Kolmogorov s Forward, Basic Results
|
|
- Silvester Bryan
- 6 years ago
- Views:
Transcription
1 Partial Differential Equations for Probabilists chapter 1 Kolmogorov s Forward, Basic Results The primary purpose of this chapter is to present some basic existence and uniqueness results for solutions to second order, parabolic, partial differential equations. Because this book is addressed to probabilists, the treatment of these results will follow, in so far as possible, a line of reasoning which is suggested by thinking about these equations in a probabilistic context. For this reason, I begin by giving an explanation of why, under suitable conditions, Kolmogorov s forward equations for the transition probability function of a continuous path Markov process is second order and parabolic. Once I have done so, I will use this connection with Markov processes to see how solutions to these equations can be constructed using probabilistically natural ideas. 1.1 Kolmogorov s Forward Equation Recall that a transition probability function on R N is a measurable map t, x [, R N P t, x M 1 R N, where M 1 R N isthespaceof Borel probability measures on R N with the topology of weak convergence, 1 which, for each x R N, satisfies P,x,{x} = 1 and the Chapman Kolmogorov equation P s + t, x, Γ = P t, y, Γ P s, x, dy for all s, t [, andγ B R N. Kolmogorov s forward equation is the equation which describes, for a fixed x R N, the evolution of t [, P t, x M 1 R N Derivation of Kolmogorov s Forward Equation: In order to derive Kolmogorov s forward equation, we will make the assumption that 1 ϕy Lϕx lim ϕx P h, x, dy h h 1 That is, the smallest topology for which the map µ M 1 R N ϕdµ R is continuous whenever ϕ C b R N ; R. See Chapter III of [53] for more information. 2 B R N denotes the Borel σ-algebra over R N. 1
2 Partial Differential Equations for Probabilists 2 1 Kolmogorov s Forward Equations exists for each x R N and ϕ Cc R N ; R, the space of infinitely differentiable, real-valued functions with compact support. Under mild additional conditions, one can combine with to conclude that d ϕy P t, x, dy = Lϕy P t, x, dy or, equivalently, dt t ϕy P t, x, dy =ϕx+ Lϕy P τ,x,dy dτ for ϕ C 2 c R N ; R. This equation is called Kolmogorov s forward equation because it describes the evolution of P t, x, dy in terms of its forward variable y, the variable giving the distribution of the process at time t, as opposed the the backward variable x which gives the initial position. Thinking of M 1 R N as a subset of C c R N ; R, the dual of C c R N ; R, one can rewrite as d dt P t, x =L P t, x, where L is the adjoint of L. Kolmogorov s idea was to recover P t, x from together with the initial condition P,x=δ x, the unit point mass at x. Of course, in order for his idea to be of any value, one must know what sort of operator L can be. A general answer is given in of [55]. However, because this book is devoted to differential equations, we will not deal here with the general case but only with the case when L is a differential operator. For this reason, we add the assumption that L is local 3 in the sense that Lϕx = whenever ϕ vanishes in a neighborhood of x. Equivalently, in terms of P t, x, locality is the condition lim h h P h, x, Bx, r =, x R N and r>. Lemma Let {µ h : h, 1} M 1 R N, and assume that 1 Aϕ lim h h ϕy ϕ µh dy exists for each ϕ C c R N ; R. ThenA is a linear functional on C c R N ; R R which satisfies the minimum principle ϕ = min x R N ϕx = Aϕ. 3 Locality of L corresponds to path continuity of the associated Markov process. 4 Readers who are familiar with Petrie s characterization of local operators may be surprised how simple it is to prove what, at first sight, might appear to be a more difficult result. Of course, the simplicity comes from the minimum principle, which allows one to control everything in terms of the action of A on quadratic functions.
3 Partial Differential Equations for Probabilists Moreover, if 1.1 Kolmogorov s Forward Equation 3 1 lim h h µ h B,r = for all r>, then A is local. Finally, if A is a linear functional on Cc R N ; R R, then A is local and satisfies the minimum principle if and only if there exists a non-negative, symmetric matrix 5 a =a ij 1 i,j N HomR N ; R N and a vector b =b i 1 i N R N such that Aϕ = 1 2 a ij xi xj ϕ + b i xi ϕ for all ϕ Cc R N ; R. i,j=1 i=1 Proof: The first assertion requires no comment. To prove the if part of the second assertion, suppose A isgivenintermsofa and b with the prescribed properties. Obviously, A is then local. In addition, if ϕ achieves its minimum value at, then the first derivatives of ϕ vanish at and its Hessian is non-negative definite there. Thus, after writing N i,j=1 a ij xi xj ϕ as the trace of a times the Hessian of ϕ at, the non-negativity of Af comes down to the fact that the product of two non-negative definite, symmetric matrices has a non-negative trace, a fact that can be seen by first writing one of them as the square of a symmetric matrix and then using the commutation invariance properties of the trace. Finally, suppose that A is local and satisfies the minimum principle. To produce the required a and b, we begin by showing that Aϕ =ifϕvanishes to second order at. For this purpose, choose η Cc R N ;[, 1] so that η =1onB, 1 and η =offb, 2, and set ϕ R x =ηr 1 xϕx for R>. Then, by locality, Aϕ = Aϕ R for all R>. In addition, by Taylor s Theorem, there exists a C< such that ϕ R CRψ for R, 1], where ψx ηx x 2. Hence, by the minimum principle applied to CRψ ϕ R, Aϕ = Aϕ R CRAψ for arbitrarily small R s. To complete the proof from here, set ψ i x =ηxx i, ψ ij = ψ i ψ j, b i = Aψ i,anda ij = Aψ ij.givenϕ, consider ϕ = ϕ xi xj ϕψ ij + xi ϕψ i. i,j=1 By Taylor s Theorem, ϕ ϕ vanishes to second order at, and therefore Aϕ = A ϕ. At the same time, by the minimum principle applied to the constant functions ±ϕ, A kills the first term on the right. Hence, Aϕ = 1 2 i,j=1 i=1 xi xj ϕaψ ij + xi ϕaψ i, 5 We will use HomR M ; R N to denote the vector space of linear transformation from R M to R N. i=1
4 Partial Differential Equations for Probabilists 4 1 Kolmogorov s Forward Equations and so it remains to check that a is non-negative definite. But, if ξ R N and ψ ξ x ηx 2 ξ,x 2 R = N N i,j=1 ξ iξ j ψ ij x, then, by the minimum principle, 2Aψ ξ =ξ,aξ R N. Since the origin can be replaced in Lemma by any point x R N, we now know that, when holds, the operator L which appears in Kolmogorov s forward equation has the form Lϕx = 1 2 a ij x xi xj ϕx+ b i x xi ϕx, i,j=1 where ax = a ij x is a non-negative definite, symmetric matrix for each x R N. In the probability literature, a is called the diffusion 1 i,j N coefficient and b is called the drift coefficient Solving Kolmogorov s Forward Equation: In this section we will prove the following general existence result for solutions to Kolmogorov s forward equation. Throughout we will use the notation ϕ, µ to denote the integral ϕdµ of the function ϕ with respect to the measure µ. Theorem Let a : R N HomR N ; R N and b : R N R N be continuous functions with the properties that ax = a ij x is 1 i,j N symmetric and non-negative definite for each x R N and + Trace ax +2 x, bx R Λ sup N x R 1+ x 2 <. N Then, for each ν M 1 R N, there is a continuous t [, µt M 1 R N which satisfies ϕ, µt ϕ, ν = t i=1 Lϕ, µτ dτ, for all ϕ Cc 2 R N ; C, wherel is the operator in Moreover, y 2 µt, dy e Λt 1 + x 2 νdx, t. Before giving the proof, it may be helpful to review the analogous result for ordinary differential equations. Indeed, when applied to the case when a =, our proof is exactly the same as the usual one there. Namely, in that case, except for the initial condition, there should be no randomness, and so, when we remove the randomness from the initial condition by taking ν = δ x, we expect that µ t = δ Xt,wheret [, Xt R N satisfies ϕ Xt ϕx = t bxτ, ϕxτ R N dτ.
5 Partial Differential Equations for Probabilists 1.1 Kolmogorov s Forward Equation 5 Equivalently, t Xt is an integral curve of the vector field b starting at x. That is, Xt =x + t b Xτ dτ. To show that such an integral curve exists, one can use the following Euler approximation scheme. For each n, define t X n t sothatx n = x and X n t =X n m2 n +t m2 n b Xm2 n for m2 n <t m+12 n. Clearly, X n t =x + t b X n [τ] n dτ, where 6 [τ] n =2 n [2 n τ] is the largest dyadic number m2 n dominated by τ. Hence, if we can show that {X n : n } is relatively compact in the space C [, ; R N, with the topology of uniform convergence on compacts, then we can take t Xt to be any limit of the X n s. To simplify matters, assume for the moment that b is bounded. In that case, it is clear that X n t X n s b u t s, and so the Ascoli Arzela Theorem guarantees the required compactness. To remove the boundedness assumption, choose a ψ Cc B, 2; [, 1] so that ψ =1onB, 1 and, for each k 1, replace b by b k,whereb k x =ψk 1 x. Next, let t X k t be an integral curve of b k starting at x, and observe that d dt X kt 2 =2 X k t,b k X k t R N Λ 1+ X k t 2, from which it is an easy step to the conclusion that X k t RT 1 + x 2 e tλ. But this means that, for each T>, X k t X k s CT t s for s, t [,T], where CT is the maximum value of b on the closed ball of radius RT centered at the origin, and so we again can invoke the Ascoli Arzela Theorem to see that {X k : k 1} is relatively compact and therefore has a limit which is an integral curve of b. In view of the preceding, it should be clear that our first task is to find an appropriate replacement for the Ascoli Arzela Theorem. The one which we will choose is the following variant of Lévy s Continuity Theorem cf. Exercise in [53], which states that if {µ n : n } M 1 R N and ˆµ n is the characteristic function i.e., the Fourier transform of µ n,then µ = lim n µ n exists in M 1 R N if and only if ˆµ n ξ converges for each ξ and uniformly in a neighborhood of, in which case µ n µ in M 1 R N where ˆµξ =lim n ˆµ n ξ. In the following, and elsewhere, we say that {ϕ k : k 1} C b R N ; C converges to ϕ in C b R N ; Candwriteϕ k ϕ in C b R N ; Cifsup k ϕ k u 6 We use [τ] to denote the integer part of a number τ R
6 Partial Differential Equations for Probabilists 6 1 Kolmogorov s Forward Equations < and ϕ k x ϕx uniformly for x in compact subsets of R N. Also, we say that {µ k : k 1} C R M ; M 1 R N converges to µ in C R M ; M 1 R N and write µ k µ in C R M ; M 1 R N if, for each ϕ C b R N ; C, ϕ, µ k z ϕ, µz uniformly for z in compact subsets of R M. Theorem If µ k µ in C R M ; M 1 R N,then ϕ k,µ k z k ϕ, µz whenever z k z in R M and ϕ k ϕ in C b R N ; C. Moreover, if {µ n : n } C R M ; M 1 R N and f n z,ξ = µ n zξ, then{µ n : n } is relatively compact in C R M ; M 1 R N if {f n : n } is equicontinuous at each z,ξ R M R N. In particular, {µ n : n } is relatively compact if, for each ξ R N, {f n,ξ: n } is equicontinuous at each z R N and, for each r,, lim sup sup R n z r µ n z,r N \ B,R =. Proof: To prove the first assertion, suppose µ k µ in C R M ; M 1 R N, z k z in R M,andϕ k ϕ in C b R N ; C. Then, for every R>, lim ϕ k,µ k z k ϕ, µz k ϕ lim ϕk,µ k z k + ϕ, µk z k ϕ, µz k k + ϕ, µzk ϕ, µz lim sup k y B,R ϕ k y ϕy +sup k sup ϕ k u µ z,b,r k ϕ k u lim k µ k zk,b,r since lim k µ k z k,f µz,f for any closed F R N. Hence, the required conclusion follows after one lets R. Turning to the second assertion, apply the Arzela Ascoli Theorem to produce an f C b R M R N ; C and a subsequence {n k : k } such that f nk f uniformly on compacts. By Lévy s Continuity Theorem, there is, for each z R M,aµz M 1 R N for which fz, = µz. Moreover, if z k z in R M, then, because f nk z k, fz, uniformly on compact subsets of R N, another application of Lévy s Theorem shows that µ nk z k µz inm 1 R N, and from this it is clear that µ nk µ in C R M ; M 1 R N. It remains to show that, under the conditions in the final assertion, {f n : n } is equicontinuous at each z,ξ. But, by assumption, for each
7 Partial Differential Equations for Probabilists 1.1 Kolmogorov s Forward Equation 7 ξ R N, {f n,ξ: n } is equicontinuous at every z R M. Thus, it suffices to show that if ξ k ξ in R N, then, for each r>, lim sup f n z,ξ k f n z,ξ =. sup k n z r To this end, note that, for any R>, f n z,ξ k f n z,ξ R ξ k ξ +2µ n z,b,r, and therefore lim sup sup fn z,ξ k f n z,ξ 2sup sup µ n z,b,r k n z r n z r as R. Now that we have a suitable compactness criterion, the next step is to develop an Euler approximation scheme. To do so, we must decide what plays the role in M 1 R N that linear translation plays in R N.Ahintcomes from the observation that if t Xt, x =x + tb is a linear translation along the constant vector field b, thenxs+t, x = Xs, x+xt,. Equivalently, δ Xs+t,x = δ x δ Xs, δ Xt,,where denotes convolution. Thus, linear translation in M 1 R N should be a path t [, µt M 1 R N givenbyµt =ν λt, where t λt satisfies λ = δ and λs + t = λs λt. That is, in the terminology of classical probability theory, µt = ν λt, where λt isaninfinitely divisible flow. Moreover, because L is local and therefore the associated process has continuous paths, the only infinitely divisible laws which can appear here must be Gaussian cf.,iii.3 and III.4 in [53]. With these hints, we now take Qt, x M 1 R N to be the normal distribution with mean x + tbx and covariance tax. Equivalently, if γdω 2π M 2 e ω 2 2 dω is the standard normal distribution on R M and σ : R N HomR M ; R N is a square root 7 of a in the sense that ax =σxσx,thenqt, x is the distribution of ω x + t 1 2 σxω + tbx under γ. To check that Qt, x will play the role that x+tbx played above, observe that if ϕ C 2 R N ; C and ϕ together with its derivatives have at most exponential growth, then ϕ, Qt, x ϕx = where L x ϕy = 1 2 i,j t L x ϕ, Qτ,x dτ, ax yi yj ϕy+ b i x yi ϕy. 7 At the moment, it makes no difference which choice of square root one chooses. Thus, one might as well assume here that σx =ax 1 2, the non-negative definite, symmetric square root ax. However, later on it will be useful to have kept our options open. i=1
8 Partial Differential Equations for Probabilists 8 1 Kolmogorov s Forward Equations To verify , simply note that d d ϕ, Qt, x = ϕ x + σxω + tbxω γ t dω, dt dt where γ t ω =gt, ω dω with gt, ω 2πt M 2 e ω 2 2t is the normal distribution on R M with mean and covariance ti, use t gt, ω = 1 2 gt, ω, and integrate twice by parts to move the off of g. As a consequence of either or direct computation, we have y 2 Qt, x, dy = x + tbx 2 + ttrace ax. Now, for each n, define the Euler approximation t [, µ n t M 1 R N sothat µ n = ν and µ n t = Q t m2 n,y µ n m2 n,dy for m2 n <t m + 12 n. By , we know that [y y 2 µ n t, dy = +t m2 n by t m2 n Trace ay ] µ n m2 n,dy for m2 n t m + 12 n. Lemma Assume that Trace ax +2 bx λ sup x R 1+ x 2 <. N Then sup 1 + y 2 µ n t, dy e 1+λt 1 + x 2 νdx. n In particular, if x 2 νdx <, then{µ n : n } is a relatively compact subset of C [, ; M 1 R N with the topology of uniform convergence on compacts. Proof: Suppose that m2 n t m + 12 n, and set τ = t m2 n. First note that y + τby 2 + τtrace ay = y 2 +2τ y, by R N + τ 2 by 2 + τtrace ay y 2 + τ [ y 2 +2 by 2 +Trace ay ] y 2 +1+λτ1 + y 2,
9 Partial Differential Equations for Probabilists 1.1 Kolmogorov s Forward Equation 9 and therefore, by , 1 + y 2 µ n t, dy λτ 1 + y 2 µ n m2 n,dy. Hence, 1 + y 2 µ n t, dy λ2 n m λτ 1 + y 2 νdy e 1+λt 1 + x 2 νdx. Next, set f n t, ξ =[ µ n t]ξ. Under the assumption that the second moment S x 2 νdx <, we want to show that {f n : n } is equicontinuous at each t, ξ [, R N. Since, by , µ n t, B,R S1 + R 2 1 e 1+λt, the last part of Theorem says that it suffices to show that, for each ξ R N, {f n,ξ: n } is equicontinuous at each t [,. To this end, first observe that, for m2 n s<t m + 12 n, fn t, ξ f n s, ξ [ Qt, y]ξ [ Qs, y]ξ] µn m2 n,dy and, by , t [ Qt, y]ξ [ Qs, y]ξ] = L y e ξ y Qτ,y,dy dτ s t s ξ,ayξ + ξ by λ1 + y ξ 2 t s, 1 2 R N where e ξ y e 1 ξ y. Hence, by , f n t, ξ f n s, ξ 1 + λ1 + ξ 2 e 1+λt 1 + x 2 νdxt s, 2 first for s<tin the same dyadic interval and then for all s<t. With Lemma , we can now prove Theorem under the assumptions that a and b are bounded and that x 2 νdx <. Indeed, because we know then that {µ n : n } is relatively compact in C [, ; M 1 R N, all that we have to do is show that every limit satisfies For this purpose, first note that, by , t ϕ, µ n t ϕ, ν = L y ϕ, Qτ [τ] n,y µ n [τ] n,dy dτ
10 Partial Differential Equations for Probabilists 1 1 Kolmogorov s Forward Equations for any ϕ C 2 b RN ; C. Next, observe that, as n, L y ϕ, Qτ [τ] n,y Lϕy boundedly and uniformly for τ,y in compacts. Hence, if µ nk C [, ; M 1 R N, then, by Theorem , µ in t ϕ, µ nk t ϕ, µt and L y ϕ, Qτ [τ] n,y µ n [τ] n,dy dτ t Lϕ, µτ dτ. Before moving on, we want to show that x 2 νdx < implies that continues to hold for ϕ C 2 R N ; C with bounded second order derivatives. Indeed, from , we know that * 1 + y 2 µt, dy e 1+λt 1 + y 2 νdy. Now choose ψ Cc R N ;[, 1] so that ψ =1onB, 1 and ψ =off of B, 2, define ψ R by ψ R y =ψr 1 yforr 1, and set ϕ R = ψ R ϕ. Observe that 8 ϕy 1+ y 2 ϕy 2 ϕy H.S. 1+ y is bounded independent of y R N, and therefore so is Lϕy 1+ y. Thus, by 2 *, there is no problem about integrability of the expressions in Moreover, because holds for each ϕ R, all that we have to do is check that t ϕ, µt = lim ϕ R,µt R Lϕ, µτ dτ = lim R t Lϕ R,µτ dτ. The first of these is an immediate application of Lebesgue s Dominated Convergence Theorem. To prove the second, observe that Lϕ R y =ψ R ylϕy+ ψ R y,ay ϕ + ϕylψ R N R y. Again the first term on the right causes no problem. To handle the other two terms, note that, because ψ R is constant off of B, 2R \ B,Rand because ψ R y =R 1 ψr 1 y while 2 ψ R y =R 2 2 ψr 1 y, one 8 We use 2 ϕ to denote the Hessian matrix of ϕ and σ H.S. to denote the Hilbert Schmidt norm ij σ2 ij of σ.
WEYL S LEMMA, ONE OF MANY. Daniel W. Stroock
WEYL S LEMMA, ONE OF MANY Daniel W Stroock Abstract This note is a brief, and somewhat biased, account of the evolution of what people working in PDE s call Weyl s Lemma about the regularity of solutions
More informationTHEOREMS, ETC., FOR MATH 515
THEOREMS, ETC., FOR MATH 515 Proposition 1 (=comment on page 17). If A is an algebra, then any finite union or finite intersection of sets in A is also in A. Proposition 2 (=Proposition 1.1). For every
More informationPROBABILITY: LIMIT THEOREMS II, SPRING HOMEWORK PROBLEMS
PROBABILITY: LIMIT THEOREMS II, SPRING 218. HOMEWORK PROBLEMS PROF. YURI BAKHTIN Instructions. You are allowed to work on solutions in groups, but you are required to write up solutions on your own. Please
More informationPROBABILITY: LIMIT THEOREMS II, SPRING HOMEWORK PROBLEMS
PROBABILITY: LIMIT THEOREMS II, SPRING 15. HOMEWORK PROBLEMS PROF. YURI BAKHTIN Instructions. You are allowed to work on solutions in groups, but you are required to write up solutions on your own. Please
More informationUniversal examples. Chapter The Bernoulli process
Chapter 1 Universal examples 1.1 The Bernoulli process First description: Bernoulli random variables Y i for i = 1, 2, 3,... independent with P [Y i = 1] = p and P [Y i = ] = 1 p. Second description: Binomial
More informationWe denote the space of distributions on Ω by D ( Ω) 2.
Sep. 1 0, 008 Distributions Distributions are generalized functions. Some familiarity with the theory of distributions helps understanding of various function spaces which play important roles in the study
More informationA Concise Course on Stochastic Partial Differential Equations
A Concise Course on Stochastic Partial Differential Equations Michael Röckner Reference: C. Prevot, M. Röckner: Springer LN in Math. 1905, Berlin (2007) And see the references therein for the original
More informationMAT 578 FUNCTIONAL ANALYSIS EXERCISES
MAT 578 FUNCTIONAL ANALYSIS EXERCISES JOHN QUIGG Exercise 1. Prove that if A is bounded in a topological vector space, then for every neighborhood V of 0 there exists c > 0 such that tv A for all t > c.
More informationFunctional Analysis. Franck Sueur Metric spaces Definitions Completeness Compactness Separability...
Functional Analysis Franck Sueur 2018-2019 Contents 1 Metric spaces 1 1.1 Definitions........................................ 1 1.2 Completeness...................................... 3 1.3 Compactness......................................
More information2. Function spaces and approximation
2.1 2. Function spaces and approximation 2.1. The space of test functions. Notation and prerequisites are collected in Appendix A. Let Ω be an open subset of R n. The space C0 (Ω), consisting of the C
More informationIntroductory Analysis I Fall 2014 Homework #9 Due: Wednesday, November 19
Introductory Analysis I Fall 204 Homework #9 Due: Wednesday, November 9 Here is an easy one, to serve as warmup Assume M is a compact metric space and N is a metric space Assume that f n : M N for each
More informationSpectral theory for compact operators on Banach spaces
68 Chapter 9 Spectral theory for compact operators on Banach spaces Recall that a subset S of a metric space X is precompact if its closure is compact, or equivalently every sequence contains a Cauchy
More information(1) u (t) = f(t, u(t)), 0 t a.
I. Introduction 1. Ordinary Differential Equations. In most introductions to ordinary differential equations one learns a variety of methods for certain classes of equations, but the issues of existence
More informationThe Heine-Borel and Arzela-Ascoli Theorems
The Heine-Borel and Arzela-Ascoli Theorems David Jekel October 29, 2016 This paper explains two important results about compactness, the Heine- Borel theorem and the Arzela-Ascoli theorem. We prove them
More informationProblem Set 5. 2 n k. Then a nk (x) = 1+( 1)k
Problem Set 5 1. (Folland 2.43) For x [, 1), let 1 a n (x)2 n (a n (x) = or 1) be the base-2 expansion of x. (If x is a dyadic rational, choose the expansion such that a n (x) = for large n.) Then the
More informationReflected Brownian Motion
Chapter 6 Reflected Brownian Motion Often we encounter Diffusions in regions with boundary. If the process can reach the boundary from the interior in finite time with positive probability we need to decide
More informationBanach Spaces V: A Closer Look at the w- and the w -Topologies
BS V c Gabriel Nagy Banach Spaces V: A Closer Look at the w- and the w -Topologies Notes from the Functional Analysis Course (Fall 07 - Spring 08) In this section we discuss two important, but highly non-trivial,
More informationLévy Processes and Infinitely Divisible Measures in the Dual of afebruary Nuclear2017 Space 1 / 32
Lévy Processes and Infinitely Divisible Measures in the Dual of a Nuclear Space David Applebaum School of Mathematics and Statistics, University of Sheffield, UK Talk at "Workshop on Infinite Dimensional
More informationSome basic elements of Probability Theory
Chapter I Some basic elements of Probability Theory 1 Terminology (and elementary observations Probability theory and the material covered in a basic Real Variables course have much in common. However
More informationWiener Measure and Brownian Motion
Chapter 16 Wiener Measure and Brownian Motion Diffusion of particles is a product of their apparently random motion. The density u(t, x) of diffusing particles satisfies the diffusion equation (16.1) u
More informationRecall that if X is a compact metric space, C(X), the space of continuous (real-valued) functions on X, is a Banach space with the norm
Chapter 13 Radon Measures Recall that if X is a compact metric space, C(X), the space of continuous (real-valued) functions on X, is a Banach space with the norm (13.1) f = sup x X f(x). We want to identify
More informationHomework #6 : final examination Due on March 22nd : individual work
Université de ennes Année 28-29 Master 2ème Mathématiques Modèles stochastiques continus ou à sauts Homework #6 : final examination Due on March 22nd : individual work Exercise Warm-up : behaviour of characteristic
More informationUnbounded operators on Hilbert spaces
Chapter 1 Unbounded operators on Hilbert spaces Definition 1.1. Let H 1, H 2 be Hilbert spaces and T : dom(t ) H 2 be a densely defined linear operator, i.e. dom(t ) is a dense linear subspace of H 1.
More informationPROBLEMS. (b) (Polarization Identity) Show that in any inner product space
1 Professor Carl Cowen Math 54600 Fall 09 PROBLEMS 1. (Geometry in Inner Product Spaces) (a) (Parallelogram Law) Show that in any inner product space x + y 2 + x y 2 = 2( x 2 + y 2 ). (b) (Polarization
More informationNOTES ON EXISTENCE AND UNIQUENESS THEOREMS FOR ODES
NOTES ON EXISTENCE AND UNIQUENESS THEOREMS FOR ODES JONATHAN LUK These notes discuss theorems on the existence, uniqueness and extension of solutions for ODEs. None of these results are original. The proofs
More informationBrownian motion. Samy Tindel. Purdue University. Probability Theory 2 - MA 539
Brownian motion Samy Tindel Purdue University Probability Theory 2 - MA 539 Mostly taken from Brownian Motion and Stochastic Calculus by I. Karatzas and S. Shreve Samy T. Brownian motion Probability Theory
More informationFeller Processes and Semigroups
Stat25B: Probability Theory (Spring 23) Lecture: 27 Feller Processes and Semigroups Lecturer: Rui Dong Scribe: Rui Dong ruidong@stat.berkeley.edu For convenience, we can have a look at the list of materials
More informationHardy-Stein identity and Square functions
Hardy-Stein identity and Square functions Daesung Kim (joint work with Rodrigo Bañuelos) Department of Mathematics Purdue University March 28, 217 Daesung Kim (Purdue) Hardy-Stein identity UIUC 217 1 /
More informationReal Analysis Problems
Real Analysis Problems Cristian E. Gutiérrez September 14, 29 1 1 CONTINUITY 1 Continuity Problem 1.1 Let r n be the sequence of rational numbers and Prove that f(x) = 1. f is continuous on the irrationals.
More informationMA5206 Homework 4. Group 4. April 26, ϕ 1 = 1, ϕ n (x) = 1 n 2 ϕ 1(n 2 x). = 1 and h n C 0. For any ξ ( 1 n, 2 n 2 ), n 3, h n (t) ξ t dt
MA526 Homework 4 Group 4 April 26, 26 Qn 6.2 Show that H is not bounded as a map: L L. Deduce from this that H is not bounded as a map L L. Let {ϕ n } be an approximation of the identity s.t. ϕ C, sptϕ
More informationSobolev spaces. May 18
Sobolev spaces May 18 2015 1 Weak derivatives The purpose of these notes is to give a very basic introduction to Sobolev spaces. More extensive treatments can e.g. be found in the classical references
More information{σ x >t}p x. (σ x >t)=e at.
3.11. EXERCISES 121 3.11 Exercises Exercise 3.1 Consider the Ornstein Uhlenbeck process in example 3.1.7(B). Show that the defined process is a Markov process which converges in distribution to an N(0,σ
More informationAn Introduction to Complex Analysis and Geometry John P. D Angelo, Pure and Applied Undergraduate Texts Volume 12, American Mathematical Society, 2010
An Introduction to Complex Analysis and Geometry John P. D Angelo, Pure and Applied Undergraduate Texts Volume 12, American Mathematical Society, 2010 John P. D Angelo, Univ. of Illinois, Urbana IL 61801.
More informationPoisson random measure: motivation
: motivation The Lévy measure provides the expected number of jumps by time unit, i.e. in a time interval of the form: [t, t + 1], and of a certain size Example: ν([1, )) is the expected number of jumps
More informationBrownian Motion. 1 Definition Brownian Motion Wiener measure... 3
Brownian Motion Contents 1 Definition 2 1.1 Brownian Motion................................. 2 1.2 Wiener measure.................................. 3 2 Construction 4 2.1 Gaussian process.................................
More informationChapter One. The Calderón-Zygmund Theory I: Ellipticity
Chapter One The Calderón-Zygmund Theory I: Ellipticity Our story begins with a classical situation: convolution with homogeneous, Calderón- Zygmund ( kernels on R n. Let S n 1 R n denote the unit sphere
More informationFUNCTIONAL ANALYSIS LECTURE NOTES: WEAK AND WEAK* CONVERGENCE
FUNCTIONAL ANALYSIS LECTURE NOTES: WEAK AND WEAK* CONVERGENCE CHRISTOPHER HEIL 1. Weak and Weak* Convergence of Vectors Definition 1.1. Let X be a normed linear space, and let x n, x X. a. We say that
More informationStrong uniqueness for stochastic evolution equations with possibly unbounded measurable drift term
1 Strong uniqueness for stochastic evolution equations with possibly unbounded measurable drift term Enrico Priola Torino (Italy) Joint work with G. Da Prato, F. Flandoli and M. Röckner Stochastic Processes
More informationMeasure Theory on Topological Spaces. Course: Prof. Tony Dorlas 2010 Typset: Cathal Ormond
Measure Theory on Topological Spaces Course: Prof. Tony Dorlas 2010 Typset: Cathal Ormond May 22, 2011 Contents 1 Introduction 2 1.1 The Riemann Integral........................................ 2 1.2 Measurable..............................................
More informationTrace Class Operators and Lidskii s Theorem
Trace Class Operators and Lidskii s Theorem Tom Phelan Semester 2 2009 1 Introduction The purpose of this paper is to provide the reader with a self-contained derivation of the celebrated Lidskii Trace
More informationIn terms of measures: Exercise 1. Existence of a Gaussian process: Theorem 2. Remark 3.
1. GAUSSIAN PROCESSES A Gaussian process on a set T is a collection of random variables X =(X t ) t T on a common probability space such that for any n 1 and any t 1,...,t n T, the vector (X(t 1 ),...,X(t
More informationGAUSSIAN MEASURES ON 1.1 BOREL MEASURES ON HILBERT SPACES CHAPTER 1
CAPTE GAUSSIAN MEASUES ON ILBET SPACES The aim of this chapter is to show the Minlos-Sazanov theorem and deduce a characterization of Gaussian measures on separable ilbert spaces by its Fourier transform.
More informationUNIVERSITY OF MANITOBA
Question Points Score INSTRUCTIONS TO STUDENTS: This is a 6 hour examination. No extra time will be given. No texts, notes, or other aids are permitted. There are no calculators, cellphones or electronic
More informationA Short Introduction to Diffusion Processes and Ito Calculus
A Short Introduction to Diffusion Processes and Ito Calculus Cédric Archambeau University College, London Center for Computational Statistics and Machine Learning c.archambeau@cs.ucl.ac.uk January 24,
More informationthe convolution of f and g) given by
09:53 /5/2000 TOPIC Characteristic functions, cont d This lecture develops an inversion formula for recovering the density of a smooth random variable X from its characteristic function, and uses that
More informationGreen s Functions and Distributions
CHAPTER 9 Green s Functions and Distributions 9.1. Boundary Value Problems We would like to study, and solve if possible, boundary value problems such as the following: (1.1) u = f in U u = g on U, where
More informationMean-field dual of cooperative reproduction
The mean-field dual of systems with cooperative reproduction joint with Tibor Mach (Prague) A. Sturm (Göttingen) Friday, July 6th, 2018 Poisson construction of Markov processes Let (X t ) t 0 be a continuous-time
More informationThe Calderon-Vaillancourt Theorem
The Calderon-Vaillancourt Theorem What follows is a completely self contained proof of the Calderon-Vaillancourt Theorem on the L 2 boundedness of pseudo-differential operators. 1 The result Definition
More informationCompact operators on Banach spaces
Compact operators on Banach spaces Jordan Bell jordan.bell@gmail.com Department of Mathematics, University of Toronto November 12, 2017 1 Introduction In this note I prove several things about compact
More informationREAL AND COMPLEX ANALYSIS
REAL AND COMPLE ANALYSIS Third Edition Walter Rudin Professor of Mathematics University of Wisconsin, Madison Version 1.1 No rights reserved. Any part of this work can be reproduced or transmitted in any
More informationEXPOSITORY NOTES ON DISTRIBUTION THEORY, FALL 2018
EXPOSITORY NOTES ON DISTRIBUTION THEORY, FALL 2018 While these notes are under construction, I expect there will be many typos. The main reference for this is volume 1 of Hörmander, The analysis of liner
More informationINTRODUCTION TO FURSTENBERG S 2 3 CONJECTURE
INTRODUCTION TO FURSTENBERG S 2 3 CONJECTURE BEN CALL Abstract. In this paper, we introduce the rudiments of ergodic theory and entropy necessary to study Rudolph s partial solution to the 2 3 problem
More informationMath The Laplacian. 1 Green s Identities, Fundamental Solution
Math. 209 The Laplacian Green s Identities, Fundamental Solution Let be a bounded open set in R n, n 2, with smooth boundary. The fact that the boundary is smooth means that at each point x the external
More informationSPECTRAL THEOREM FOR SYMMETRIC OPERATORS WITH COMPACT RESOLVENT
SPECTRAL THEOREM FOR SYMMETRIC OPERATORS WITH COMPACT RESOLVENT Abstract. These are the letcure notes prepared for the workshop on Functional Analysis and Operator Algebras to be held at NIT-Karnataka,
More informationGAUSSIAN PROCESSES; KOLMOGOROV-CHENTSOV THEOREM
GAUSSIAN PROCESSES; KOLMOGOROV-CHENTSOV THEOREM STEVEN P. LALLEY 1. GAUSSIAN PROCESSES: DEFINITIONS AND EXAMPLES Definition 1.1. A standard (one-dimensional) Wiener process (also called Brownian motion)
More informationContinuous Functions on Metric Spaces
Continuous Functions on Metric Spaces Math 201A, Fall 2016 1 Continuous functions Definition 1. Let (X, d X ) and (Y, d Y ) be metric spaces. A function f : X Y is continuous at a X if for every ɛ > 0
More informationat time t, in dimension d. The index i varies in a countable set I. We call configuration the family, denoted generically by Φ: U (x i (t) x j (t))
Notations In this chapter we investigate infinite systems of interacting particles subject to Newtonian dynamics Each particle is characterized by its position an velocity x i t, v i t R d R d at time
More informationAn Introduction to Malliavin Calculus. Denis Bell University of North Florida
An Introduction to Malliavin Calculus Denis Bell University of North Florida Motivation - the hypoellipticity problem Definition. A differential operator G is hypoelliptic if, whenever the equation Gu
More informationExtreme points of compact convex sets
Extreme points of compact convex sets In this chapter, we are going to show that compact convex sets are determined by a proper subset, the set of its extreme points. Let us start with the main definition.
More informationReminder Notes for the Course on Measures on Topological Spaces
Reminder Notes for the Course on Measures on Topological Spaces T. C. Dorlas Dublin Institute for Advanced Studies School of Theoretical Physics 10 Burlington Road, Dublin 4, Ireland. Email: dorlas@stp.dias.ie
More informationSingular Integrals. 1 Calderon-Zygmund decomposition
Singular Integrals Analysis III Calderon-Zygmund decomposition Let f be an integrable function f dx 0, f = g + b with g Cα almost everywhere, with b
More informationKolmogorov Equations and Markov Processes
Kolmogorov Equations and Markov Processes May 3, 013 1 Transition measures and functions Consider a stochastic process {X(t)} t 0 whose state space is a product of intervals contained in R n. We define
More informationCommutative Banach algebras 79
8. Commutative Banach algebras In this chapter, we analyze commutative Banach algebras in greater detail. So we always assume that xy = yx for all x, y A here. Definition 8.1. Let A be a (commutative)
More informationON THE REGULARITY OF SAMPLE PATHS OF SUB-ELLIPTIC DIFFUSIONS ON MANIFOLDS
Bendikov, A. and Saloff-Coste, L. Osaka J. Math. 4 (5), 677 7 ON THE REGULARITY OF SAMPLE PATHS OF SUB-ELLIPTIC DIFFUSIONS ON MANIFOLDS ALEXANDER BENDIKOV and LAURENT SALOFF-COSTE (Received March 4, 4)
More informationStanford Mathematics Department Math 205A Lecture Supplement #4 Borel Regular & Radon Measures
2 1 Borel Regular Measures We now state and prove an important regularity property of Borel regular outer measures: Stanford Mathematics Department Math 205A Lecture Supplement #4 Borel Regular & Radon
More informationfor all subintervals I J. If the same is true for the dyadic subintervals I D J only, we will write ϕ BMO d (J). In fact, the following is true
3 ohn Nirenberg inequality, Part I A function ϕ L () belongs to the space BMO() if sup ϕ(s) ϕ I I I < for all subintervals I If the same is true for the dyadic subintervals I D only, we will write ϕ BMO
More informationHarmonic Functions and Brownian motion
Harmonic Functions and Brownian motion Steven P. Lalley April 25, 211 1 Dynkin s Formula Denote by W t = (W 1 t, W 2 t,..., W d t ) a standard d dimensional Wiener process on (Ω, F, P ), and let F = (F
More informationMcGill University Department of Mathematics and Statistics. Ph.D. preliminary examination, PART A. PURE AND APPLIED MATHEMATICS Paper BETA
McGill University Department of Mathematics and Statistics Ph.D. preliminary examination, PART A PURE AND APPLIED MATHEMATICS Paper BETA 17 August, 2018 1:00 p.m. - 5:00 p.m. INSTRUCTIONS: (i) This paper
More informationQUALIFYING EXAMINATION Harvard University Department of Mathematics Tuesday August 31, 2010 (Day 1)
QUALIFYING EXAMINATION Harvard University Department of Mathematics Tuesday August 31, 21 (Day 1) 1. (CA) Evaluate sin 2 x x 2 dx Solution. Let C be the curve on the complex plane from to +, which is along
More informationLecture 12. F o s, (1.1) F t := s>t
Lecture 12 1 Brownian motion: the Markov property Let C := C(0, ), R) be the space of continuous functions mapping from 0, ) to R, in which a Brownian motion (B t ) t 0 almost surely takes its value. Let
More informationMcGill University Math 354: Honors Analysis 3
Practice problems McGill University Math 354: Honors Analysis 3 not for credit Problem 1. Determine whether the family of F = {f n } functions f n (x) = x n is uniformly equicontinuous. 1st Solution: The
More informationContinuity of convex functions in normed spaces
Continuity of convex functions in normed spaces In this chapter, we consider continuity properties of real-valued convex functions defined on open convex sets in normed spaces. Recall that every infinitedimensional
More informationCHAPTER VIII HILBERT SPACES
CHAPTER VIII HILBERT SPACES DEFINITION Let X and Y be two complex vector spaces. A map T : X Y is called a conjugate-linear transformation if it is a reallinear transformation from X into Y, and if T (λx)
More informationErrata Applied Analysis
Errata Applied Analysis p. 9: line 2 from the bottom: 2 instead of 2. p. 10: Last sentence should read: The lim sup of a sequence whose terms are bounded from above is finite or, and the lim inf of a sequence
More informationCHAPTER V DUAL SPACES
CHAPTER V DUAL SPACES DEFINITION Let (X, T ) be a (real) locally convex topological vector space. By the dual space X, or (X, T ), of X we mean the set of all continuous linear functionals on X. By the
More informationPart V. 17 Introduction: What are measures and why measurable sets. Lebesgue Integration Theory
Part V 7 Introduction: What are measures and why measurable sets Lebesgue Integration Theory Definition 7. (Preliminary). A measure on a set is a function :2 [ ] such that. () = 2. If { } = is a finite
More informationHere we used the multiindex notation:
Mathematics Department Stanford University Math 51H Distributions Distributions first arose in solving partial differential equations by duality arguments; a later related benefit was that one could always
More informationInvariance Principle for Variable Speed Random Walks on Trees
Invariance Principle for Variable Speed Random Walks on Trees Wolfgang Löhr, University of Duisburg-Essen joint work with Siva Athreya and Anita Winter Stochastic Analysis and Applications Thoku University,
More informationMetric Spaces and Topology
Chapter 2 Metric Spaces and Topology From an engineering perspective, the most important way to construct a topology on a set is to define the topology in terms of a metric on the set. This approach underlies
More informationERRATA: Probabilistic Techniques in Analysis
ERRATA: Probabilistic Techniques in Analysis ERRATA 1 Updated April 25, 26 Page 3, line 13. A 1,..., A n are independent if P(A i1 A ij ) = P(A 1 ) P(A ij ) for every subset {i 1,..., i j } of {1,...,
More information(convex combination!). Use convexity of f and multiply by the common denominator to get. Interchanging the role of x and y, we obtain that f is ( 2M ε
1. Continuity of convex functions in normed spaces In this chapter, we consider continuity properties of real-valued convex functions defined on open convex sets in normed spaces. Recall that every infinitedimensional
More informationFunctional Analysis Exercise Class
Functional Analysis Exercise Class Wee November 30 Dec 4: Deadline to hand in the homewor: your exercise class on wee December 7 11 Exercises with solutions Recall that every normed space X can be isometrically
More informationarxiv: v1 [math.ca] 7 Aug 2015
THE WHITNEY EXTENSION THEOREM IN HIGH DIMENSIONS ALAN CHANG arxiv:1508.01779v1 [math.ca] 7 Aug 2015 Abstract. We prove a variant of the standard Whitney extension theorem for C m (R n ), in which the norm
More informationLecture Notes in Advanced Calculus 1 (80315) Raz Kupferman Institute of Mathematics The Hebrew University
Lecture Notes in Advanced Calculus 1 (80315) Raz Kupferman Institute of Mathematics The Hebrew University February 7, 2007 2 Contents 1 Metric Spaces 1 1.1 Basic definitions...........................
More informationOverview of normed linear spaces
20 Chapter 2 Overview of normed linear spaces Starting from this chapter, we begin examining linear spaces with at least one extra structure (topology or geometry). We assume linearity; this is a natural
More informationVARIATIONAL PRINCIPLE FOR THE ENTROPY
VARIATIONAL PRINCIPLE FOR THE ENTROPY LUCIAN RADU. Metric entropy Let (X, B, µ a measure space and I a countable family of indices. Definition. We say that ξ = {C i : i I} B is a measurable partition if:
More informationFunctional Differential Equations with Causal Operators
ISSN 1749-3889 (print), 1749-3897 (online) International Journal of Nonlinear Science Vol.11(211) No.4,pp.499-55 Functional Differential Equations with Causal Operators Vasile Lupulescu Constantin Brancusi
More informationb i (µ, x, s) ei ϕ(x) µ s (dx) ds (2) i=1
NONLINEAR EVOLTION EQATIONS FOR MEASRES ON INFINITE DIMENSIONAL SPACES V.I. Bogachev 1, G. Da Prato 2, M. Röckner 3, S.V. Shaposhnikov 1 The goal of this work is to prove the existence of a solution to
More informationIntegration on Measure Spaces
Chapter 3 Integration on Measure Spaces In this chapter we introduce the general notion of a measure on a space X, define the class of measurable functions, and define the integral, first on a class of
More informationProbability and Measure
Part II Year 2018 2017 2016 2015 2014 2013 2012 2011 2010 2009 2008 2007 2006 2005 2018 84 Paper 4, Section II 26J Let (X, A) be a measurable space. Let T : X X be a measurable map, and µ a probability
More informationS chauder Theory. x 2. = log( x 1 + x 2 ) + 1 ( x 1 + x 2 ) 2. ( 5) x 1 + x 2 x 1 + x 2. 2 = 2 x 1. x 1 x 2. 1 x 1.
Sep. 1 9 Intuitively, the solution u to the Poisson equation S chauder Theory u = f 1 should have better regularity than the right hand side f. In particular one expects u to be twice more differentiable
More informationApplied Analysis (APPM 5440): Final exam 1:30pm 4:00pm, Dec. 14, Closed books.
Applied Analysis APPM 44: Final exam 1:3pm 4:pm, Dec. 14, 29. Closed books. Problem 1: 2p Set I = [, 1]. Prove that there is a continuous function u on I such that 1 ux 1 x sin ut 2 dt = cosx, x I. Define
More informationMath 118B Solutions. Charles Martin. March 6, d i (x i, y i ) + d i (y i, z i ) = d(x, y) + d(y, z). i=1
Math 8B Solutions Charles Martin March 6, Homework Problems. Let (X i, d i ), i n, be finitely many metric spaces. Construct a metric on the product space X = X X n. Proof. Denote points in X as x = (x,
More informationProblem Set 6: Solutions Math 201A: Fall a n x n,
Problem Set 6: Solutions Math 201A: Fall 2016 Problem 1. Is (x n ) n=0 a Schauder basis of C([0, 1])? No. If f(x) = a n x n, n=0 where the series converges uniformly on [0, 1], then f has a power series
More informationJoint work with Nguyen Hoang (Univ. Concepción, Chile) Padova, Italy, May 2018
EXTENDED EULER-LAGRANGE AND HAMILTONIAN CONDITIONS IN OPTIMAL CONTROL OF SWEEPING PROCESSES WITH CONTROLLED MOVING SETS BORIS MORDUKHOVICH Wayne State University Talk given at the conference Optimization,
More informationPACKING-DIMENSION PROFILES AND FRACTIONAL BROWNIAN MOTION
PACKING-DIMENSION PROFILES AND FRACTIONAL BROWNIAN MOTION DAVAR KHOSHNEVISAN AND YIMIN XIAO Abstract. In order to compute the packing dimension of orthogonal projections Falconer and Howroyd 997) introduced
More informationPacking-Dimension Profiles and Fractional Brownian Motion
Under consideration for publication in Math. Proc. Camb. Phil. Soc. 1 Packing-Dimension Profiles and Fractional Brownian Motion By DAVAR KHOSHNEVISAN Department of Mathematics, 155 S. 1400 E., JWB 233,
More informationMULTIPLICATIVE MONOTONIC CONVOLUTION
Illinois Journal of Mathematics Volume 49, Number 3, Fall 25, Pages 929 95 S 9-282 MULTIPLICATIVE MONOTONIC CONVOLUTION HARI BERCOVICI Abstract. We show that the monotonic independence introduced by Muraki
More informationMATH MEASURE THEORY AND FOURIER ANALYSIS. Contents
MATH 3969 - MEASURE THEORY AND FOURIER ANALYSIS ANDREW TULLOCH Contents 1. Measure Theory 2 1.1. Properties of Measures 3 1.2. Constructing σ-algebras and measures 3 1.3. Properties of the Lebesgue measure
More informationTraces, extensions and co-normal derivatives for elliptic systems on Lipschitz domains
Traces, extensions and co-normal derivatives for elliptic systems on Lipschitz domains Sergey E. Mikhailov Brunel University West London, Department of Mathematics, Uxbridge, UB8 3PH, UK J. Math. Analysis
More information