1). Then, for all functions u H s (), we have u 2 u u(x) u(y) 2 dx C dx, where the constant C > 0 depends only on and s. Proof. We give the details for the facility of the reader. We argue by contradiction and we assume that the inequality does not hold. Then, there exists a sequence of functions u k H s () satisfying u k = 0, u k L 2 () = 1, (3.24)

17 NONLOCAL PROBLEMS WITH NEUMANN BOUNDARY CONDITIONS 17 and u k (x) u k (y) 2 dx < 1 k. (3.25) In particular, the functions {u k } k 1 are bounded in H s (). Using now that the embedding H s () L 2 () is compact (see e.g. [12]), it follows that a subsequence {u kj } j 1 converges to a function ū L 2 (), i.e., u kj ū in L 2 (). Moreover, we deduce from (3.24) that ū = 0, and ū L 2 () = 1. (3.26) On the other hand, (3.25) implies that ū(x) ū(y) 2 dx = 0. Thus, ū is constant in, and this contradicts (3.26). We finally give the description of the eigenvalues of ( ) s with zero Neumann boundary conditions. Theorem Let R n be a bounded Lipschitz domain. Then, there exist a sequence of nonnegative values 0 = λ 1 < λ 2 λ 3, and a sequence of functions u i : R n R such that { ( ) s u i (x) = λ i u i (x) for any x N s u i (x) = 0 for any x R n \. Also, the functions u i (when restricted to ) provide a complete orthogonal system in L 2 (). Proof. We define L 2 0() := { u L 2 () : } u = 0. Let the operator T o be defined by T o f = u, where u is the unique solution of { ( ) s u = f in N s u = 0 in R n \ according to Definition 3.6. We remark that the existence and uniqueness of such solution is a consequence of the fact that f L 2 0 () and Theorem 3.9. We also define T to be the restriction of T o in, that is T f = T o f. In this way T : L 2 0 () L2 0 (). Also, we claim that the operator T is compact and self-adjoint. We first show that T is compact. Indeed, taking v = u = T o f in the weak formulation of the problem (3.10), we obtain c n,s 2 R2n\(C)2 u(x) u(y) 2 dx f L 2 () u L 2 (). (3.27)

18 18 SERENA DIPIERRO, XAVIER ROS-OTON, AND ENRICO VALDINOCI Now, using the Poincaré inequality in Lemma 3.10 (recall that u = 0), we deduce that ( u(x) u(y) 2 ) 1/2 u L 2 () C dx. (3.28) This and (3.27) give that ( u(x) u(y) 2 ) 1/2 dx C f L 2(). (3.29) Now, we take a sequence {f k } k N bounded in L 2 (). From (3.28) and (3.29) we obtain that u k = T f k is bounded in H s (). Hence, since the embedding H s () L 2 () is compact, there exists a subsequence that converges in L 2 (). Therefore, T is compact. Now we show that T is self-adjoint in L 2 0 (). For this, we take f 1 and f 2 in C0 (), with f 1 = f 2 = 0. Then from the weak formulation in (3.10) we have that, for every v, w H s, c n,s (T o f 1 (x) T o f 1 (y))(v(x) v(y)) 2 R 2n \(C) 2 dx = f 1 v (3.30) and c n,s 2 R 2n \(C) 2 (T o f 2 (x) T o f 2 (y))(w(x) w(y)) dx = f 2 w. (3.31) We observe that we can take v := T o f 2 in (3.30) and w := T o f 1 in (3.31) (and recall that T o f i = T f i in ), obtaining that f 1 T f 2 = f 2 T f 1, for any f 1, f 2 C0 (). (3.32) Now, if f 1, f 2 L 2 0 () we can find sequences of functions f 1,k, f 2,k C0 () such that f 1,k f 1 and f 2,k f 2 in L 2 () as k +. Therefore, from (3.32), we have f 1,k T f 2,k = f 2,k T f 1,k. (3.33) We notice that, thanks to (3.28) and (3.29), T f 1,k T f 1 and T f 2,k T f 2 in L 2 () as k +, and therefore, from (3.33), we obtain that f 1 T f 2 = f 2 T f 1, thus proving that T is self-adjoint in L 2 0 (). Thus, by the spectral theorem there exists a sequence of eigenvalues {µ i } i 2 of T, and its corresponding eigenfunctions {e i } i 2 are a complete orthogonal system in L 2 0 (). We remark that Indeed, suppose by contradiction that µ i = 0. Then µ i 0. (3.34) 0 = µ i e i = T e i = T o e i in. (3.35)

19 NONLOCAL PROBLEMS WITH NEUMANN BOUNDARY CONDITIONS 19 By construction, N s (T o e i ) = 0 in R n \. This and (3.35) give that T o e i (y) T o e i (x) = = 0 in R n \. Using this and (3.35) once again we conclude that T o e i 0 a.e. in R n. Therefore 0 = ( ) s (T o e i ) = e i in, which gives that e i 0 in, hence it is not an eigenfunction. This establishes (3.34). From (3.34), we can define λ i := µ 1 i. We also define u i := T o e i and we claim that u 2, u 3, is the desired system of eigenfunctions, with corresponding eigenvalues λ 2, λ 3, Indeed, u i = T o e i = T e i = µ i e i in, (3.36) hence the orthogonality and completeness properties of u 2, u 3, in L 2 0 () follow from those of e 2, e 3, Furthermore, in, we have that ( ) s u i = ( ) s (T o e i ) = e i = λ i u i, where (3.36) was used in the last step, and this proves the desired spectral property. Now, we notice that λ i > 0 for any i 2. (3.37) Indeed, its corresponding eigenfunction u i solves { ( ) s u i = λ i u i in N s u i = 0 in R n \, (3.38) Then, if we take u i as a test function in the weak formulation of (3.38), we obtain that c R2n\(C)2 n,s u i (x) u i (y) 2 2 dx = λ i u 2 i, which implies that λ i 0. Now, suppose by contradiction that λ i = 0. Then, from Lemma 3.8 we have that u i is constant. On the other hand, we know that u i L 2 0 (), and this implies that u i 0, which is a contradiction since u i is an eigenfunction. This establishes (3.37). From (3.37), up to reordering them, we can suppose that 0 < λ 2 λ 3 ; now, we notice that λ 1 := 0 is an eigenvalue (its eigenfunction is a constant), thanks to Lemma 3.8. Therefore, we have a sequence of eigenvalues 0 = λ 1 < λ 2 λ 3, and its corresponding eigenfunctions are a complete orthogonal system in L 2 (). This concludes the proof of Theorem Remark We point out that the notion of eigenfunctions in Theorem 3.11 is not completely standard. Indeed, the eigenfunctions u i corresponding to the eigenvalues λ i are defined in the whole of R n, but they satisfy an orthogonality conditions only in L 2 (). Alternatively, one can think that the natural domain of definition for u i is itself, since there the eigenvalue equation ( ) s u i = λ i u i takes place, together with the orthogonality condition, and then u i is naturally extended outside via the nonlocal

20 20 SERENA DIPIERRO, XAVIER ROS-OTON, AND ENRICO VALDINOCI Neumann condition. Notice indeed that the condition N s u i = 0 in R n \ is equivalent to prescribing u i outside from the values inside according to the formula u i (y) u i (x) = for any x R n \. In the following proposition we deal with the behavior of the solution of (1.1) at infinity. Proposition Let R n be a bounded domain and let u H s be a weak solution (according to Definition 3.6) of { ( ) s u = f in N s u = 0 in R n \. Then lim u(x) = 1 u uniformly in x. x Proof. First we observe that, since is bounded, there exists R > 0 such that B R. Hence, if y, we have that and so x R x y x + R, 1 R x y 1 + R x x x. Therefore, given ɛ > 0, there exists R > R such that, for any x R, we have x n+2s = 1 + γ(x, y), where γ(x, y) ɛ. Recalling the definition of N s u given in (1.2) and using the fact that N s u = 0 in R n \, we have that for any x R n \ u(y) x n+2s u(y) u(x) = = x n+2s (1 + γ(x, y)) u(y) = (1 + γ(x, y)) u(y) + γ(x, y) u(y) =. + γ(x, y) We set γ 1 (x) := γ(x, y) u(y) and γ 2 (x) := γ(x, y),

21 NONLOCAL PROBLEMS WITH NEUMANN BOUNDARY CONDITIONS 21 and we notice that γ 1 (x) Cɛ and γ 2 (x) ɛ, for some C > 0. Hence, we have that for any x R n \ u(x) u(y) + γ 1 (x) u(y) = u(y) 1 + γ 2 (x) γ 1(x) γ 2 (x) u(y) = 1 + γ 2 (x) C ɛ 1 ɛ. Therefore, sending ɛ 0 (that is, x + ), we obtain the desired result. Remark 3.14 (Interior regularity of solutions). We notice that, in particular, Proposition 3.13 implies that u is bounded at infinity. Thus, if solutions are locally bounded, then one could apply interior regularity results for solutions to ( ) s u = f in (see e.g. [17, 20, 7, 19]). 4. The heat equation Here we show that solutions of the nonlocal heat equation with zero Neumann datum preserve their mass and have energy that decreases in time. To avoid technicalities, we assume that u is a classical solution of problem (1.4), so that we can differentiate under the integral sign. Proposition 4.1. Assume that u(x, t) is a classical solution to (1.4), in the sense that u is bounded and u t + ( ) s u K for all t > 0. Then, for all t > 0, u(x, t) dx = u 0 (x)dx. In other words, the total mass is conserved. Proof. By the dominated convergence theorem, and using Lemma 3.2, we have d u = u t = ( ) s u = N s u = 0. dt R n \ Thus, the quantity u does not depend on t, and the result follows. Proposition 4.2. Assume that u(x, t) is a classical solution to (1.4), in the sense that u is bounded and u t + ( ) s u K for all t > 0. Then, the energy E(t) = is decreasing in time t > 0. R2n\(C)2 u(x, t) u(y, t) 2 dx

22 22 SERENA DIPIERRO, XAVIER ROS-OTON, AND ENRICO VALDINOCI Proof. Let us compute E (t), and we will see that it is negative. Indeed, using Lemma 3.3, E (t) = d R2n\(C)2 u(x, t) u(y, t) 2 dt dx ( )( 2 u(x, t) u(y, t) ut (x, t) u t (y, t) ) = R 2n \(C) 2 dx = 4 u t ( ) s u dx, c n,s where we have used that N s u = 0 in R n \. Thus, using now the equation u t + ( ) s u = 0 in, we find E (t) = 4 ( ) s u 2 dx 0, c n,s with strict inequality unless u is constant. Next we prove that solutions of the nonlocal heat equation with Neumann condition approach a constant as t + : Proposition 4.3. Assume that u(x, t) is a classical solution to (1.4), in the sense that u is bounded and u t + ( ) s u K for all t > 0. Then, u 1 u 0 in L 2 () as t +. Proof. Let m := 1 u 0 be the total mass of u. Define also A(t) := u m 2 dx. Notice that, by Proposition 4.1, we have ( A(t) = u 2 2mu + m 2) dx = Then, by Lemma 3.3, A (t) = 2 u t u dx = 2 u( ) s u dx = c n,s u 2 dx m 2. R 2n \(C) 2 u(x, t) u(y, t) 2 dx. Hence, A is decreasing. Moreover, using the Poincaré inequality in Lemma 3.10 and again Proposition 4.1, we deduce that A (t) c u m 2 dx = c A(t), for some c > 0. Thus, it follows that A(t) e ct A(0),

23 NONLOCAL PROBLEMS WITH NEUMANN BOUNDARY CONDITIONS 23 and thus lim u(x, t) m 2 dx = 0, t + i.e., u converges to m in L 2 (). Notice that, in fact, we have proved that the convergence is exponentially fast. 5. Limits In this section we stu the limits as s 1 and the continuity properties induced by the fractional Neumann condition Limit as s 1. Proposition 5.1. Let R n be any bounded Lipschitz domain. Let u and v be C0 2(Rn ) functions. Then, u lim N s u v = s 1 R n \ ν v. Proof. By Lemma 3.3, we have that N s u v = c n,s (u(x) u(y))(v(x) v(y)) 2 R 2n \(C) 2 dx R n \ Now, we claim that c n,s (u(x) u(y))(v(x) v(y)) lim s 1 2 R 2n \(C) 2 dx = v( ) s u. (5.1) u v. (5.2) We observe that to show (5.2), it is enough to prove that, for any u C0 2(Rn ), c R2n\(C)2 n,s u(x) u(y) 2 lim s 1 2 dx = u 2. (5.3) Indeed, R 2n \(C) 2 (u(x) u(y))(v(x) v(y)) dx = 1 2 R2n\(C)2 (u + v)(x) (u + v)(y) 2 dx R2n\(C)2 u(x) u(y) 2 dx R2n\(C)2 v(x) v(y) 2 dx. Now, we recall that c n,s lim s 1 1 s = 4n, ω n 1 (see Corollary 4.2 in [12]), and so we have to show that lim (1 s) u(x) u(y) 2 s 1 R 2n \(C) 2 dx = ω n 1 u 2. (5.4) 2n

24 24 SERENA DIPIERRO, XAVIER ROS-OTON, AND ENRICO VALDINOCI For this, we first show that lim (1 s) u(x) u(y) s 1 (C) 2 dx = 0. (5.5) Without loss of generality, we can suppose that B r B R, for some 0 < r < R. Since u C 2 0 (Rn ), then (C) u(x) u(y) 2 dx 4 u 2 L (R n ) 4 u 2 L (R n ) which implies (5.5). Hence, (C) 4 u 2 L (R n ) ω n 1 = 4 u 2 L (R n ) ω n 1 B R (CB r) B R B R 1 dx 1 dx dx dx = 4 u 2 ω n 1 r 2s L (R n ) 2s + r + B R = 4 u 2 ωn 1 2 Rn r 2s L (R n ), 2s r dx ρ n 1 ρ n 2s dρ ρ 1 2s dρ lim (1 s) u(x) u(y) 2 s 1 R 2n \(C) 2 dx = lim s 1 (1 s) u(x) u(y) 2 dx = C n u 2, (5.6) where C n > 0 depends only on the dimension, see [5]. In order to determine the constant C n, we take a C 2 -function u supported in. In this case, we have u 2 dx = u 2 dx = R n ξ 2 û(ξ) 2 dξ, R n (5.7) where û is the Fourier transform of u. Moreover, R2n\(C)2 u(x) u(y) 2 dx = R2n u(x) u(y) 2 dx ξ 2s û(ξ) 2 dx, R n = 2 c 1 n,s

25 NONLOCAL PROBLEMS WITH NEUMANN BOUNDARY CONDITIONS 25 thanks to Proposition 3.4 in [12]. Therefore, using Corollary 4.2 in [12] and (5.7), we have lim (1 s) u(x) u(y) 2 s 1 R 2n \(C) 2 dx 2(1 s) = lim ξ 2s û(ξ) 2 dx s 1 c n,s R n = ω n 1 ξ 2 û(ξ) 2 dx 2n R n = ω n 1 u 2 dx. 2n Hence, the constant in (5.6) is C n = ω n 1 2n. This concludes the proof of (5.4), and in turn of (5.2). On the other hand, ( ) s u u uniformly in R n, (see Proposition 4.4 in [12]). This, (5.1) and (5.2) give N s u v = u v + v u = as desired. lim s 1 R n \ u ν v, 5.2. Continuity properties. Following is a continuity result for functions satisfying the nonlocal Neumann condition: Proposition 5.2. Let R n be a domain with C 1 boundary. Let u be continuous in, with N s u = 0 in R n \. Then u is continuous in the whole of R n. Proof. First, let us fix x 0 R n \. Since the latter is an open set, there exists ρ > 0 such that x 0 y ρ for any y. Thus, if x B ρ/2 (x 0 ), we have that x y x 0 y x 0 x ρ/2. Moreover, if x B ρ/2 (x 0 ), we have that x y y x 0 x 0 x y 2 + ( ) ( y y 4 x ρ ) y 2 2, provided that y R := 4 x 0 + 2ρ. As a consequence, for any x B ρ/2 (x 0 ), we have that ( u(y) + 1 2n+2s ( u L () + 1) χbr (y) ρ n+2s + χ ) Rn \B R (y) y n+2s =: ψ(y) and the function ψ belongs to L 1 (R n ). Thus, by the Neumann condition and the Dominated Convergence Theorem, we obtain that u(y) u(y) lim u(x) = lim x 0 y n+2s x x 0 x x 0 = This proves that u is continuous at any points of R n \. = u(x 0 ). x 0 y n+2s

26 26 SERENA DIPIERRO, XAVIER ROS-OTON, AND ENRICO VALDINOCI Now we show the continuity at a point p. We take a sequence p k p as k +. We let q k be the projection of p k to. Since p, we have from the minimizing property of the projection that p k q k = inf p k ξ p k p, ξ and so q k p q k p k + p k p 2 p k p 0 as k +. Therefore, since we alrea know from the assumptions the continuity of u at, we obtain that lim u(q k) = u(p). (5.8) k + Now we claim that lim u(p k) u(q k ) = 0. (5.9) k + To prove it, it is enough to consider the points of the sequence p k that belong to R n \ (since, of course, the points p k belonging to satisfy p k = q k and for them (5.9) is obvious). We define ν k := (p k q k )/ p k q k. Notice that ν k is the exterior normal of at q k. We consider a rigid motion R k such that R k q k = 0 and R k ν k = e n = (0,, 0, 1). Let also h k := p k q k. Notice that h 1 k R kp k = h 1 k R k(p k q k ) = R k ν k = e n. (5.10) Then, the domain k := h 1 k R k has vertical exterior normal at 0 and approaches the halfspace Π := {x n < 0} as k +. Now, we use the Neumann condition at p k and we obtain that u(y) p u(p k ) u(q k ) = k y n+2s u(q k ) p k y n+2s u(y) u(q k ) p = k y n+2s = I 1 + I 2, p k y n+2s with I 1 := and I 2 := B h k (q k ) \B h k (q k ) u(y) u(q k ) p k y n+2s p k y n+2s u(y) u(q k ) p k y n+2s. p k y n+2s

27 NONLOCAL PROBLEMS WITH NEUMANN BOUNDARY CONDITIONS 27 We observe that the uniform continuity of u in gives that As a consequence lim sup k + y B (q h k ) k u(y) u(q k ) = 0. I 1 sup u(y) u(q k ) 0 (5.11) y B (q h k ) k as k +. Moreover, exploiting the change of variable η := h 1 k R ky and recalling (5.10), we obtain that u(y) u(q k ) \B h (q k ) p k y n+2s I 2 k p k y n+2s Notice that, if η k \ B 1/ hk 2 u L () = 2 u L () then \B h k (q k ) k \B 1/ hk k p k y n+2s p k y n+2s e n η n+2s. e n η n+2s e n η n+2s = e n η n+s e n η s e n η n+s( η 1 ) s for large k. Therefore Since e n η n+s( ) h 1/2 s k 1 en η n+s h s/4 I 2 2h s/4 k lim k + k k u L () k k e n η n+s = e n η n+2s e n η n+s. e n η n+2s Π Π k e n η n+s, e n η n+2s we conlude that I 2 0 as k +. This and (5.11) imply (5.9). From (5.8) and (5.9), we conclude that hence u is continuous at p. lim u(p k) = u(p), k +

28 28 SERENA DIPIERRO, XAVIER ROS-OTON, AND ENRICO VALDINOCI As a direct consequence of Proposition 5.2 we obtain: Corollary 5.3. Let R n be a domain with C 1 boundary. Let v 0 C(R n ). Let v 0 (x) if x, v 0 (y) v(x) := x y n+2s if x Rn \. Then v C(R n ) and it satisfies v = v 0 in and N s v = 0 in R n \. Proof. By construction, v = v 0 in and N s v = 0 in R n \. Then we can use Proposition 5.2 and obtain that v C(R n ). Now we stu the boundary behavior of the nonlocal Neumann function Ñsu. Proposition 5.4. Let R n be a C 1 domain, and u C(R n ). Then, for all s (0, 1), lim Ñ s u(x) = 0. (5.12) x x R n \ Also, if s > 1 2 and u C1,α (R n ) for some α (0, 2s 1), then Ñ s u(x + ɛν) ν Ñ s u(x) := lim ɛ 0 + ɛ for some constant κ > 0. = κ ν u for any x, (5.13) Proof. Let x k be a sequence in R n \ such that x k x as k +. By Corollary 5.3 (applied here with v 0 := u), there exists v C(R n ) such that v = u in and N s v = 0 in R n \. By the continuity of u and v we have that Moreover This and (5.14) imply that that is (5.12). lim u(x k) v(x k ) = u(x ) v(x ) = 0. (5.14) k + Ñ s u(x k ) = Ñsu(x k ) Ñsv(x k ) u(x k ) u(y) v(x k ) v(y) x = k y n+2s x k y n+2s x k y n+2s u(x k ) v(x k ) x = k y n+2s x k y n+2s = u(x k ) v(x k ). lim Ñ s u(x k ) = 0, k +

29 NONLOCAL PROBLEMS WITH NEUMANN BOUNDARY CONDITIONS 29 Now, we prove (5.13). For this, we suppose that s > 1 2, that 0 and that the exterior normal ν coincides with e n = (0,, 0, 1); then we use (5.12) and the change of variable η := ɛ 1 y in the following computation: ɛ 1( ) Ñ s u(ɛe n ) Ñsu(0) = ɛ 1 Ñ s u(ɛe n ) ɛ 1 u(ɛe n ) u(y) ɛe = n y n+2s ɛe n y n+2s ɛ 1 u(ɛe n ) u(ɛη) 1 ɛ = e n η n+2s = I 1 + I 2, e n η n+2s 1 ɛ where I 1 := and I 2 := u(ɛe n ) (e n η) 1 ɛ e n η n+2s e n η n+2s ɛ 1 1 ɛ u(ɛe n ) u(ɛη) ɛ u(ɛe n ) (e n η) 1 ɛ e n η n+2s. e n η n+2s 1 ɛ So, if Π := {x n < 0}, we have that lim I 1 = ɛ 0 + = Π Π u(0) (e n η) e n η n+2s Π e n η n+2s n u(0)(1 η n ) e n η n+2s, e n η n+2s Π where we have used that, for any i {1,, n 1} the map η its integral averages to zero. So, we can write (1 η n ) lim I Π e 1 = κ n u(0) with κ := n η ɛ 0 + Π n+2s iu(0) η i e n η n+2s is odd and so. (5.15) e n η n+2s

30 30 SERENA DIPIERRO, XAVIER ROS-OTON, AND ENRICO VALDINOCI We remark that κ is finite, since s > 1 2. Moreover ɛ 1 u(ɛen ) u(ɛη) ɛ u(ɛe n ) (e n η) 1 ( ) = u(tɛe n + (1 t)ɛη) u(ɛe n ) (e n η) dt As a consequence ɛ α I 2 0 u C 1,α(R n ) e n η 1 u C 1,α(R n )ɛ α e n η 1+α. u C 1,α(R n ) 1 ɛ 1 ɛ 0 e n η n+2s 1 α e n η n+2s tɛe n + (1 t)ɛη ɛe n α dt u C 1,α(R n ) Π Π e n η n+2s 1 α e n η n+2s as ɛ 0, which is finite, thanks to our assumptions on α. This shows that I 2 0 as ɛ 0. Hence, recalling (5.15), we get that ) lim ɛ 1( Ñ s u(ɛe n ) Ñsu(0) = κ n u(0), ɛ 0 + which establishes (5.13). 6. An overdetermined problem In this section we consider an overdetemined problem. For this, we will use the renormalized nonlocal Neumann condition that has been introduced in Remark 3.4. Indeed, as we pointed out in Remark 3.5, this is natural if one considers nonhomogeneous Neumann conditions. Theorem 6.1. Let R n be a bounded and Lipschitz domain. Then there exists no function u C(R n ) satisfying { u(x) = 0 for any x R n \ Ñ s u(x) = 1 for any x R n (6.1) \. Remark 6.2. We notice that u = χ satisfies (6.1), but it is a discontinuous function. Proof. Without loss of generality, we can suppose that 0. We argue by contradiction and we assume that there exists a continuous function u that satisfies (6.1). Therefore, there exists δ > 0 such that u 1/2 in B δ. (6.2) Since is Lipschitz, up to choosing δ small enough, we have that B δ = B δ, where := {x = (x, x n ) R n 1 R s.t. x n < γ(x )} for a suitable Lipschitz function γ : R n 1 R such that γ(0) = 0 and x γ(0) = 0. Now we let x := ɛ e n R n \, for suitable ɛ > 0 sufficiently small. We observe that u(ɛ e n ) = 0. (6.3)

31 NONLOCAL PROBLEMS WITH NEUMANN BOUNDARY CONDITIONS 31 Moreover we consider the set { 1 ɛ = y = (y, y n ) R n 1 R s.t. y n < 1 } ɛ γ(ɛy ). We also define K := { y = (y, y n ) R n 1 R s.t. y n < L y }, where L is the Lipschitz constant of γ. We claim that K ɛ 1. (6.4) Indeed, since γ is Lipschitz and 0, we have that and so, if y K, γ(ɛy ) = γ(ɛy ) + γ(0) L ɛ y, y n L y 1 ɛ γ(ɛy ), which implies that y ɛ 1. This shows (6.4). Now we define Σ ɛ := ɛe n y n+2s, and we observe that B δ B δ u(y) u(ɛ e n ) ɛe n y n+2s 1 2 Σ ɛ, (6.5) thanks to (6.3) and (6.2). Furthermore, if y R n \ B δ and ɛ δ/2, we have which implies that u(y) u(ɛ e n ) C \B δ ɛe n y n+2s y ɛe n y ɛ y 2, \B δ up to renaming the constants. On the othe hand, we have that ɛe n y n+2s Finally, we observe that ɛ 2s Σ ɛ =ɛ 2s = =:κ, ɛe n y n+2s C R n \B δ B δ B δ B δ/ɛ (ɛ 1 ) B δ/ɛ K y n+2s = C δ 2s, (6.6) ɛe n y n+2s = Σ ɛ. (6.7) ɛe n y n+2s dz e n z n+2s dz e n z n+2s where we have used the change of variable y = ɛz and (6.4). (6.8)

32 32 SERENA DIPIERRO, XAVIER ROS-OTON, AND ENRICO VALDINOCI Hence, using the second condition in (6.1) and putting together (6.5), (6.6), (6.7) and (6.8), we obtain 0 = ɛe n y n+2s u(ɛe n ) u(x) ɛe n y n+2s = ɛe n y n+2s Σ ɛ 1 2 Σ ɛ C δ 2s u(ɛe n ) u(x) B δ ɛe n y n+2s u(ɛe n ) u(x) \B δ ɛe n y n+2s = 1 2 Σ ɛ C δ 2s ( ) ɛ = ɛ 2s 2s 2 Σ ɛ C ɛ 2s δ 2s ( ɛ 2s κ 2 C ɛ2s δ 2s) > 0 if ɛ is sufficiently small. This gives a contradiction and concludes the proof. 7. Comparison with previous works In this last section we compare our new Neumann nonlocal conditions with the previous works in the literature that also deal with Neumann-type conditions for the fractional Laplacian ( ) s. The idea of [4, 8] (and also [9, 10, 11]) is to consider the regional fractional Laplacian, associated to the Dirichlet form ( )( ) u(x) u(y) v(x) v(y) c n,s dx. (7.1) This operator corresponds to a censored process, i.e., a process whose jumps are restricted to be in. The operator can be defined in general domains, and seems to give a natural analogue of homogeneous Neumann condition. However, no nonhomogeneous Neumann conditions can be considered with this model, and the operator depends on the domain. On the other hand, in [1, 3] the usual diffusion associated to the fractional Laplacian (1.3) was considered inside, and thus the particle can jump outside. When it jumps outside, then it is reflected or projected inside in a deterministic way. Of course, different types of reflections or projections lead to different Neumann conditions. To appropriately define these reflections, some assumptions on the domain (like smoothness or convexity) need to be done. In contrast with the regional fractional Laplacian, this problem does not have a variational formulation and everything is done in the context of viscosity solutions. Finally, in [15] a different Neumann problem for the fractional Laplacian was considered. Solutions to this type of Neumann problems are large solutions, in the sense that they are not bounded in a neighborhood of. More precisely, it is proved in [15] that the following problem is well-posed ( ) s u = f in u = 0 in R n \ ν ( u/d s 1 ) = g on,

Metric Spaces. Chapter 7. 7.1. Metrics

Metric Spaces. Chapter 7. 7.1. Metrics Chapter 7 Metric Spaces A metric space is a set X that has a notion of the distance d(x, y) between every pair of points x, y X. The purpose of this chapter is to introduce metric spaces and give some

More information

An Introduction to Partial Differential Equations

An Introduction to Partial Differential Equations An Introduction to Partial Differential Equations Andrew J. Bernoff LECTURE 2 Cooling of a Hot Bar: The Diffusion Equation 2.1. Outline of Lecture An Introduction to Heat Flow Derivation of the Diffusion

More information

No: 10 04. Bilkent University. Monotonic Extension. Farhad Husseinov. Discussion Papers. Department of Economics

No: 10 04. Bilkent University. Monotonic Extension. Farhad Husseinov. Discussion Papers. Department of Economics No: 10 04 Bilkent University Monotonic Extension Farhad Husseinov Discussion Papers Department of Economics The Discussion Papers of the Department of Economics are intended to make the initial results

More information

MATH 425, PRACTICE FINAL EXAM SOLUTIONS.

MATH 425, PRACTICE FINAL EXAM SOLUTIONS. MATH 45, PRACTICE FINAL EXAM SOLUTIONS. Exercise. a Is the operator L defined on smooth functions of x, y by L u := u xx + cosu linear? b Does the answer change if we replace the operator L by the operator

More information

1 Completeness of a Set of Eigenfunctions. Lecturer: Naoki Saito Scribe: Alexander Sheynis/Allen Xue. May 3, 2007. 1.1 The Neumann Boundary Condition

1 Completeness of a Set of Eigenfunctions. Lecturer: Naoki Saito Scribe: Alexander Sheynis/Allen Xue. May 3, 2007. 1.1 The Neumann Boundary Condition MAT 280: Laplacian Eigenfunctions: Theory, Applications, and Computations Lecture 11: Laplacian Eigenvalue Problems for General Domains III. Completeness of a Set of Eigenfunctions and the Justification

More information

BANACH AND HILBERT SPACE REVIEW

BANACH AND HILBERT SPACE REVIEW BANACH AND HILBET SPACE EVIEW CHISTOPHE HEIL These notes will briefly review some basic concepts related to the theory of Banach and Hilbert spaces. We are not trying to give a complete development, but

More information

1 if 1 x 0 1 if 0 x 1

1 if 1 x 0 1 if 0 x 1 Chapter 3 Continuity In this chapter we begin by defining the fundamental notion of continuity for real valued functions of a single real variable. When trying to decide whether a given function is or

More information

Separation Properties for Locally Convex Cones

Separation Properties for Locally Convex Cones Journal of Convex Analysis Volume 9 (2002), No. 1, 301 307 Separation Properties for Locally Convex Cones Walter Roth Department of Mathematics, Universiti Brunei Darussalam, Gadong BE1410, Brunei Darussalam

More information

The Heat Equation. Lectures INF2320 p. 1/88

The Heat Equation. Lectures INF2320 p. 1/88 The Heat Equation Lectures INF232 p. 1/88 Lectures INF232 p. 2/88 The Heat Equation We study the heat equation: u t = u xx for x (,1), t >, (1) u(,t) = u(1,t) = for t >, (2) u(x,) = f(x) for x (,1), (3)

More information

Parabolic Equations. Chapter 5. Contents. 5.1.2 Well-Posed Initial-Boundary Value Problem. 5.1.3 Time Irreversibility of the Heat Equation

Parabolic Equations. Chapter 5. Contents. 5.1.2 Well-Posed Initial-Boundary Value Problem. 5.1.3 Time Irreversibility of the Heat Equation 7 5.1 Definitions Properties Chapter 5 Parabolic Equations Note that we require the solution u(, t bounded in R n for all t. In particular we assume that the boundedness of the smooth function u at infinity

More information

MASSACHUSETTS INSTITUTE OF TECHNOLOGY 6.436J/15.085J Fall 2008 Lecture 5 9/17/2008 RANDOM VARIABLES

MASSACHUSETTS INSTITUTE OF TECHNOLOGY 6.436J/15.085J Fall 2008 Lecture 5 9/17/2008 RANDOM VARIABLES MASSACHUSETTS INSTITUTE OF TECHNOLOGY 6.436J/15.085J Fall 2008 Lecture 5 9/17/2008 RANDOM VARIABLES Contents 1. Random variables and measurable functions 2. Cumulative distribution functions 3. Discrete

More information

A PRIORI ESTIMATES FOR SEMISTABLE SOLUTIONS OF SEMILINEAR ELLIPTIC EQUATIONS. In memory of Rou-Huai Wang

A PRIORI ESTIMATES FOR SEMISTABLE SOLUTIONS OF SEMILINEAR ELLIPTIC EQUATIONS. In memory of Rou-Huai Wang A PRIORI ESTIMATES FOR SEMISTABLE SOLUTIONS OF SEMILINEAR ELLIPTIC EQUATIONS XAVIER CABRÉ, MANEL SANCHÓN, AND JOEL SPRUCK In memory of Rou-Huai Wang 1. Introduction In this note we consider semistable

More information

0 <β 1 let u(x) u(y) kuk u := sup u(x) and [u] β := sup

0 <β 1 let u(x) u(y) kuk u := sup u(x) and [u] β := sup 456 BRUCE K. DRIVER 24. Hölder Spaces Notation 24.1. Let Ω be an open subset of R d,bc(ω) and BC( Ω) be the bounded continuous functions on Ω and Ω respectively. By identifying f BC( Ω) with f Ω BC(Ω),

More information

INDISTINGUISHABILITY OF ABSOLUTELY CONTINUOUS AND SINGULAR DISTRIBUTIONS

INDISTINGUISHABILITY OF ABSOLUTELY CONTINUOUS AND SINGULAR DISTRIBUTIONS INDISTINGUISHABILITY OF ABSOLUTELY CONTINUOUS AND SINGULAR DISTRIBUTIONS STEVEN P. LALLEY AND ANDREW NOBEL Abstract. It is shown that there are no consistent decision rules for the hypothesis testing problem

More information

Pacific Journal of Mathematics

Pacific Journal of Mathematics Pacific Journal of Mathematics GLOBAL EXISTENCE AND DECREASING PROPERTY OF BOUNDARY VALUES OF SOLUTIONS TO PARABOLIC EQUATIONS WITH NONLOCAL BOUNDARY CONDITIONS Sangwon Seo Volume 193 No. 1 March 2000

More information

Reference: Introduction to Partial Differential Equations by G. Folland, 1995, Chap. 3.

Reference: Introduction to Partial Differential Equations by G. Folland, 1995, Chap. 3. 5 Potential Theory Reference: Introduction to Partial Differential Equations by G. Folland, 995, Chap. 3. 5. Problems of Interest. In what follows, we consider Ω an open, bounded subset of R n with C 2

More information

Høgskolen i Narvik Sivilingeniørutdanningen STE6237 ELEMENTMETODER. Oppgaver

Høgskolen i Narvik Sivilingeniørutdanningen STE6237 ELEMENTMETODER. Oppgaver Høgskolen i Narvik Sivilingeniørutdanningen STE637 ELEMENTMETODER Oppgaver Klasse: 4.ID, 4.IT Ekstern Professor: Gregory A. Chechkin e-mail: chechkin@mech.math.msu.su Narvik 6 PART I Task. Consider two-point

More information

FUNCTIONAL ANALYSIS LECTURE NOTES: QUOTIENT SPACES

FUNCTIONAL ANALYSIS LECTURE NOTES: QUOTIENT SPACES FUNCTIONAL ANALYSIS LECTURE NOTES: QUOTIENT SPACES CHRISTOPHER HEIL 1. Cosets and the Quotient Space Any vector space is an abelian group under the operation of vector addition. So, if you are have studied

More information

CHAPTER IV - BROWNIAN MOTION

CHAPTER IV - BROWNIAN MOTION CHAPTER IV - BROWNIAN MOTION JOSEPH G. CONLON 1. Construction of Brownian Motion There are two ways in which the idea of a Markov chain on a discrete state space can be generalized: (1) The discrete time

More information

Inner Product Spaces

Inner Product Spaces Math 571 Inner Product Spaces 1. Preliminaries An inner product space is a vector space V along with a function, called an inner product which associates each pair of vectors u, v with a scalar u, v, and

More information

t := maxγ ν subject to ν {0,1,2,...} and f(x c +γ ν d) f(x c )+cγ ν f (x c ;d).

t := maxγ ν subject to ν {0,1,2,...} and f(x c +γ ν d) f(x c )+cγ ν f (x c ;d). 1. Line Search Methods Let f : R n R be given and suppose that x c is our current best estimate of a solution to P min x R nf(x). A standard method for improving the estimate x c is to choose a direction

More information

Metric Spaces Joseph Muscat 2003 (Last revised May 2009)

Metric Spaces Joseph Muscat 2003 (Last revised May 2009) 1 Distance J Muscat 1 Metric Spaces Joseph Muscat 2003 (Last revised May 2009) (A revised and expanded version of these notes are now published by Springer.) 1 Distance A metric space can be thought of

More information

Some stability results of parameter identification in a jump diffusion model

Some stability results of parameter identification in a jump diffusion model Some stability results of parameter identification in a jump diffusion model D. Düvelmeyer Technische Universität Chemnitz, Fakultät für Mathematik, 09107 Chemnitz, Germany Abstract In this paper we discuss

More information

4 Lyapunov Stability Theory

4 Lyapunov Stability Theory 4 Lyapunov Stability Theory In this section we review the tools of Lyapunov stability theory. These tools will be used in the next section to analyze the stability properties of a robot controller. We

More information

SOLUTIONS TO EXERCISES FOR. MATHEMATICS 205A Part 3. Spaces with special properties

SOLUTIONS TO EXERCISES FOR. MATHEMATICS 205A Part 3. Spaces with special properties SOLUTIONS TO EXERCISES FOR MATHEMATICS 205A Part 3 Fall 2008 III. Spaces with special properties III.1 : Compact spaces I Problems from Munkres, 26, pp. 170 172 3. Show that a finite union of compact subspaces

More information

Notes on metric spaces

Notes on metric spaces Notes on metric spaces 1 Introduction The purpose of these notes is to quickly review some of the basic concepts from Real Analysis, Metric Spaces and some related results that will be used in this course.

More information

1. Let P be the space of all polynomials (of one real variable and with real coefficients) with the norm

1. Let P be the space of all polynomials (of one real variable and with real coefficients) with the norm Uppsala Universitet Matematiska Institutionen Andreas Strömbergsson Prov i matematik Funktionalanalys Kurs: F3B, F4Sy, NVP 005-06-15 Skrivtid: 9 14 Tillåtna hjälpmedel: Manuella skrivdon, Kreyszigs bok

More information

EXISTENCE AND NON-EXISTENCE RESULTS FOR A NONLINEAR HEAT EQUATION

EXISTENCE AND NON-EXISTENCE RESULTS FOR A NONLINEAR HEAT EQUATION Sixth Mississippi State Conference on Differential Equations and Computational Simulations, Electronic Journal of Differential Equations, Conference 5 (7), pp. 5 65. ISSN: 7-669. UL: http://ejde.math.txstate.edu

More information

UNIVERSITETET I OSLO

UNIVERSITETET I OSLO NIVERSITETET I OSLO Det matematisk-naturvitenskapelige fakultet Examination in: Trial exam Partial differential equations and Sobolev spaces I. Day of examination: November 18. 2009. Examination hours:

More information

Metric Spaces. Chapter 1

Metric Spaces. Chapter 1 Chapter 1 Metric Spaces Many of the arguments you have seen in several variable calculus are almost identical to the corresponding arguments in one variable calculus, especially arguments concerning convergence

More information

RESONANCES AND BALLS IN OBSTACLE SCATTERING WITH NEUMANN BOUNDARY CONDITIONS

RESONANCES AND BALLS IN OBSTACLE SCATTERING WITH NEUMANN BOUNDARY CONDITIONS RESONANCES AND BALLS IN OBSTACLE SCATTERING WITH NEUMANN BOUNDARY CONDITIONS T. J. CHRISTIANSEN Abstract. We consider scattering by an obstacle in R d, d 3 odd. We show that for the Neumann Laplacian if

More information

Extremal equilibria for reaction diffusion equations in bounded domains and applications.

Extremal equilibria for reaction diffusion equations in bounded domains and applications. Extremal equilibria for reaction diffusion equations in bounded domains and applications. Aníbal Rodríguez-Bernal Alejandro Vidal-López Departamento de Matemática Aplicada Universidad Complutense de Madrid,

More information

Scalar Valued Functions of Several Variables; the Gradient Vector

Scalar Valued Functions of Several Variables; the Gradient Vector Scalar Valued Functions of Several Variables; the Gradient Vector Scalar Valued Functions vector valued function of n variables: Let us consider a scalar (i.e., numerical, rather than y = φ(x = φ(x 1,

More information

Math 4310 Handout - Quotient Vector Spaces

Math 4310 Handout - Quotient Vector Spaces Math 4310 Handout - Quotient Vector Spaces Dan Collins The textbook defines a subspace of a vector space in Chapter 4, but it avoids ever discussing the notion of a quotient space. This is understandable

More information

The Ergodic Theorem and randomness

The Ergodic Theorem and randomness The Ergodic Theorem and randomness Peter Gács Department of Computer Science Boston University March 19, 2008 Peter Gács (Boston University) Ergodic theorem March 19, 2008 1 / 27 Introduction Introduction

More information

TOPIC 4: DERIVATIVES

TOPIC 4: DERIVATIVES TOPIC 4: DERIVATIVES 1. The derivative of a function. Differentiation rules 1.1. The slope of a curve. The slope of a curve at a point P is a measure of the steepness of the curve. If Q is a point on the

More information

Fuzzy Differential Systems and the New Concept of Stability

Fuzzy Differential Systems and the New Concept of Stability Nonlinear Dynamics and Systems Theory, 1(2) (2001) 111 119 Fuzzy Differential Systems and the New Concept of Stability V. Lakshmikantham 1 and S. Leela 2 1 Department of Mathematical Sciences, Florida

More information

CHAPTER II THE LIMIT OF A SEQUENCE OF NUMBERS DEFINITION OF THE NUMBER e.

CHAPTER II THE LIMIT OF A SEQUENCE OF NUMBERS DEFINITION OF THE NUMBER e. CHAPTER II THE LIMIT OF A SEQUENCE OF NUMBERS DEFINITION OF THE NUMBER e. This chapter contains the beginnings of the most important, and probably the most subtle, notion in mathematical analysis, i.e.,

More information

Online Appendix to Stochastic Imitative Game Dynamics with Committed Agents

Online Appendix to Stochastic Imitative Game Dynamics with Committed Agents Online Appendix to Stochastic Imitative Game Dynamics with Committed Agents William H. Sandholm January 6, 22 O.. Imitative protocols, mean dynamics, and equilibrium selection In this section, we consider

More information

THE EIGENVALUES OF THE LAPLACIAN ON DOMAINS WITH SMALL SLITS

THE EIGENVALUES OF THE LAPLACIAN ON DOMAINS WITH SMALL SLITS THE EIGENVALUES OF THE LAPLACIAN ON DOMAINS WITH SMALL SLITS LUC HILLAIRET AND CHRIS JUDGE Abstract. We introduce a small slit into a planar domain and study the resulting effect upon the eigenvalues of

More information

Modern Optimization Methods for Big Data Problems MATH11146 The University of Edinburgh

Modern Optimization Methods for Big Data Problems MATH11146 The University of Edinburgh Modern Optimization Methods for Big Data Problems MATH11146 The University of Edinburgh Peter Richtárik Week 3 Randomized Coordinate Descent With Arbitrary Sampling January 27, 2016 1 / 30 The Problem

More information

Elasticity Theory Basics

Elasticity Theory Basics G22.3033-002: Topics in Computer Graphics: Lecture #7 Geometric Modeling New York University Elasticity Theory Basics Lecture #7: 20 October 2003 Lecturer: Denis Zorin Scribe: Adrian Secord, Yotam Gingold

More information

Duality of linear conic problems

Duality of linear conic problems Duality of linear conic problems Alexander Shapiro and Arkadi Nemirovski Abstract It is well known that the optimal values of a linear programming problem and its dual are equal to each other if at least

More information

Geometrical Characterization of RN-operators between Locally Convex Vector Spaces

Geometrical Characterization of RN-operators between Locally Convex Vector Spaces Geometrical Characterization of RN-operators between Locally Convex Vector Spaces OLEG REINOV St. Petersburg State University Dept. of Mathematics and Mechanics Universitetskii pr. 28, 198504 St, Petersburg

More information

KATO S INEQUALITY UP TO THE BOUNDARY. Contents 1. Introduction 1 2. Properties of functions in X 4 3. Proof of Theorem 1.1 6 4.

KATO S INEQUALITY UP TO THE BOUNDARY. Contents 1. Introduction 1 2. Properties of functions in X 4 3. Proof of Theorem 1.1 6 4. KATO S INEQUALITY UP TO THE BOUNDARY HAÏM BREZIS(1),(2) AND AUGUSTO C. PONCE (3) Abstract. We show that if u is a finite measure in then, under suitable assumptions on u near, u + is also a finite measure

More information

5.3 Improper Integrals Involving Rational and Exponential Functions

5.3 Improper Integrals Involving Rational and Exponential Functions Section 5.3 Improper Integrals Involving Rational and Exponential Functions 99.. 3. 4. dθ +a cos θ =, < a

More information

Undergraduate Notes in Mathematics. Arkansas Tech University Department of Mathematics

Undergraduate Notes in Mathematics. Arkansas Tech University Department of Mathematics Undergraduate Notes in Mathematics Arkansas Tech University Department of Mathematics An Introductory Single Variable Real Analysis: A Learning Approach through Problem Solving Marcel B. Finan c All Rights

More information

ON NONNEGATIVE SOLUTIONS OF NONLINEAR TWO-POINT BOUNDARY VALUE PROBLEMS FOR TWO-DIMENSIONAL DIFFERENTIAL SYSTEMS WITH ADVANCED ARGUMENTS

ON NONNEGATIVE SOLUTIONS OF NONLINEAR TWO-POINT BOUNDARY VALUE PROBLEMS FOR TWO-DIMENSIONAL DIFFERENTIAL SYSTEMS WITH ADVANCED ARGUMENTS ON NONNEGATIVE SOLUTIONS OF NONLINEAR TWO-POINT BOUNDARY VALUE PROBLEMS FOR TWO-DIMENSIONAL DIFFERENTIAL SYSTEMS WITH ADVANCED ARGUMENTS I. KIGURADZE AND N. PARTSVANIA A. Razmadze Mathematical Institute

More information

1 Lecture 3: Operators in Quantum Mechanics

1 Lecture 3: Operators in Quantum Mechanics 1 Lecture 3: Operators in Quantum Mechanics 1.1 Basic notions of operator algebra. In the previous lectures we have met operators: ˆx and ˆp = i h they are called fundamental operators. Many operators

More information

Class Meeting # 1: Introduction to PDEs

Class Meeting # 1: Introduction to PDEs MATH 18.152 COURSE NOTES - CLASS MEETING # 1 18.152 Introduction to PDEs, Fall 2011 Professor: Jared Speck Class Meeting # 1: Introduction to PDEs 1. What is a PDE? We will be studying functions u = u(x

More information

Mathematical Finance

Mathematical Finance Mathematical Finance Option Pricing under the Risk-Neutral Measure Cory Barnes Department of Mathematics University of Washington June 11, 2013 Outline 1 Probability Background 2 Black Scholes for European

More information

2.3 Convex Constrained Optimization Problems

2.3 Convex Constrained Optimization Problems 42 CHAPTER 2. FUNDAMENTAL CONCEPTS IN CONVEX OPTIMIZATION Theorem 15 Let f : R n R and h : R R. Consider g(x) = h(f(x)) for all x R n. The function g is convex if either of the following two conditions

More information

Lecture 10. Finite difference and finite element methods. Option pricing Sensitivity analysis Numerical examples

Lecture 10. Finite difference and finite element methods. Option pricing Sensitivity analysis Numerical examples Finite difference and finite element methods Lecture 10 Sensitivities and Greeks Key task in financial engineering: fast and accurate calculation of sensitivities of market models with respect to model

More information

ALMOST COMMON PRIORS 1. INTRODUCTION

ALMOST COMMON PRIORS 1. INTRODUCTION ALMOST COMMON PRIORS ZIV HELLMAN ABSTRACT. What happens when priors are not common? We introduce a measure for how far a type space is from having a common prior, which we term prior distance. If a type

More information

Finite dimensional C -algebras

Finite dimensional C -algebras Finite dimensional C -algebras S. Sundar September 14, 2012 Throughout H, K stand for finite dimensional Hilbert spaces. 1 Spectral theorem for self-adjoint opertors Let A B(H) and let {ξ 1, ξ 2,, ξ n

More information

Roughness effect on the Neumann boundary condition

Roughness effect on the Neumann boundary condition Roughness effect on the Neumann boundary condition Laurent Chupin 1 Abstract We study the effect of a periodic roughness on a Neumann boundary condition. We show that, as in the case of a Dirichlet boundary

More information

1. Prove that the empty set is a subset of every set.

1. Prove that the empty set is a subset of every set. 1. Prove that the empty set is a subset of every set. Basic Topology Written by Men-Gen Tsai email: b89902089@ntu.edu.tw Proof: For any element x of the empty set, x is also an element of every set since

More information

(Basic definitions and properties; Separation theorems; Characterizations) 1.1 Definition, examples, inner description, algebraic properties

(Basic definitions and properties; Separation theorems; Characterizations) 1.1 Definition, examples, inner description, algebraic properties Lecture 1 Convex Sets (Basic definitions and properties; Separation theorems; Characterizations) 1.1 Definition, examples, inner description, algebraic properties 1.1.1 A convex set In the school geometry

More information

Continued Fractions and the Euclidean Algorithm

Continued Fractions and the Euclidean Algorithm Continued Fractions and the Euclidean Algorithm Lecture notes prepared for MATH 326, Spring 997 Department of Mathematics and Statistics University at Albany William F Hammond Table of Contents Introduction

More information

Finite dimensional topological vector spaces

Finite dimensional topological vector spaces Chapter 3 Finite dimensional topological vector spaces 3.1 Finite dimensional Hausdorff t.v.s. Let X be a vector space over the field K of real or complex numbers. We know from linear algebra that the

More information

Numerical Verification of Optimality Conditions in Optimal Control Problems

Numerical Verification of Optimality Conditions in Optimal Control Problems Numerical Verification of Optimality Conditions in Optimal Control Problems Dissertation zur Erlangung des naturwissenschaftlichen Doktorgrades der Julius-Maximilians-Universität Würzburg vorgelegt von

More information

Stochastic Inventory Control

Stochastic Inventory Control Chapter 3 Stochastic Inventory Control 1 In this chapter, we consider in much greater details certain dynamic inventory control problems of the type already encountered in section 1.3. In addition to the

More information

MICROLOCAL ANALYSIS OF THE BOCHNER-MARTINELLI INTEGRAL

MICROLOCAL ANALYSIS OF THE BOCHNER-MARTINELLI INTEGRAL PROCEEDINGS OF THE AMERICAN MATHEMATICAL SOCIETY Volume 00, Number 0, Pages 000 000 S 0002-9939(XX)0000-0 MICROLOCAL ANALYSIS OF THE BOCHNER-MARTINELLI INTEGRAL NIKOLAI TARKHANOV AND NIKOLAI VASILEVSKI

More information

Adaptive Online Gradient Descent

Adaptive Online Gradient Descent Adaptive Online Gradient Descent Peter L Bartlett Division of Computer Science Department of Statistics UC Berkeley Berkeley, CA 94709 bartlett@csberkeleyedu Elad Hazan IBM Almaden Research Center 650

More information

Introduction to the Finite Element Method

Introduction to the Finite Element Method Introduction to the Finite Element Method 09.06.2009 Outline Motivation Partial Differential Equations (PDEs) Finite Difference Method (FDM) Finite Element Method (FEM) References Motivation Figure: cross

More information

Practical Guide to the Simplex Method of Linear Programming

Practical Guide to the Simplex Method of Linear Programming Practical Guide to the Simplex Method of Linear Programming Marcel Oliver Revised: April, 0 The basic steps of the simplex algorithm Step : Write the linear programming problem in standard form Linear

More information

Conductance, the Normalized Laplacian, and Cheeger s Inequality

Conductance, the Normalized Laplacian, and Cheeger s Inequality Spectral Graph Theory Lecture 6 Conductance, the Normalized Laplacian, and Cheeger s Inequality Daniel A. Spielman September 21, 2015 Disclaimer These notes are not necessarily an accurate representation

More information

DIRICHLET S PROBLEM WITH ENTIRE DATA POSED ON AN ELLIPSOIDAL CYLINDER. 1. Introduction

DIRICHLET S PROBLEM WITH ENTIRE DATA POSED ON AN ELLIPSOIDAL CYLINDER. 1. Introduction DIRICHLET S PROBLEM WITH ENTIRE DATA POSED ON AN ELLIPSOIDAL CYLINDER DMITRY KHAVINSON, ERIK LUNDBERG, HERMANN RENDER. Introduction A function u is said to be harmonic if u := n j= 2 u = 0. Given a domain

More information

Lecture L3 - Vectors, Matrices and Coordinate Transformations

Lecture L3 - Vectors, Matrices and Coordinate Transformations S. Widnall 16.07 Dynamics Fall 2009 Lecture notes based on J. Peraire Version 2.0 Lecture L3 - Vectors, Matrices and Coordinate Transformations By using vectors and defining appropriate operations between

More information

EXIT TIME PROBLEMS AND ESCAPE FROM A POTENTIAL WELL

EXIT TIME PROBLEMS AND ESCAPE FROM A POTENTIAL WELL EXIT TIME PROBLEMS AND ESCAPE FROM A POTENTIAL WELL Exit Time problems and Escape from a Potential Well Escape From a Potential Well There are many systems in physics, chemistry and biology that exist

More information

ON SEQUENTIAL CONTINUITY OF COMPOSITION MAPPING. 0. Introduction

ON SEQUENTIAL CONTINUITY OF COMPOSITION MAPPING. 0. Introduction ON SEQUENTIAL CONTINUITY OF COMPOSITION MAPPING Abstract. In [1] there was proved a theorem concerning the continuity of the composition mapping, and there was announced a theorem on sequential continuity

More information

16.3 Fredholm Operators

16.3 Fredholm Operators Lectures 16 and 17 16.3 Fredholm Operators A nice way to think about compact operators is to show that set of compact operators is the closure of the set of finite rank operator in operator norm. In this

More information

Discrete Mathematics and Probability Theory Fall 2009 Satish Rao, David Tse Note 18. A Brief Introduction to Continuous Probability

Discrete Mathematics and Probability Theory Fall 2009 Satish Rao, David Tse Note 18. A Brief Introduction to Continuous Probability CS 7 Discrete Mathematics and Probability Theory Fall 29 Satish Rao, David Tse Note 8 A Brief Introduction to Continuous Probability Up to now we have focused exclusively on discrete probability spaces

More information

Lecture 4: BK inequality 27th August and 6th September, 2007

Lecture 4: BK inequality 27th August and 6th September, 2007 CSL866: Percolation and Random Graphs IIT Delhi Amitabha Bagchi Scribe: Arindam Pal Lecture 4: BK inequality 27th August and 6th September, 2007 4. Preliminaries The FKG inequality allows us to lower bound

More information

Section 6.1 - Inner Products and Norms

Section 6.1 - Inner Products and Norms Section 6.1 - Inner Products and Norms Definition. Let V be a vector space over F {R, C}. An inner product on V is a function that assigns, to every ordered pair of vectors x and y in V, a scalar in F,

More information

Mathematical Methods of Engineering Analysis

Mathematical Methods of Engineering Analysis Mathematical Methods of Engineering Analysis Erhan Çinlar Robert J. Vanderbei February 2, 2000 Contents Sets and Functions 1 1 Sets................................... 1 Subsets.............................

More information

RANDOM INTERVAL HOMEOMORPHISMS. MICHA L MISIUREWICZ Indiana University Purdue University Indianapolis

RANDOM INTERVAL HOMEOMORPHISMS. MICHA L MISIUREWICZ Indiana University Purdue University Indianapolis RANDOM INTERVAL HOMEOMORPHISMS MICHA L MISIUREWICZ Indiana University Purdue University Indianapolis This is a joint work with Lluís Alsedà Motivation: A talk by Yulij Ilyashenko. Two interval maps, applied

More information

MATH10212 Linear Algebra. Systems of Linear Equations. Definition. An n-dimensional vector is a row or a column of n numbers (or letters): a 1.

MATH10212 Linear Algebra. Systems of Linear Equations. Definition. An n-dimensional vector is a row or a column of n numbers (or letters): a 1. MATH10212 Linear Algebra Textbook: D. Poole, Linear Algebra: A Modern Introduction. Thompson, 2006. ISBN 0-534-40596-7. Systems of Linear Equations Definition. An n-dimensional vector is a row or a column

More information

Properties of BMO functions whose reciprocals are also BMO

Properties of BMO functions whose reciprocals are also BMO Properties of BMO functions whose reciprocals are also BMO R. L. Johnson and C. J. Neugebauer The main result says that a non-negative BMO-function w, whose reciprocal is also in BMO, belongs to p> A p,and

More information

MEASURE AND INTEGRATION. Dietmar A. Salamon ETH Zürich

MEASURE AND INTEGRATION. Dietmar A. Salamon ETH Zürich MEASURE AND INTEGRATION Dietmar A. Salamon ETH Zürich 12 May 2016 ii Preface This book is based on notes for the lecture course Measure and Integration held at ETH Zürich in the spring semester 2014. Prerequisites

More information

A characterization of trace zero symmetric nonnegative 5x5 matrices

A characterization of trace zero symmetric nonnegative 5x5 matrices A characterization of trace zero symmetric nonnegative 5x5 matrices Oren Spector June 1, 009 Abstract The problem of determining necessary and sufficient conditions for a set of real numbers to be the

More information

Notes on Continuous Random Variables

Notes on Continuous Random Variables Notes on Continuous Random Variables Continuous random variables are random quantities that are measured on a continuous scale. They can usually take on any value over some interval, which distinguishes

More information

Nonparametric adaptive age replacement with a one-cycle criterion

Nonparametric adaptive age replacement with a one-cycle criterion Nonparametric adaptive age replacement with a one-cycle criterion P. Coolen-Schrijner, F.P.A. Coolen Department of Mathematical Sciences University of Durham, Durham, DH1 3LE, UK e-mail: Pauline.Schrijner@durham.ac.uk

More information

Fixed Point Theorems

Fixed Point Theorems Fixed Point Theorems Definition: Let X be a set and let T : X X be a function that maps X into itself. (Such a function is often called an operator, a transformation, or a transform on X, and the notation

More information

Moving Least Squares Approximation

Moving Least Squares Approximation Chapter 7 Moving Least Squares Approimation An alternative to radial basis function interpolation and approimation is the so-called moving least squares method. As we will see below, in this method the

More information

Chapter 2: Binomial Methods and the Black-Scholes Formula

Chapter 2: Binomial Methods and the Black-Scholes Formula Chapter 2: Binomial Methods and the Black-Scholes Formula 2.1 Binomial Trees We consider a financial market consisting of a bond B t = B(t), a stock S t = S(t), and a call-option C t = C(t), where the

More information

MATH 4330/5330, Fourier Analysis Section 11, The Discrete Fourier Transform

MATH 4330/5330, Fourier Analysis Section 11, The Discrete Fourier Transform MATH 433/533, Fourier Analysis Section 11, The Discrete Fourier Transform Now, instead of considering functions defined on a continuous domain, like the interval [, 1) or the whole real line R, we wish

More information

1. (First passage/hitting times/gambler s ruin problem:) Suppose that X has a discrete state space and let i be a fixed state. Let

1. (First passage/hitting times/gambler s ruin problem:) Suppose that X has a discrete state space and let i be a fixed state. Let Copyright c 2009 by Karl Sigman 1 Stopping Times 1.1 Stopping Times: Definition Given a stochastic process X = {X n : n 0}, a random time τ is a discrete random variable on the same probability space as

More information

1 The Brownian bridge construction

1 The Brownian bridge construction The Brownian bridge construction The Brownian bridge construction is a way to build a Brownian motion path by successively adding finer scale detail. This construction leads to a relatively easy proof

More information

An Introduction to Partial Differential Equations in the Undergraduate Curriculum

An Introduction to Partial Differential Equations in the Undergraduate Curriculum An Introduction to Partial Differential Equations in the Undergraduate Curriculum J. Tolosa & M. Vajiac LECTURE 11 Laplace s Equation in a Disk 11.1. Outline of Lecture The Laplacian in Polar Coordinates

More information

Galois Theory III. 3.1. Splitting fields.

Galois Theory III. 3.1. Splitting fields. Galois Theory III. 3.1. Splitting fields. We know how to construct a field extension L of a given field K where a given irreducible polynomial P (X) K[X] has a root. We need a field extension of K where

More information

Current Density Impedance Imaging with Complete Electrode Model

Current Density Impedance Imaging with Complete Electrode Model Current Density Impedance Imaging with Complete Electrode Model Alexandru Tamasan jointly with A. Nachman & J. Veras University of Central Florida Work supported by NSF BIRS Workshop on Hybrid Methods

More information

x a x 2 (1 + x 2 ) n.

x a x 2 (1 + x 2 ) n. Limits and continuity Suppose that we have a function f : R R. Let a R. We say that f(x) tends to the limit l as x tends to a; lim f(x) = l ; x a if, given any real number ɛ > 0, there exists a real number

More information

Probability and Statistics Prof. Dr. Somesh Kumar Department of Mathematics Indian Institute of Technology, Kharagpur

Probability and Statistics Prof. Dr. Somesh Kumar Department of Mathematics Indian Institute of Technology, Kharagpur Probability and Statistics Prof. Dr. Somesh Kumar Department of Mathematics Indian Institute of Technology, Kharagpur Module No. #01 Lecture No. #15 Special Distributions-VI Today, I am going to introduce

More information

DIFFERENTIABILITY OF COMPLEX FUNCTIONS. Contents

DIFFERENTIABILITY OF COMPLEX FUNCTIONS. Contents DIFFERENTIABILITY OF COMPLEX FUNCTIONS Contents 1. Limit definition of a derivative 1 2. Holomorphic functions, the Cauchy-Riemann equations 3 3. Differentiability of real functions 5 4. A sufficient condition

More information

n k=1 k=0 1/k! = e. Example 6.4. The series 1/k 2 converges in R. Indeed, if s n = n then k=1 1/k, then s 2n s n = 1 n + 1 +...

n k=1 k=0 1/k! = e. Example 6.4. The series 1/k 2 converges in R. Indeed, if s n = n then k=1 1/k, then s 2n s n = 1 n + 1 +... 6 Series We call a normed space (X, ) a Banach space provided that every Cauchy sequence (x n ) in X converges. For example, R with the norm = is an example of Banach space. Now let (x n ) be a sequence

More information

Walrasian Demand. u(x) where B(p, w) = {x R n + : p x w}.

Walrasian Demand. u(x) where B(p, w) = {x R n + : p x w}. Walrasian Demand Econ 2100 Fall 2015 Lecture 5, September 16 Outline 1 Walrasian Demand 2 Properties of Walrasian Demand 3 An Optimization Recipe 4 First and Second Order Conditions Definition Walrasian

More information

F. ABTAHI and M. ZARRIN. (Communicated by J. Goldstein)

F. ABTAHI and M. ZARRIN. (Communicated by J. Goldstein) Journal of Algerian Mathematical Society Vol. 1, pp. 1 6 1 CONCERNING THE l p -CONJECTURE FOR DISCRETE SEMIGROUPS F. ABTAHI and M. ZARRIN (Communicated by J. Goldstein) Abstract. For 2 < p

More information

Radial positive solutions of elliptic systems with Neumann boundary conditions

Radial positive solutions of elliptic systems with Neumann boundary conditions Radial positive solutions of elliptic systems with Neumann boundary conditions Denis Bonheure 1, Enrico Serra,2, Paolo Tilli 2 1 Département de Mathématique, Université libre de Bruxelles CP 214, Boulevard

More information

Markov random fields and Gibbs measures

Markov random fields and Gibbs measures Chapter Markov random fields and Gibbs measures 1. Conditional independence Suppose X i is a random element of (X i, B i ), for i = 1, 2, 3, with all X i defined on the same probability space (.F, P).

More information