Tao Nonlinear Dispersive PDEs

7/25/2019 Tao Nonlinear Dispersive PDEs

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Nonlinear dispersive equations: local and global

analysis

Terence Tao

Department of Mathematics, UCLA, Los Angeles, CA 90095

E-mail address: [email protected]


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1991 Mathematics Subject Classification. Primary 35Q53, 35Q55, 35L15

The author is partly supported by a grant from the Packard foundation.


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To Laura, for being so patient.


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Contents

Preface ix

Chapter 1. Ordinary differential equations 11.1. General theory 21.2. Gronwalls inequality 11

1.3. Bootstrap and continuity arguments 201.4. Noethers theorem 261.5. Monotonicity formulae 351.6. Linear and semilinear equations 401.7. Completely integrable systems 49

Chapter 2. Constant coefficient linear dispersive equations 552.1. The Fourier transform 622.2. Fundamental solution 692.3. Dispersion and Strichartz estimates 732.4. Conservation laws for the Schrodinger equation 822.5. The wave equation stress-energy tensor 892.6. Xs,b spaces 97

Chapter 3. Semilinear dispersive equations 1093.1. On scaling and other symmetries 1143.2. What is a solution? 1203.3. Local existence theory 1293.4. Conservation laws and global existence 1433.5. Decay estimates 1533.6. Scattering theory 1623.7. Stability theory 1713.8. Illposedness results 1803.9. Almost conservation laws 186

Chapter 4. The Korteweg de Vries equation 197

4.1. Existence theory 2024.2. Correction terms 2134.3. Symplectic non-squeezing 2184.4. The Benjamin-Ono equation and gauge transformations 223

Chapter 5. Energy-critical semilinear dispersive equations 2315.1. The energy-critical NLW 2335.2. Bubbles of energy concentration 2475.3. Local Morawetz and non-concentration of mass 2575.4. Minimal-energy blowup solutions 262

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viii CONTENTS

5.5. Global Morawetz and non-concentration of mass 271

Chapter 6. Wave maps 2776.1. Local theory 2886.2. Orthonormal frames and gauge transformations 2996.3. Wave map decay estimates 3106.4. Heat flow 320

Chapter A. Appendix: tools from harmonic analysis 329

Chapter B. Appendix: construction of ground states 347

Bibliography 363


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Preface

Politics is for the present, but an equation is something for eternity.(Albert Einstein)

This monograph is based on (and greatly expanded from) a lecture series givenat the NSF-CBMS regional conference on nonlinear and dispersive wave equationsat New Mexico State University, held in June 2005. Its objective is to present

some aspects of the global existence theory (and in particular, the regularity andscattering theory) for various nonlinear dispersive and wave equations, such as theKorteweg-de Vries (KdV), nonlinear Schrodinger, nonlinear wave, and wave mapsequations. The theory here is rich and vast and we cannot hope to present acomprehensive survey of the field here; our aim is instead to present a sample ofresults, and to give some idea of the motivation and general philosophy underlyingthe problems and results in the field, rather than to focus on the technical details.We intend this monograph to be an introduction to the field rather than an ad-vanced text; while we do include some very recent results, and we imbue some moreclassical results with a modern perspective, our main concern will be to developthe fundamental tools, concepts, and intuitions in as simple and as self-containeda matter as possible. This is also a pedagogical text rather than a reference; many

details of arguments are left to exercises or to citations, or are sketched informally.Thus this text should be viewed as being complementary to the research literatureon these topics, rather than being a substitute for them.

The analysis of PDE is a beautiful subject, combining the rigour and techniqueof modern analysis and geometry with the very concrete real-world intuition ofphysics and other sciences. Unfortunately, in some presentations of the subject (atleast in pure mathematics), the former can obscure the latter, giving the impressionof a fearsomely technical and difficult field to work in. To try to combat this, thisbook is devoted in equal parts to rigour and to intuition; the usual formalism ofdefinitions, propositions, theorems, and proofs appear here, but will be interspersedand complemented with many informal discussions of the same material, centeringaround vague Principles and figures, appeal to physical intuition and examples,back-of-the-envelope computations, and even some whimsical quotations. Indeed,the exposition and exercises here reflect my personal philosophy that to truly under-stand a mathematical result one must view it from as many perspectives as possible(including both rigorous arguments and informal heuristics), and must also be ableto translate easily from one perspective to another. The reader should thus beaware of which statements in the text are rigorous, and which ones are heuristic,but this should be clear from context in most cases.

To restrict the field of study, we shall focus primarily on defocusingequations,in which soliton-type behaviour is prohibited. From the point of view of globalexistence, this is a substantially easier case to study than the focusing problem, in

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x PREFACE

which one has the fascinating theory of solitons and multi-solitons, as well as variousmechanisms to enforce blow-up of solutions in finite or infinite time. However, we

shall see that there are still several analytical subtleties in the defocusing case,especially when considering critical nonlinearities, or when trying to establish asatisfactory scattering theory. We shall also work in very simple domains suchas Euclidean space Rn or tori Tn, thus avoiding consideration of boundary-valueproblems, or curved space, though these are certainly very important extensions tothe theory. One further restriction we shall make is to focus attention on the initialvalue problem when the initial data lies in a Sobolev space Hsx(R

d), as opposed tomore localised choices of initial data (e.g. in weighted Sobolev spaces, or self-similarinitial data). This restriction, combined with the previous one, makes our choice ofproblem translation-invariant in space, which leads naturally to the deployment ofthe Fourier transform, which turns out to be a very powerful tool in our analysis.Finally, we shall focus primarily on only four equations: the semilinear Schr odingerequation, the semilinear wave equation, the Korteweg-de Vries equation, and thewave maps equation. These four equations are of course only a very small sample ofthe nonlinear dispersive equations studied in the literature, but they are reasonablyrepresentative in that they showcase many of the techniques used for more generalequations in a comparatively simple setting.

Each chapter of the monograph is devoted to a different class of differentialequations; generally speaking, in each chapter we first study the algebraic struc-ture of these equations (e.g. symmetries, conservation laws, and explicit solutions),and then turn to the analytic theory (e.g. existence and uniqueness, and asymptoticbehaviour). The first chapter is devoted entirely to ordinary differential equations(ODE). One can view partial differential equations (PDE) such as the nonlineardispersive and wave equations studied here, as infinite-dimensional analogues ofODE; thus finite-dimensional ODE can serve as a simplified model for understand-

ing techniques and phenomena in PDE. In particular, basic PDE techniques suchas Picard and Duhamel iteration, energy methods, continuity or bootstrap argu-ments, conservation laws, near-conservation laws, and monotonicity formulae allhave useful ODE analogues. Furthermore, the analogy between classical mechan-ics and quantum mechanics provides a useful heuristic correspondence betweenSchrodinger type equations, and classical ODE involving one or more particles, atleast in the high-frequency regime.

The second chapter is devoted to the theory of the basic linear dispersive mod-els: theAiry equation, the free Schrodinger equation, and the free wave equation.In particular, we show how the Fourier transform and conservation law methods,can be used to establish existence of solutions, as well as basic estimates such asthe dispersive estimate, local smoothing estimates, Strichartz estimates, and Xs,b

estimates.In the third chapter we begin studying nonlinear dispersive equations in earnest,beginning with two particularly simple semilinear models, namely the nonlinearSchrodinger equation (NLS) and nonlinear wave equation (NLW). Using theseequations as examples, we illustrate the basic approaches towards defining andconstructing solutions, and establishing local and global properties, though we de-fer the study of the more delicate energy-critical equations to a later chapter. (Themass-critical nonlinear Schrodinger equation is also of great interest, but we willnot discuss it in detail here.)


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PREFACE xi

In the fourth chapter, we analyze theKorteweg de Vries equation(KdV), whichrequires some more delicate analysis due to the presence of derivatives in the non-

linearity. To partly compensate for this, however, one now has the structures ofnonresonance and complete integrability; the interplay between the integrability onone hand, and the Fourier-analytic structure (such as nonresonance) on the other,is still only partly understood, however we are able to at least establish a quitesatisfactory local and global wellposedness theory, even at very low regularities,by combining methods from both. We also discuss a less dispersive cousin of theKdV equation, namely theBenjamin-Ono equation, which requires more nonlineartechniques, such as gauge transforms, in order to obtain a satisfactory existenceand wellposedness theory.

In the fifth chapter we return to the semilinear equations (NLS and NLW),and now establish large data global existence for these equations in the defocusing,energy-critical case. This requires the full power of the local wellposedness and per-turbation theory, together with Morawetz-type estimates to prevent various kindsof energy concentration. The situation is especially delicate for the Schrodingerequation, in which one must employ the induction on energy methods of Bourgainin order to obtain enough structural control on a putative minimal energy blowupsolutionto obtain a contradiction and thus ensure global existence.

In the final chapter, we turn to thewave maps equation(WM), which is some-what more nonlinear than the preceding equations, but which on the other handenjoys a strongly geometric structure, which can in fact be used to renormalisemost of the nonlinearity. The small data theory here has recently been completed,but the large data theory has just begun; it appears however that the geometricrenormalisation provided by the harmonic map heat flow, together with a Morawetzestimate, can again establish global existence in the negatively curved case.

As a final disclaimer, this monograph is by no means intended to be a defini-

tive, exhaustive, or balanced survey of the field. Somewhat unavoidably, the textfocuses on those techniques and results which the author is most familiar with, inparticular the use of the iteration method in various function spaces to establish alocal and perturbative theory, combined with frequency analysis, conservation laws,and monotonicity formulae to then obtain a global non-perturbative theory. Thereare other approaches to this subject, such as via compactness methods, nonlineargeometric optics, infinite-dimensional Hamiltonian dynamics, or the techniques ofcomplete integrability, which are also of major importance in the field (and cansometimes be combined, to good effect, with the methods discussed here); however,we will be unable to devote a full-length treatment of these methods in this text. Itshould also be emphasised that the methods, heuristics, principles and philosophygiven here are tailored for the goal of analyzing the Cauchy problem for semilinear

dispersive PDE; they do not necessarily extend well to other PDE questions (e.g.control theory or inverse problems), or to other classes of PDE (e.g. conservationlaws or to parabolic and elliptic equations), though there are certain many connec-tions and analogies between results in dispersive equations and in other classes ofPDE.

I am indebted to my fellow members of the I-team (Jim Colliander, MarkusKeel, Gigliola Staffilani, Hideo Takaoka), to Sergiu Klainerman, and to MichaelChrist for many entertaining mathematical discussions, which have generated muchof the intuition that I have tried to place into this monograph. I am also very


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xii PREFACE

thankful for Jim Ralston for using this text to teach a joint PDE course, andproviding me with careful corrections and other feedback on the material. I also

thank Soonsik Kwon and Shaunglin Shao for additional corrections. Last, butnot least, I am grateful to my wife Laura for her support, and for pointing outthe analogy between the analysis of nonlinear PDE and the electrical engineeringproblem of controlling feedback, which has greatly influenced my perspective onthese problems (and has also inspired many of the diagrams in this text).

Terence Tao

Notation. As is common with any book attempting to survey a wide rangeof results by different authors from different fields, the selection of a unified no-tation becomes very painful, and some compromises are necessary. In this text Ihave (perhaps unwisely) decided to make the notation as globally consistent across

chapters as possible, which means that any individual result presented here willlikely have a notation slightly different from the way it is usually presented in theliterature, and also that the notation is more finicky than a local notation wouldbe (often because of some ambiguity that needed to be clarified elsewhere in thetext). For the most part, changing from one convention to another is a matter ofpermuting various numerical constants such as 2, , i, and1; these constants areusually quite harmless (except for the sign1), but one should nevertheless takecare in transporting an identity or formula in this book to another context in whichthe conventions are slightly different.

In this text,d will always denote the dimension of the ambient physical space,which will either be a Euclidean space1 Rd or the torusTd := (R/2Z)d. (Chapter1 deals with ODE, which can be considered to be the case d = 0.) All integrals onthese spaces will be with respect to Lebesgue measure dx. Ifx= (x1, . . . , xd) and

= (1, . . . , d) lie inRd, we usex to denote the dot productx := x11 + . . . +xdd, and|x| to denote the magnitude|x| := (x21+ . . .+x2d)1/2. We also usexto denote the inhomogeneous magnitude (or Japanese bracket)x:= (1 + |x|2)1/2of x, thusx is comparable to|x| for large x and comparable to 1 for small x.In a similar spirit, ifx = (x1, . . . , xd) Td and k = (k1, . . . , kd) Zd we definek x:= k1x1+ . . . + kdxd T. In particular the quantity eikx is well-defined.

We say thatI is atime intervalif it is a connected subset ofR which containsat least two points (so we allow time intervals to be open or closed, bounded orunbounded). Ifu : IRd Cn is a (possibly vector-valued) function of spacetime,we write tu for the time derivative

ut , and x1u , . . . , xdu for the spatial derivatives

ux1

, . . . , uxd ; these derivatives will either be interpreted in the classical sense (when

u is smooth) or the distributional (weak) sense (when u is rough). We use

xu :

IRd Cnd to denote the spatial gradientxu = (x1u , . . . , xdu). We caniterate this gradient to define higher derivativeskx for k = 0, 1, . . .. Of course,

1We will be using two slightly different notions of spacetime, namely Minkowski spaceR1+d

and Galilean spacetimeRRd; in the very last section we also need to use parabolic spacetimeR+ Rd. As vector spaces, they are of course equivalent to each other (and to the EuclideanspaceRd+1), but we will place different (pseudo)metric structures on them. Generally speaking,wave equations will use Minkowski space, whereas nonrelativistic equations such as Schrodingerequations will use Galilean spacetime, while heat equations use parabolic spacetime. For the mostpart the reader will be able to safely ignore these subtle distinctions.


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PREFACE xiii

these definitions also apply to functions on Td, which can be identified with periodicfunctions on Rd.

We use the Einstein convention for summing indices, with Latin indices rangingfrom 1 to d, thus for instance xjxju is short for

dj=1 xjxju. When we come

to wave equations, we will also be working in a Minkowski space R1+d with aMinkowski metricg ; in such cases, we will use Greek indices and sum from 0 tod(withx0 =t being the time variable), and use the metric to raise and lower indices.Thus for instance if we use the standard Minkowski metric dg2 =dt2 + |dx|2, then0u= tu but0u=tu.

In this monograph we always adopt the convention thatts

= st

if t < s.This convention will usually be applied only to integrals in the time variable.

We use the Lebesgue norms

fLpx(RdC) := (Rd

|f(x)|p dx)1/p

for 1 p


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xiv PREFACE

the norm

uCkt(IX) :=k

j=0

jtuLt (IX).

We adopt the convention thatuCkt(IX) = ifu is not k-times continuouslydifferentiable. One can of course also define spatial analogues Ckx(R

d X) of thesespaces, as well as spacetime versionsCkt,x(IRd X). We caution that ifIis notcompact, then it is possible for a function to be k-times continuously differentiablebut have infiniteCkt norm; in such cases we say that u Ckt,loc(IX) rather thanu Ckt(IX). More generally, a statement of the form u Xloc() on a domain means that we can cover by open sets V such that the restriction u|V of uto each of these sets V is in X(V); under reasonable assumptions on X, this alsoimplies thatu|K X(K) for any compact subset Kof . As a rule of thumb, theglobal spaces X() will be used for quantitative control (estimates), whereas thelocal spaces Xloc() are used for qualitative control (regularity); indeed, the localspaces Xloc are typically only Frechet spaces rather than Banach spaces. We willneed both types of control in this text, as one typically needs qualitative control toensure that the quantitative arguments are rigorous.

If (X, dX) is a metric space and Y is a Banach space, we use C0,1(XY) todenote the space of all Lipschitz continuous functions f :XY, with norm

fC0,1(XY):= supx,xX:x=x

f(x) f(x)YdX(x, x)

.

(One can also define theinhomogeneous Lipschitz normfC0,1 :=fC0,1+fC0,but we will not need this here.) Thus for instance C1(Rd Rm) is a subsetof

C

0,1

(Rd

Rm

), which is in turn a subset of C0

loc(Rd

Rm

). The spaceC0,1loc (X Y) is thus the space of locallyLipschitz functions (i.e. every xX iscontained in a neighbourhood on which the function is Lipschitz).

In addition to the above function spaces, we shall also use Sobolev spaces Hs,Ws,p, Hs, Ws,p, which are defined in Appendix A, and Xs,b spaces, which aredefined in Section 2.6.

IfV andWare finite-dimensional vector spaces, we use End(VW) to denotethe space of linear transformations from V toW, and End(V) = End(VV) forthe ring of linear transformations from V to itself. This ring contains the identitytransformation id = idV.

IfXand Y are two quantities (typically non-negative), we useX Y orY Xto denote the statement that X C Yfor some absolute constant C >0. We useX = O(Y) synonymously with

|X

| Y. More generally, given some parameters

a1, . . . , ak, we use Xa1,...,ak Y or Y a1,...,ak X to denote the statement thatXCa1,...,akY for some (typically large) constant Ca1,...,ak >0 which can dependon the parameters a1, . . . , ak, and define X=Oa1,...,ak(Y) similarly. Typical choicesof parameters include the dimension d, the regularity s, and the exponent p. Wewill also say that X is controlled by a1, . . . , ak if X = Oa1,...,ak(1). We use XY to denote the statement X Y X, and similarly Xa1,...,ak Y denotesXa1,...,ak Y a1,...,ak X. We will occasionally use the notation Xa1,...,ak Yor Ya1,...,ak Xto denote the statement X ca1,...,akY for some suitably smallquantity ca1,...,ak > 0 depending on the parameters a1, . . . , ak. This notation is


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PREFACE xv

somewhat imprecise (as one has to specify what suitably small means) and so weshall usually only use it in informal discussions.

Recall that a function f :Rd

C is said to be rapidly decreasing if we havexNf(x)Lx (Rd)


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CHAPTER 1

Ordinary differential equations

Science is a differential equation. Religion is a boundary condition.(Alan Turing, quoted in J.D. Barrow, Theories of everything)

This monograph is primarily concerned with the global Cauchy problem (or

initial value problem) for partial differential equations (PDE), but in order to as-semble some intuition on the behaviour of such equations, and on the power andlimitations of the various techniques available to analyze these equations, we shallfirst study these phenomena and methods in the much simpler context ofordinarydifferential equations(ODE), in which many of the technicalities in the PDE anal-ysis are not present. Conversely, the theory of ODEs, particularly HamiltonianODEs, has a very rich and well-developed structure, the extension of which to non-linear dispersive PDEs is still far from complete. For instance, phenomena fromHamiltonian dynamics such as Kolmogorov-Arnold-Moser (KAM) invariant tori,symplectic non-squeezing, Gibbs and other invariant measures, or Arnold diffusionare well established in the ODE setting, but the rigorous theory of such phenomenafor PDEs is still its infancy.

One technical advantage of ODE, as compared with PDE, is that with ODEone can often work entirely in the category of classical (i.e. smooth) solutions,thus bypassing the need for the theory of distributions, weak limits, and so forth.However, even with ODE it is possible to exhibit blowup in finite time, and in high-dimensional ODE (which begin to approximate PDE in the infinite dimensionallimit) it is possible to have the solution stay bounded in one norm but becomeextremely large in another norm. Indeed, the quantitative study of expressionssuch as mass, energy, momentum, etc. is almost as rich in the ODE world as it isin the PDE world, and thus the ODE model does serve to illuminate many of thephenomena that we wish to study for PDE.

A common theme in both nonlinear ODE and nonlinear PDE is that offeedback- the solution to the equation at any given time generates some forcing term, whichin turn feeds back into the system to influence the solution at later times, usuallyin a nonlinear fashion. The tools we will develop here to maintain control of thisfeedback effect - the Picard iteration method, Gronwalls inequality, the bootstrapprinciple, conservation laws, monotonicity formulae, and Duhamels formula - willform the fundamental tools we will need to analyze nonlinear PDE in later chapters.Indeed, the need to deal with such feedback gives rise to a certain nonlinear wayof thinking, in which one continually tries to control the solution in terms of itself,or derive properties of the solution from (slightly weaker versions of) themselves.This way of thinking may initially seem rather unintuitive, even circular, in nature,but it can be made rigorous, and is absolutely essential to proceed in this theory.

1


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2 1. ORDINARY DIFFERENTIAL EQUATIONS

1.1. General theory

It is a capital mistake to theorise before one has data. Insensiblyone begins to twist facts to suit theories, instead of theories to suitfacts. (Sir Arthur Conan Doyle, A Study in Scarlet)

In this section we introduce the concept of an ordinary differential equationand the associated Cauchy problem, but then quickly specialise to an importantsubclass of such problems, namely the Cauchy problem (1.7) for autonomous first-order quasilinear systems.

Throughout this chapter,Dwill denote a (real or complex) finite dimensionalvector space, which at times we will endow with some norm D; the letter Dstandsfor data. An ordinary differential equation(ODE) is an equation which governscertain functions u : I D mapping a (possibly infinite) time interval I R tothe vector space1 D. In this setup, the most general form of an ODE is that of afully nonlinear ODE

(1.1) G(u(t), tu(t), . . . , ktu(t), t) = 0

wherek1 is an integer, andG:Dk+1 IXis a given function taking values inanother finite-dimensional vector spaceX. We say that a function u Ckloc(I D)is aclassical solution2 (orsolutionfor short) of the ODE if (1.1) holds for all t I.The integer k is called the order of the ODE, thus for instance ifk = 2 then wehave a second-order ODE. One can think ofu(t) as describing the state of somephysical system at a given time t; the dimension ofD then measures the degreesof freedom available. We shall refer toD as the state space, and sometimes referto the ODE as the equation(s) of motion, where the plural reflects the fact that Xmay have more than one dimension. While we will occasionally consider thescalarcase, when

D is just the real line R or the complex plane C, we will usually be

more interested in the case when the dimension ofDis large. Indeed one can viewPDE as a limiting case of ODE as dim(D) .

In this monograph we will primarily consider those ODE which are time-translation-invariant (or autonomous), in the sense that the function G does notactually depend explicitly on the time parameter t, thus simplifying (1.1) to

(1.2) G(u(t), tu(t), . . . , ktu(t)) = 0

for some function G:Dk+1 X. One can in fact convert any ODE into a time-translation-invariant ODE, by the trick of embedding the time variable itself intothe state space, thus replacingDwithD R, Xwith XR, u with the function

1One could generalise the concept of ODE further, by allowing D to be a smooth manifoldinstead of a vector space, or even a smooth bundle over the time interval I. This leads for instanceto the theory ofjet bundles, which we will not pursue here. In practice, one can descend from thismore general setup back to the original framework of finite-dimensional vector spaces - locallyin time, at least - by choosing appropriate local coordinate charts, though often the choice ofsuch charts is somewhat artifical and makes the equations messier; see Chapter 6 for some relatedissues.

2We will discuss non-classical solutions shortly. As it turns out, for finite-dimensional ODEthere is essentially no distinction between a classical and non-classical solution, but for PDE therewill be a need to distinguish between classical, strong, and weak solutions. See Section 3.2 forfurther discussion.


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1.1. GENERAL THEORY 3

u(t) := (u(t), t), andG with the function3

G((u0, s0), (u1, s1), . . . , (uk, sk)) := (G(u0, . . . , uk), s1

1).

For instance, solutions to the non-autonomous ODE

tu(t) = F(t, u(t))

are equivalent to solutions to the system of autonomous ODE

tu(t) = F(s(t), u(t)); ts(t) 1 = 0provided that we also impose a new initial condition s(0) = 0. This trick is notalways without cost; for instance, it will convert a non-autonomous linear equationinto an autonomous nonlinear equation.

By working with time translation invariant equations we obtain our first sym-metry, namely the time translation symmetry

(1.3) u(t)

u(t

t0).

More precisely, if u : I D solves the equation (1.2), and t0 R is any timeshift parameter, then the time-translated function ut0 : I+t0 D defined byut0(t) := u(t t0), where I+t0 :={t+t0 : t I} is the time translation of I,is also a solution to (1.2). This symmetry tells us, for instance, that the initialvalue problem for this equation starting from time t = 0 will be identical (afterapplying the symmetry (1.3)) to the initial value problem starting from any othertimet = t0.

The equation (1.2) implicitly determines the value of the top-order derivative

ktu(t) in terms of the lower order derivatives u(t), tu(t), . . . , k1t u(t). If the

hypotheses of the implicit function theorem4 are satisfied, then we can solve forktu(t) uniquely, and rewrite the ODE as an autonomous quasilinear ODE of orderk

(1.4) ktu(t) =F(u(t), tu(t), . . . , k1t u(t)),

for some function F :Dk D. Of course, there are times when the implicitfunction theorem is not available, for instance if the domainY ofGhas a differentdimension than that ofD. IfYhas larger dimension thanD then the equation isoften over-determined; it has more equations of motion than degrees of freedom,and one may require some additional hypotheses on the initial data before a solutionis guaranteed. IfYhas smaller dimension thanDthen the equation is often under-determined; it has too few equations of motion, and one now expects to have amultiplicity of solutions for any given initial datum. And even ifD andY havethe same dimension, it is possible for the ODE to sometimes be degenerate, in thatthe Jacobian that one needs to invert for the implicit function theorem becomes

singular.3Informally, what one has done is added a clock s to the system, which evolves at the fixed

rate of one time unit per time unit ( dsdt 1 = 0), and then the remaining components of the systemare now driven by clock time rather than by the system time. The astute reader will note that thisnew ODE not only contains all the solutions to the old ODE, but also contains some additionalsolutions; however these new solutions are simply time translations of the solutions coming fromthe original ODE.

4An alternate approach is to differentiate (1.2) in time using the chain rule, obtaining an

equation which is linear in k+1t u(t), and provided that a certain matrix is invertible, one can

rewrite this in the form (1.4) but with k replaced by k + 1.


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Degenerate ODE are rather difficult to study and will not be addressed here.Both under-determined and over-determined equations cause difficulties for analy-

sis, which are resolved in different ways. An over-determined equation can often bemade determined by forgetting some of the constraints present in (1.2), for in-stance by projectingY down to a lower-dimensional space. In many cases, one canthen recover the forgotten constraints by using some additional hypothesis on theinitial datum, together with an additional argument (typically involving Gronwallsinequality); see for instance Exercises 1.13, (6.4). Meanwhile, an under-determinedequation often enjoys a large group of gauge symmetries which help explainthe multiplicity of solutions to the equation; in such a case one can often fix a spe-cial gauge, thus adding additional equations to the system, to make the equationdetermined again; see for instance Section 6.2 below. In some cases, an ODE cancontain both over-determined and under-determined components, requiring one toperform both of these types of tricks in order to recover a determined equation,such as one of the form (1.4).

Suppose that u is a classical solution to the quasilinear ODE (1.4), and thatthe nonlinearity F :Dk D is smooth. Then one can differentiate (1.4) in time,use the chain rule, and then substitute in (1.4) again, obtain an equation of theform

k+1t u(t) = Fk+1(u(t), tu(t), . . . , k1t u(t))

for some smooth function Fk+1:Dk Dwhich can be written explicitly in termsofG. More generally, by an easy induction we obtain equations of the form

(1.5) k

t u(t) = Fk (u(t), tu(t), . . . , k1t u(t))

for anyk k, whereFk :Dk Dis a smooth function which depends only on Gandk. Thus, if one specifies the initial datau(t0), . . . , k1t u(t0) at some fixed timet0, then all higher derivatives ofu at t0are also completely specified. This shows in

particular that ifu is k 1-times continuously differentiable and Fis smooth, thenu is automatically smooth. Ifu is not only smooth but analytic, then from Taylorexpansion we see that u is now fixed uniquely. Of course, it is only reasonable toexpect u to be analytic ifF is also analytic. In such a case, we can complementthe above uniqueness statement with a (local) existence result:

Theorem 1.1 (Cauchy-Kowalevski theorem). Letk 1. SupposeF :Dk Dis real analytic, lett0R, and letu0, . . . , uk1 Dbe arbitrary. Then there existsan open time intervalI containingt0, and a unique real analytic solutionu : I Dto (1.4), which obeys the initial value conditions

u(t0) = u0; tu(t0) = u1, . . . , k1t u(t0) = uk1.

We defer the proof of this theorem to Exercise 1.1. This beautiful theorem

can be considered as a complete local existence theorem for the ODE (1.4), inthe case when G is real analytic; it says that the initial position u(t0), and the

first k 1 derivatives, tu(t0), . . . , k1t u(t0), are precisely the right amount ofinitial data5 needed in order to have a wellposed initial value problem (we willdefine wellposedness more precisely later). However, it turns out to have somewhat

5Conventions differ on when to use the singular datum and the plural data. In this text,we shall use the singular datum for ODE and PDE that are first-order in time, and the pluraldata for ODE and PDE that are higher order (or unspecified order) in time. Of course, in bothcases we use the plural when considering an ensemble or class of data.


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limited application when we move from ODE to PDE (though see Exercise 3.25).We will thus rely instead primarily on a variant of the Cauchy-Kowalevski theorem,

namely the Picard existence theorem, which we shall discuss below.

Remark 1.2. The fact that the solution u is restricted to lie in a open intervalI, as opposed to the entire real line R, is necessary. A basic example is the initialvalue problem

(1.6) ut = u2; u(0) = 1

where u takes values on the real line R. One can easily verify that the functionu(t) := 11t solves this ODE with the given initial datum as long as t


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u0

u(t)

F(u(t))

Figure 1. Depicting Fas a vector field onD, the trajectory ofthe solution u(t) to the first order ODE (1.7) thus follows thearrows and integrates the vector field F. Contrast this classi-cal solution interpretation of an ODE with the rather differentstrong solution interpretation in Figure 2.

A classical solutionof (1.7) is a function u C1loc(I D) which solves(1.7) for all tI in the classical sense (i.e. using the classical notion ofderivative). Astrong solutionof (1.7) is a function u C0loc(I D) which solves (1.7)in the integral sense that

(1.8) u(t) = u0+

tt0

F(u(s))ds

holds for all8 t I; A weak solutionof (1.7) is a function uL(I D) which solves (1.8)

in the sense of distributions, thus for any test function C0 (I), onehas

I

u(t)(t) dt = u0

I

(t) +

I

(t)

tt0

F(u(s))dsdt.

Later, when we turn our attention to PDE, these three notions of solutionshall become somewhat distinct; see Section 3.2. In the ODE case, however, wefortunately have the following equivalence (under a very mild assumption on F):

Lemma 1.3. Let F C0loc(D D). Then the notions of classical solution,strong solution, and weak solution are equivalent.

Proof. It is clear that a classical solution is strong (by the fundamental the-orem of calculus), and that a strong solution is weak. Ifu is a weak solution, then

8Recall that we are adopting the convention that ts =

st ift < s.


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Forcing term

F(u)

Solution

uu

0Constant evolution

Integration

in time

Initial datum

Nonlinearity F

Figure 2. A schematic depiction of the relationship between theinitial datumu0, the solutionu(t), and the nonlinearityF(u). Themain issue is to control the feedback loop in which the solutioninfluences the nonlinearity, which in turn returns to influence thesolution.

it is bounded and measurable, hence F(u) is also bounded and measurable. Thus

the integraltt0

F(u(s))ds is Lipschitz continuous, and (since u solves (1.8) in the

sense of distributions) u(t) is also Lipschitz continuous, so it is a strong solution(we allow ourselves the ability to modify u on a set of measure zero). Then F(u)is continuous, and so the fundamental theorem of calculus and (1.8) again, u is infact in C1loc and is a classical solution.

The three perspectives of classical, strong, and weak solutions are all importantin the theory of ODE and PDE. The classical solution concept, based on the differ-ential equation (1.7), is particularly useful for obtaining conservation laws (Section1.4) and monotonicity formulae (Section 1.5), and for understanding symmetriesof the equation. The strong solution concept, based on the integral equation (1.8),is more useful for constructing solutions (in part because it requires less a prioriregularity on the solution), and establishing regularity and growth estimates on thesolution. It also leads to a very important perspective on the equation, viewingthe solution u(t) as being the combination of two influences, one coming from theinitial datum u0 and the other coming from the forcing term F(u); see Figure 2.Finally, the concept of a weak solution arises naturally when constructing solutionsvia compactness methods (e.g. by considering weak limits of classical solutions),

since continuity is nota prioripreserved by weak limits.To illustrate the strong solution concept, we can obtain the first fundamentaltheorem concerning such Cauchy problems, namely the Picard existence theorem.We begin with a simplified version of this theorem to illustrate the main point.

Theorem1.4 (Picard existence theorem, simplified version). LetDbe a finite-dimensional normed vector space. LetF C0,1(D D)be a Lipschitz function onD with Lipschitz constantFC0,1 =M. Let0< T


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Proof. Fixu0 Dand t0R, and let : C0(I D)C0(I D) be themap

(u)(t) := u0+ tt0

F(u(t))dt.

Observe from (1.8) that a strong solution is nothing more than a fixed point of themap . It is easy to verify that is indeed a map from C0(I D) toC0(I D).Using the Lipschitz hypothesis on Fand the triangle inequality, we obtain

(u)(t) (v)(t)D = tt0

F(u(t)) F(v(t))dtD tt0

Mu(t) v(t)D dt

for allt I and u, vC0(I), and thus(u) (v)C0(ID)T Mu vC0(ID).

Since we have T M 0. LetF :D Dbe a function which is Lipschitz on the closed


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Forcing term

Solution

Nonlinearity F

Constant evolution

Integration

in time

in0

u

(dilates by M)

(contracts by T)

F(u) in C

in Cu0

0

Initial datum

Figure 3. The Picard iteration scheme. The map is basicallythe loop from the solution u to itself. To obtain the fixed point,start with the initial datum u0 as the first approximant to u, andapply repeatedly to obtain further approximations to u. As longas the net contraction factor T M is less than 1, the iterationscheme will converge to an actual solution.

neighbourhood N() with some Lipschitz constantFC0,1(N()) = M > 0, andwhich is bounded by some A > 0 on this region. Let 0 < T < min(/A, 1/M),and letIbe the intervalI := [t0 T, t0+T]. Then for everyu0, there existsa strong (hence classical) solution u : I N() to the Cauchy problem (1.7).Furthermore, if we then define the solution maps St0(t) :

D for t

I and

St0 : C0(I D) by setting St0(t)(u0) := u(t) and St0(u0) := u, then St0(t)andSt0 are Lipschitz continuous maps, with Lipschitz constant at most

11TM.

Proof. Write := N() for short. For each u0 let u0 : C0(I) C0(I) be the map

u0(u)(t) :=u0+

tt0

F(u(s))ds.

As before, a strong solution to (1.7) is nothing more than a fixed point of the mapu0 . Since F is bounded by A on and T < /A, we see from the triangleinequality that u0 will indeed map C

0(I ) to C0(I ). Also, since Fhas Lipschitz constant at most M on , we may argue as in the proof of Theorem

1.4 and conclude that u0 will thus be a strict contraction on the complete metricspaceC0(I ) with contraction constant c:= T M


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Taking norms and applying the contraction property and the triangle inequality,we conclude

u uC0(ID)cu uC0(ID)+ u0 u0Dand hence

u uC0(ID) 11 cu0 u0D.

This proves the desired Lipschitz property on St0, and hence on each individualSt0(t).

Remark 1.8. The above theorem illustrates a basic point in nonlinear differ-ential equations: in order to construct solutions, one does not need to control thenonlinearityF(u) forallchoices of stateu, but only for thoseu that one expects toencounter in the evolution of the solution. For instance, if the initial datum is small,one presumably only needs to control F(u) for small u in order to obtain a localexistence result. This observation underlies many of the perturbative arguments

which we shall see in this text (see for instance Proposition 1.24 below).

Remark 1.9. In the next section we shall complement the Picard existencetheorem with a uniqueness theorem. The hypothesis that F is locally Lipschitz canbe weakened, but at the cost of losing the uniqueness; see Exercise 1.23.

Exercise1.1. Begin the proof of the Cauchy-Kowalevski theorem by reducingto the case k = 1, t0 = 0, and u0 = 0. Then, use induction to show that if thehigher derivatives mt u(0) are derived recursively as in (1.5), then we have somebound of the form

mt u(0)DKm+1m!for all m0 and some large K >0 depending on F, whereD is some arbitrarynorm on the finite-dimensional space D. Then, defineu : I D for some sufficientlysmall neighbourhoodIof the origin by the power series

u(t) =m=0

mt u(0)

m! tm

and show that tu(t) G(u(t)) is real analytic on Iand vanishes at infinite orderat zero, and is thus zero on all ofI.

Exercise 1.2. (Contraction mapping theorem) Let (X, d) be a complete non-empty metric space, and let : X Xbe a strict contraction on X, thus thereexists a constant 0 < c < 1 such that d((u), (v)) cd(u, v) for all u, v X.Show that has a unique fixed point, thus there is a unique u X such thatu = (u). Furthermore, if u0 is an arbitrary element ofXand we construct thesequenceu1, u2, . . .X iteratively by un+1 := (un), show that un will convergeto the fixed pointu. Finally, we have the bound(1.9) d(v, u) 1

1 c d(v, (v))for allvX.

Exercise1.3. (Inverse function theorem) Let Dbe a finite-dimensional vectorspace, and let C1loc(D D) be such that(x0) has full rank for somex0 D. Using the contraction mapping theorem, show that there exists an openneighbourhood U ofx0 and an open neighbourhood V of (x0) such that is abijection from U to V , and that 1 is alsoC1loc.


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1.2. GRONWALLS INEQUALITY 11

Exercise1.4. Suppose we make the further assumption in the Picard existencetheorem thatF Ckloc(D D) for some k 1. Show that the maps St0(t) andS(t) are then also continuously k-times differentiable, and that u C

k+1loc (I D).

Exercise 1.5. How does the Picard existence theorem generalise to higher or-der quasilinear ODE? What if there is time dependence in the nonlinearity (i.e. theODE is non-autonomous)? The latter question can also be asked of the Cauchy-Kowaleski theorem. (These questions can be answered quickly by using the reduc-tion tricks mentioned in this section.)

Exercise 1.6. One could naively try to extend the local solution given by thePicard existence theorem to a global solution by iteration, as follows: start withthe initial time t0, and use the existence theorem to construct a solution all theway up to some later time t1. Then useu(t1) as a new initial datum and apply theexistence theorem again to move forward to a later time t2, and so forth. What

goes wrong with this strategy, for instance when applied to the problem (1.6)?

1.2. Gronwalls inequality

It takes money to make money. (Proverbial)

As mentioned earlier, we will be most interested in the behaviour of ODE invery high dimensions. However, in many cases one can compress the key featuresof an equation to just a handful of dimensions, by isolating some important scalarquantities arising from the solution u(t), for instance by inspecting some suitablenorm u(t)Dof the solution, or looking at special quantities related to conservationor pseudoconservation laws such as energy, centre-of-mass, or variance. In manycases, these scalar quantities will not obey an exact differential equation themselves,but instead obey adifferential inequality, which places an upper limit on how quickly

these quantities can grow or decay. One is then faced with the task of solvingsuch inequalities in order to obtain good bounds on these quantities for extendedperiods of time. For instance, if a certain quantity is zero or small at some timet0, and one has some upper bound on its growth rate, one would like to say thatit is still zero or small at later times. Besides the iteration method used alreadyin the Picard existence theorem, there are two very useful tools for achieving this.One isGronwalls inequality, which deals with linear growth bounds and is treatedhere. The other is the continuity method, which can be used with nonlinear growthbounds and is treated in Section 1.3.

We first give Gronwalls inequality in an integral form.

Theorem 1.10 (Gronwall inequality, integral form). Let u : [t0, t1] R+ becontinuous and non-negative, and suppose thatu obeys the integral inequality

(1.10) u(t) A + tt0

B(s)u(s) ds

for al lt [t0, t1], whereA 0 andB : [t0, t1]R+ is continuous and nonnegative.Then we have

(1.11) u(t) A exp( tt0

B(s) ds)

for allt [t0, t1].


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Forcing term

Solution

uConstant evolution

Integration

in time

Initial bound

A

B uGrowth factor B

Figure 4. The linear feedback encountered in Theorem 1.10, thatcauses exponential growth by an amount depending on the growthfactorB . Contrast this with Figure 2.

Remark 1.11. This estimate is absolutely sharp, since the function u(t) :=

A exp(tt0

B(s)ds) obeys the hypothesis (1.10) with equality.

Proof. By a limiting argument it suffices to prove the claim when A >0. By(1.10) and the fundamental theorem of calculus, (1.10) implies

d

dt(A +

tt0

B(s)u(s)ds) B(t)(A+ tt0

B(s)u(s) ds)

and hence by the chain rule

d

dtlog(A +

tt0

B(s)u(s)ds) B(t).

Applying the fundamental theorem of calculus again, we conclude

log(A +

tt0

B(s)u(s) ds) log A + tt0

B(s) ds.

Exponentiating this and applying (1.10) again, the claim follows.

There is also a differential form of Gronwalls inequality in which B is allowedto be negative:

Theorem 1.12 (Gronwall inequality, differential form). Let u : [t0, t1]

R+

be absolutely continuous and non-negative, and suppose thatu obeys the differentialinequality

tu(t) B(t)u(t)for almost everyt [t0, t1], whereB : [t0, t1]R+ is continuous and nonnegative.Then we have

u(t) u(t0) exp( tt0

B(s) ds)

for allt [t0, t1].


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Proof. Writev(t) :=u(t) exp( tt0

B(s)ds). Thenvis absolutely continuous,

and an application of the chain rule shows thattv(t)

0. In particular v(t)

v(t0)

for allt [t0, t1], and the claim follows. Remark 1.13. This inequality can be viewed as controlling the effect of linear

feedback; see Figure 4. As mentioned earlier, this inequality is sharp in the worstcase scenario when tu(t) equalsB(t)u(t) for allt. This is the case of adversarialfeedback, when the forcing term B(t)u(t) is always acting to increase u(t) bythe maximum amount possible. Many other arguments in this text have a similarworst-case analysis flavour. In many cases (in particular, supercritical defocusingequations) it is suspected that the average-case behaviour of such solutions (i.e.for generic choices of initial data) is significantly better than what the worst-caseanalysis suggests, thanks to self-cancelling oscillations in the nonlinearity, but wecurrently have very few tools which can separate the average case from the worstcase.

As a sample application of this theorem, we have

Theorem 1.14 (Picard uniqueness theorem). LetIbe an interval. Suppose wehave two classical solutionsu, v C1loc(I D) to the ODE

tu(t) =F(u(t))

for someF C0,1loc (D D). Ifu andv agree at one time t0 I, then they agreefor all timest I.

Remark 1.15. Of course, the same uniqueness claim follows for strong or weaksolutions, thanks to Lemma 1.3.

Proof. By a limiting argument (writing Ias the union of compact intervals)it suffices to prove the claim for compact I. We can use time translation invarianceto set t0 = 0. By splitting Iinto positive and negative components, and using thechange of variablest t if necessary, we may take I= [0, T] for some T >0.

Here, the relevant scalar quantity to analyze is the distanceu(t)v(t)Dbetween u andv, whereD is some arbitrary norm onD. We then take the ODEfor u and v and subtract, to obtain

t(u(t) v(t)) = F(u(t)) F(v(t)) for all t[0, T]Applying the fundamental theorem of calculus, the hypothesis u(0) =v(0), and thetriangle inequality, we conclude the integral inequality

(1.12)

u(t)

v(t)

D t

0 F(u(s))

F(v(s))

D ds for allt

[0, T].

Since I is compact and u, v are continuous, we see that u(t) and v(t) range overa compact subset ofD. Since F is locally Lipschitz, we thus have a bound of theform|F(u(s)) F(v(s))| M|u(s) v(s)| for some finite M > 0. Inserting thisinto (1.12) and applying Gronwalls inequality (with A = 0), the claim follows.

Remark 1.16. The requirement that Fbe Lipschitz is essential; for instancethe non-Lipschitz Cauchy problem

(1.13) tu(t) = p|u(t)|(p1)/p; u(0) = 0


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t

u

0u

T+

T

Figure 5. The maximal Cauchy development of an ODE whichblows up both forwards and backwards in time. Note that in orderfor the time of existence to be finite, the solution u(t) must goto infinity in finite time; thus for instance oscillatory singularitiescannot occur (at least when the nonlinearity Fis smooth).

for some p > 1 has the two distinct (classical) solutions u(t) := 0 and v(t) :=max(0, t)p. Note that a modification of this example also shows that one cannotexpect any continuous or Lipschitz dependence on the initial data in such cases.

By combining the Picard existence theorem with the Picard uniqueness theo-rem, we obtain

Theorem 1.17 (Picard existence and uniqueness theorem). LetF C0,1loc (D D)be a locally Lipschitz function, lett0 Rbe a time, and letu0 Dbe an initialdatum. Then there exists a maximal interval of existence I = (T, T+) for some T < t0 < T+ +, and a unique classical solution u : I D to theCauchy problem (1.7). Furthermore, if T+ is finite, we haveu(t)D ast T+ from below, and similarly if T is finite then we haveu(t)D astT from above.

Remark 1.18. This theorem gives a blowup criterion for the Cauchy problem(1.7): a solution exists globally if and only if theu(t)D norm does not go toinfinity9 in finite time; see Figure 5. (Clearly, if

u(t)

D goes to infinity in finite

time, u is not a global classical solution.) As we shall see later, similar blowupcriteria (for various normsD) can be established for certain PDE.

Proof. We define Ito be the union of all the open intervals containing t0 forwhich one has a classical solution to (1.7). By the existence theorem, Icontains a

9We sometimes say that a solution blows up at infinity if the solution exists globally ast , but that the norm u(t)D is unbounded; note that Theorem 1.17 says nothing aboutwhether a global solution will blow up at infinity or not, and indeed both scenarios are easily seento be possible.


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neighbourhood oft0 and is clearly open and connected, and thus has the desiredform I = (T, T+) for some T < t0 < T+ +. By the uniquenesstheorem, we may glue all of these solutions together and obtain a classical solutionu : I D on (1.7). Now suppose for contradiction that T+ was finite, and thatthere was some sequence of times tn approachingT+ from below for whichu(t)Dstayed bounded. On this bounded set (or on any slight enlargement of this set) Fis Lipschitz. Thus we may apply the existence theorem and conclude that one canextend the solutionu to a short time beyond T+; gluing this solution to the existingsolution (again using the uniqueness theorem) we contradict the maximality ofI.This proves the claim forT+, and the claim for T is proven similarly.

The Picard theorem gives a very satisfactory local theory for the existence anduniqueness of solutions to the ODE (1.7), assuming of course that F is locallyLipschitz. The issue remains, however, as to whether the interval of existence

(T, T+) is finite or infinite. If one can somehow ensure thatu(t)D does notblow up to infinity at any finite time, then the above theorem assures us that theinterval of existence is all ofR; as we shall see in the exercises, Gronwalls inequalityis one method in which one can assure the absence of blowup. Another commonway to ensure global existence is to obtain a suitably coercive conservation law(e.g. energy conservation), which manages to contain the solution to a bounded set;see Proposition 1.24 below, as well as Section 1.4 for a fuller discussion. A thirdway is to obtain decay estimates, either via monotonicity formulae (see Section1.5) or some sort of dispersion or dissipation effect. We shall return to all of thesethemes throughout this monograph, in order to construct global solutions to variousequations.

Gronwalls inequality is causal in nature; in its hypothesis, the value of theunknown function u(t) at times t is controlled by its value at previous times 0 t. In many such cases, these inequalitieslead to no useful conclusion. However, if the feedback is sufficiently weak, and onehas some mild growth condition at infinity, one can still proceed as follows.

Theorem 1.19 (Acausal Gronwall inequality). Let 0 < < , 0 < < and , > 0 be real numbers. Let u : R R+ be measurable and non-negative,and suppose thatu obeys the integral inequality

(1.14) u(t) A(t) +R

min(e(st), e(ts))u(s) ds

for all t R, where A : R R+ is an arbitrary function. Suppose also that wehave the subexponential growth condition

suptR

e|t|u(t)< .


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Then if < min(, ) and is sufficiently small depending on ,,, , , wehave

(1.15) u(t) 2 supsR

min(e(st), e(ts))A(s).

for allt R.Proof. We shall use an argument similar in spirit to that of the contraction

mapping theorem, though in this case there is no actual contraction to iterate aswe have an integral inequalityrather than an integral equation. By raising and (depending on ,,) if necessary we may assume < min(, ). We willassume that there exists > 0 such that A(t) e|t| for all t R; the generalcase can then be deduced by replacingA(t) by A(t) + e|t| and then letting 0,noting that the growth of the e|t| factor will be compensated for by the decay ofthe min(e

(st), e(ts)) factor since 0, and 0 < < min(, ) be real numbers. Let (un)nZ be asequence of non-negative numbers such that

(1.17) unAn+ mZ

min(e(mn), e(nm))um


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for all tR, where (An)nZ is an arbitrary non-negative sequence. Suppose alsothat we have the subexponential growth condition

supnZ

une|n|


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(1.7). Also, show that the solution mapsSt0(t) : D Ddefined bySt0(u0) = u(t0)are locally Lipschitz, obey the time translation invariance St0(t) =S0(t t0), andthe group laws S0(t)S0(t) = S0(t+t) and S0(0) = id. (Hint: use Gronwalls in-equality to obtain bounds onu(t)D in the maximal interval of existence (T, T+)given by Theorem 1.17.) This exercise can be viewed as the limiting case p = 1 ofExercise 1.11 below.

Exercise 1.11. Letp >1, letDbe a finite-dimensional normed vector space,and letF C0,1loc (D D) have at mostpth-power growth, thus F(u)D 1+upDfor all u D. Let t0 R and u0 D, and let u : (T, T+) Dbe the maximalclassical solution to the Cauchy problem (1.7) given by the Picard theorem. Showthat ifT+ is finite, then we have the lower bound

u(t)Dp (T+ t)1/(p1)as t approaches T+ from below, and similarly for T. Give an example to show

that this blowup rate is best possible.Exercise 1.12 (Slightly superlinear equations). Suppose F C0,1loc (D D)

has at most x log x growth, thus

F(u)D (1 + uD) log(2 + uD)for allu D. Do solutions to the Cauchy problem (1.7) exist classically for all time(as in Exercise 1.10), or is it possible to blow up (as in Exercise 1.11)? In the lattercase, what is the best bound one can place on the growth ofu(t)D in time; inthe former case, what is the best lower bound one can place on the blow-up rate?

Exercise 1.13 (Persistence of constraints). Let u : I D be a (classical)solution to the ODE tu(t) = F(u(t)) for some time interval I and some FC0loc(

D D), and let H

C1loc(

D R) be such that

F(v), dH(v)

= G(v)H(v)

for someG C0loc(D R) and allv D; here we use(1.19) u,dH(v):= d

dH(v+ u)|=0

to denote the directional derivative of H at v in the direction u. Show that ifH(u(t)) vanishes for one time t I, then it vanishes for all t I. Interpret thisgeometrically, viewingFas a vector field and studying the level surfaces ofH. Notethat it is necessary that the ratio G betweenF,dHand Hbe continuous; it is notenough merely forF,dH to vanish whenever Hdoes, as can be seen for instancefrom the counterexampleH(u) = u2, F(u) = 2|u|1/2, u(t) = t2.

Exercise 1.14 (Compatibility of equations). Let F, G C1loc(D D) havethe property that

(1.20) F(v), dG(v) G(v), dF(v)= 0for all v D. (The left-hand side has a natural interpretation as the Lie bracket[F, G] of the differential operators F,G associated to the vector fields F andG.) Show that for any u0 D, there exists a neighbourhood B R2 of the origin,and a map u C2(B D) which satisfies the two equations(1.21) su(s, t) = F(u(s, t)); tu(s, t) = G(u(s, t))

for all (s, t)B, with initial datum u(0, 0) = u0. Conversely, ifuC2(B D)solves (1.21) on B, show that (1.20) must hold for all v in the range ofu. (Hint:


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use the Picard existence theorem to construct u locally on the s-axis{t = 0}by using the first equation of (1.21), and then for each fixed s, extend u in the

t direction using the second equation of (1.21). Use Gronwalls inequality and(1.20) to establish that u(s, t) u(0, t) s

0F(u(s, t)) ds = 0 for all (s, t) in a

neighbourhood of the origin.) This is a simple case ofFrobeniuss theorem, regardingwhen a collection of vector fields can be simultaneously integrated.

Exercise 1.15 (Integration on Lie groups). Let H be a finite-dimensionalvector space, and let G be a Lie group in End(H) (i.e. a group of linear transfor-mations onHwhich is also a smooth manifold). Let gbe the Lie algebra ofG (i.e.

the tangent space of G at the identity). Let g0 G, and let X C0,1loc (R g)be arbitrary. Show that there exists a unique function g C1loc(R G) suchthat g(0) = g0 and tg(t) = X(t)g(t) for all t R. (Hint: first use Gronwallsinequality and Picards theorem to construct a global solution g : R Mn(C)to the equation tg(t) = X(t)g(t), and then use Gronwalls inequality again,

and local coordinate patches of G, to show that g stays on G.) Show that thesame claim holds if the matrix product X(t)g(t) is replaced by the Lie bracket[g(t), X(t)] :=g(t)X(t) X(t)g(t).

Exercise 1.16 (Levinsons theorem). Let L C0(R End(D)) be a time-dependent linear transformation acting on a finite-dimensional Hilbert space D, andletF C0(R D) be a time-dependent forcing term. Show that for everyu0 Dthere exists a global solution u C0loc(R D) to the ODE tu = L(t)u+F(t),with the bound

|u(t)| (|u0|D+ t

0

|F(s)|D ds)exp( t

0

(L(t) + L(t))/2op dt)

for all t0. (Hint: control the evolution of|u(t)|2 =u(t), u(t)D.) Thus one canobtain global control on a linear ODE with arbitrarily large coefficients, as long asthe largeness is almost completely contained in the skew-adjoint component of thelinear operator. In particular, ifFand the self-adjoint component ofL are bothabsolutely integrable, conclude thatu(t) is bounded uniformly in t.

Exercise 1.17. Give examples to show that Theorem 1.19 and Corollary 1.20fail (even when A is identically zero) if or become too large, or if the hypothesisthatu has subexponential growth is dropped.

Exercise 1.18. Let , > 0, let d 1 be an integer, let 0 < d, and letu: Rd R+ and A: Rd R+ be locally integrable functions such that one hasthe pointwise inequality

u(x) A(x) + Rd

e|xy|

|x

y

|

u(y) dy

for almost everyx Rd. Suppose also thatu is a tempered distribution in additionto a locally integrable function. Show that if 0 < < and is sufficiently smalldepending on, , , then we have the bound

u(x)2e|xy|A(y)Ly (Rd)for almost every xRd. (Hint: you will need to regulariseu first, averaging on asmall ball, in order to convert the tempered distribution hypothesis into a pointwisesubexponential bound. Then argue as in Proposition 1.19. One can then take limitsat the end using the Lebesgue differentiation theorem.)


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Exercise 1.19 (Singular ODE). Let F, G C0,1(D D) be Lipschitz mapswith F(0) = 0 andFC0,1(DD) < 1. Show that there exists a T > 0 forwhich there exists a unique classical solution u : (0, T] D to the singularnon-autonomous ODE tu(t) =

1tF(u(t)) + G(u(t)) with the boundary condi-

tion lim supt0 u(t)D/t


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1.3. BOOTSTRAP AND CONTINUITY ARGUMENTS 21

0

Base case

H(t )

(closed)

H(t)

Hypothesis

Conclusion

C(t)can extend t by

an open set

("continuity")

"nonlinear"

implication

Figure 6. A schematic depiction of the relationship between thehypothesisH(t) and the conclusion C(t); compare this with Figure2. The reasoning is noncircular because at each loop of the iterationwe extend the set of times for which the hypothesis and conclusionare known to hold. The closure hypothesis prevents the iterationfrom getting stuck indefinitely at some intermediate time.

of the principle of mathematical induction - is known as the bootstrap principle orthecontinuity method11. Abstractly, the principle works as follows.

Proposition 1.21 (Abstract bootstrap principle). Let I be a time interval,and for each t I suppose we have two statements, a hypothesis H(t) and aconclusionC(t). Suppose we can verify the following four assertions:

(a) (Hypothesis implies conclusion) IfH(t) is true for some timetI, thenC(t) is also true for that timet.

(b) (Conclusion is stronger than hypothesis) If C(t) is true for some t I,then H(t) is true for allt I in a neighbourhood oft.

(c) (Conclusion is closed) If t1, t2, . . . is a sequence of times in I which con-verges to another time t I, and C(tn) is true for all tn, then C(t) istrue.

(d) (Base case) H(t) is true for at least one timetI.ThenC(t) is true for allt I.

Remark 1.22. When applying the principle, the propertiesH(t) and C(t) aretypically chosen so that properties (b), (c), (d) are relatively easy to verify, withproperty (a) being the important one (and the nonlinear one, usually provenby exploiting one or more nonlinear feedback loops in the equations under study).The bootstrap principle shows that in order to prove a property C(t) obeying (c),it would suffice to prove the seemingly easier assertion H(t) = C(t), as long asHis weaker than C in the sense of (b) and is true for at least one time.

11The terminology bootstrap principle arises because a solution u obtains its regularityfrom its own resources rather than from external assumptions - pulling itself up by its bootstraps,as it were. The terminology continuity method is used because the continuity of the solution isessential to making the method work.


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Proof. Let be the set of times tI for which C(t) holds. Properties (d)and (a) ensure that is non-empty. Properties (b) and (a) ensure that is open.

Property (c) ensures that is closed. Since the intervalIis connected, we thus seethat =I, and the claim follows.

More informally, one can phrase the bootstrap principle as follows:

Principle1.23 (Informal bootstrap principle). If a quantityu can be boundedin a nontrivial way in terms of itself, then under reasonable conditions, one canconclude thatu is bounded unconditionally.

We give a simple example of the bootstrap principle in action, establishingglobal existence for a system in a locally stable potential well from small initialdata.

Proposition 1.24. LetD be a finite-dimensional Hilbert space, and let VC2

loc

(D

R) be such that such thatV(0) = 0,

V(0) = 0, and

2V(0) is strictlypositive definite. Then for all u0, u1 D sufficiently close to 0, there is a uniqueclassical global solutionu C2loc(R D) to the Cauchy problem(1.23) 2t u(t) =V(u(t)); u(0) =u0; tu(0) =u1.Furthermore, this solution stays bounded uniformly int.

Remark1.25. The point here is that the potential well Vis known to be stablenear zero by hypothesis, but could be highly unstable away from zero; see Figure7. Nevertheless, the bootstrap argument can be used to prevent the solution fromtunnelling from the stable region to the unstable region.

Proof. Applying the Picard theorem (converting the second-order ODE intoa first-order ODE in the usual manner) we see that there is a maximal interval

of existence I = (T, T+) containing 0, which supports a unique classical solutionuC2loc(I D) to the Cauchy problem (1.23). Also, ifT+ is finite, then we havelimtT+ u(t)D+ tu(t)D =, and similarly ifT is finite.

For any time t I, letE(t) denote the energy(1.24) E(t) :=

1

2tu(t)2D+ V(u(t)).

From (1.23) we see that

tE(t) = tu(t), 2t u(t) + tu(t), V(u(t))= 0and thus we have the conservation law

E(t) =E(0) =1

2u12D+ V(u0).

If u0, u1 are sufficiently close to 0, we can thus make E(t) = E(0) as small asdesired.

The problem is that we cannot quite conclude from the smallness ofE thatuis itself small, becauseVcould turn quite negative away from the origin. However,such a scenario can only occur when u is large. Thus we need to assume that u issmall in order to prove thatu is small. This may seem circular, but fortunately thebootstrap principle allows one to justify this argument.

Let >0 be a parameter to be chosen later, and let H(t) denote the statement

tu(t)2D+ u(t)2D (2)2


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u

V(u)

22

Figure 7. The potential well V in Proposition 1.24. As long astu(t)2D+ u(t)2D is known to be bounded by (2)2, the Hamil-tonian becomes coercive and energy conservation will trap a parti-cle in the region tu(t)2D +u(t)2D 2 provided that the initialenergy is sufficiently small. The bootstrap hypothesis can be re-

moved because the motion of the particle is continuous. Withoutthat bootstrap hypothesis, it is conceivable that a particle coulddiscontinuously tunnel through the potential well and escape,without violating conservation of energy.

and let C(t) denote the statement

tu(t)2D+ u(t)2D2.Sinceu is continuously twice differentiable, and blows up at any finite endpoint ofI, we can easily verify properties (b) and (c) of the bootstrap principle, and ifu0and u1 are sufficiently close to 0 (depending on ) we can also verify (d) at timet= 0. Now we verify (a), showing that the hypothesisH(t) can be bootstrapped

into the stronger conclusion C(t). If H(t) is true, thenu(t)D = O(). We thensee from the hypotheses on V and Taylor expansion that

V(u(t)) cu(t)2D+ O(3)for somec >0. Inserting this into (1.24), we conclude

1

2tu(t)2D+ cu(t)2DE(0) + O(3).

This is enough to imply the conclusion C(t) as long as is sufficiently small, andE(0) is also sufficiently small. This closes the bootstrap, and allows us to conclude


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2A

A

u

A + F(u)

u

hereu forbidden

2A

Figure 8. A depiction of the situation in Exercise 1.21. Note theimpenetrable barrier in the middle of the u domain.

that C(t) is true for all t I. In particular, I must be infinite, since we knowthattu(t)2D + u(t)2D would blow up at any finite endpoint of I, and we aredone.

One can think of the bootstrap argument here as placing an impenetrable

barrier2


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for some A, > 0 and some function F : R+ R+ which is locally bounded.Suppose also that u(t0)2A for some t0 I. If is sufficiently small dependingon A and F, show that in fact u(t)2A for all t I. Show that the conclusioncan fail ifu is not continuous or is not small. Note however that no assumptionis made on the growth of F at infinity. Informally speaking, this means that ifone ever obtains an estimate of the form u A+ F(u), then one can drop theF(u) term (at the cost of increasing the main term A by a factor of 2) providedthat is suitably small, some initial condition is verified, and some continuity isavailable. This is particularly useful for showing that a nonlinear solution obeysalmost the same estimates as a linear solution if the nonlinear effect is sufficientlyweak. Compare this with Principle 1.23.

Exercise 1.22. Let Ibe a time interval, and let uC0loc(I R+) obey theinequality

u(t) A + F(u(t)) + Bu(t)for someA, B, >0 and 0< 0. Show that if issufficiently small depending onA, A, B , , F , then we haveu(t) A+B1/(1) forall tI. Thus we can tolerate an additionalu-dependent term on the right-handside of (1.25) as long as it grows slower than linearly inu.

Exercise 1.23 (Compactness solutions). Let t0 R be a time, let u0 D,and let F :D D be a function which is continuous (and hence bounded) in aneighbourhood of u0. Show that there exists an open time interval I containingt0, and a classical solution u C1(I D) to the Cauchy problem (1.7). (Hint:approximateFby a sequence of Lipschitz functions Fm and apply Theorem 1.17to obtain solutions um to the problem tum =Fm(um) on some maximal interval(T,m, T+,m). Use a bootstrap argument and Gronwalls inequality to show that

for some fixed open interval I (independent ofm) containing t0, the solutions umwill stay uniformly bounded and uniformly Lipschitz (hence equicontinuous) in thisinterval, and that this interval is contained inside all of the (T,m, T+,m). Thenapply the Arzela-Ascoli theorem to extract a uniformly convergent subsequence ofthe um on I, and see what happens to the integral equations um(t) = um(t0) +tt0

Fm(um(s)) ds in the limit, using Lemma 1.3 if necessary.) This is a simple

example of acompactness methodto construct solutions to equations such as (1.13),for which uniqueness is not available.

Exercise1.24 (Persistence of constraints, II). Let u: [t0, t1] D be a classicalsolution to the ODE tu(t) = F(u(t)) for some continuous F :D D, and letH1, . . . , H n C1loc(D R) have the property that

F(v), dHj(v) 0whenever 1 j n and v D is such that Hj(v) = 0 and Hi(v) 0 for all1 i n. Show that if the statement

Hi(u(t))0 for all 1 inis true at time t = t0, then it is true for all times t[t0, t1]. Compare this resultwith Exercise 1.13.

Exercise 1.25 (Forced blowup). Let k 1, and let u : [0, T) R be aclassical solution to the equationktu(t) = F(u(t)), whereF :RRis continuous.


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Suppose thatu(0)> 0 and jt u(0) 0 for all 1j < k, and suppose that one hasthe lower bound such that F(v) vp for all v u(0) and some p > 1. Concludethe upper bound Tp,k u(0)

(1p)/k

on the time of existence. (Hint: first establishthatu(t)u(0) and jt u(t)0 for all 1j < k and 0t < T, for instance byusing Exercise 1.24. Then bootstrap these bounds to obtain some estimate on thedoubling time ofu, in other words to obtain an upper bound on the first timet forwhich u(t) reaches 2u(0).) This shows that equations of the formktu(t) = F(u(t))can blow up if the initial datum is sufficiently large and positive.

Exercise 1.26. Use the continuity method to give another proof of Gronwallsinequality (Theorem 1.10). (Hint: for technical reasons it may be easier to first

prove thatu(t) (1 + )A exp(tt0

B(s)ds) for each >0, as continuity arguments

generally require an epsilon of room.) This alternate proof of Gronwalls inequal-ity is more robust, as it can handle additional nonlinear terms on the right-handside provided that they are suitably small.

1.4. Noethers theorem

Now symmetry and consistency are convertible terms - thus Poetryand Truth are one. (Edgar Allen Poe, Eureka: A Prose Poem)

A remarkable feature of many important differential equations, especially thosearising from mathematical physics, is that their dynamics, while complex, still con-tinue to maintain a certain amount of unexpected structure. One of the mostimportant examples of such structures are conservation laws- certain scalar quan-tities of the system that remain constant throughout the evolution of the system;another important example aresymmetriesof the equation - that there often existsa rich and explicit group of transformations which necessarily take one solutionof the equation to another. A remarkable result of Emmy Noether shows that

these two structures are in fact very closely related, provided that the differentialequation isHamiltonian; as we shall see, many interesting nonlinear dispersive andwave equations will be of this type. Noethers theorem is one of the fundamentaltheorems of Hamiltonian mechanics, and has proven to be extremely fruitful in theanalysis of such PDE. Of course, the field of Hamiltonian mechanics offers manymore beautiful mathematical results than just Noethers theorem; it is of great in-terest to see how much else of this theory (which is still largely confined to ODE)can be extended to the PDE setting. See [Kuk3] for some further discussion.

Noethers theorem can be phrased symplectically, in the context of Hamiltonianmechanics, or variationally, in the context of Lagrangian mechanics. We shall optto focus almost exclusively on the former; the variational perspective has certainstrengths for the nonlinear PDE we shall analyse (most notably in elucidating the

role of the stress-energy tensor, and of the distinguished role played by groundstates) but we will not pursue it in detail here (though see Exercises 1.44, 1.45,2.60). We shall content ourselves with describing only a very simple special case ofthis theorem; for a discussion of Noethers theorem in full generality, see [Arn].

Hamiltonian mechanics can be defined on any symplectic manifold, but forsimplicity we shall restrict our attention to symplectic vector spaces.

Definition 1.26. Asymplectic vector space (D, ) is a finite-dimensional realvector spaceD, equipped with a symplectic form :D D R, which is bilinearand anti-symmetric, and also non-degenerate (so for each non-zero u D there


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1.4. NOETHERS THEOREM 27

exists a v Dsuch that (u, v) = 0). Given anyHC1loc(D R), we define thesymplectic gradientH C0loc(D D) to be the unique function such that

(1.26) v,dH(u)= dd

H(u + v)|=0= (H(u), v);this definition is well-defined thanks to the non-degeneracy of and the finitedimensionality ofD. Given two functions H, E C1loc(D R), we define thePoisson bracket{H, E} :D R by the formula(1.27) {H, E}(u) := (H(u), E(u)).A Hamiltonian functionon a phase space (D, ) is any function12 H C2loc(D R); to each such Hamiltonian, we associate the corresponding Hamiltonian flow

(1.28) tu(t) =H(u(t)).Note that with this definition, Hamiltonian ODE (1.28) are automatically au-

tonomous (time-translation-invariant). However it is possible to consider time-varying Hamiltonians also: see Exercise 1.42. Note that the Hamiltonian of anequation is only determined up to a constant, since replacingHbyH+ Cdoes notaffect the symplectic gradient ofH.

Example 1.27. If

D:= Rn Rn ={(q1, . . . , q n, p1, . . . , pn) : q1, . . . , q n, p1, . . . , pn R}for somen 1, and :D D R is the bilinear form

:=nj=1

dqj dpj

or in other words

((q1, . . . , q n, p1, . . . , pn), (q1, . . . , q

n, p

1, . . . , p

n)) :=

nj=1

pjqjpjqj

then (D, ) is symplectic, and for any H, E C1loc(D R) we have

H= ( Hp1

, . . . ,H

pn, H

q1, . . . , H

qn)

and

{H, E} =nj=1

H

pj

E

qj H

qj

E

pj.

In particular, the Hamiltonian ODE associated to a Hamiltonian function H

C2loc(D R) is given by Hamiltons equations of motion(1.29) tqj(t) =

H

pj(q(t), p(t)); tpj(t) =H

qj(q(t), p(t)),

where we write

u(t) = (q(t), p(t)); q(t) = (q1(t), . . . , q n(t)); p(t) = (p1(t), . . . , pn(t)).

12One can weaken this hypothesis of continuous twice differentiability and still define aHamiltonian flow, but the theory becomes more delicate and we will not address it here.


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Thus, for instance, ifH takes the form

(1.30) H(q, p) =

1

2m |p|2

+ V(q)

where|p|2 := p21+ . . .+ p2n, m > 0 is a constant, and V C2loc(Rn R), thenHamiltons equations become Newtons laws of motion

tq(t) = 1

mp(t); tp(t) =V(q(t)).

Example 1.28. LetD= Cn be endowed with the symplectic form

(1.31) :=nj=1

1

2Im(dzj dzj)

or in other words

((z1, . . . , zn), (z1, . . . , z

n)) :=

nj=1

Im(zjzj).

Then for any H, EC1loc(D R) we have

H= (2i Hz1

, . . . , 2iH

zn)

and

{H, E}= 4nj=1

Im(H

zj

E

zj)

where H

z := H

x iH

y and H

z := H

x + iH

y . Thus for instance ifHis the simpleharmonic oscillator

(1.32) H(z) :=nj=1

1

2j |zj|2

for some1, . . . , n R, then the equations of motion aretzj(t) =ijzj(t).

This is in fact the canonical form for any quadratic Hamiltonian; see Exercise1.41.

Hamiltonian equations enjoy a number of good properties. Since H C2loc(D R), the function

H

C0,1loc (

D D) is locally Lipschitz, and so the Picard

existence theorem applies; in particular, for any bounded set D, there is apositive timeT >0 for which we have Lipschitz flow maps S(t) = S0(t) C0,1(D) defined for|t| T. In the quadratic growthcase when2H is bounded, thenHis globally Lipschitz, and one thus has global flow maps S(t) C0,1loc (D D)defined for all times t R (by Exercise 1.10). These maps obey the group lawsS(t+ t) = S(t)S(t), S(0) = id. Furthemore the S(t) are diffeomorphisms andsymplectomorphisms; see Exercise 1.4 and Exercise 1.32.

Let H, E C2loc(D R) be two Hamiltonians on a symplectic phase space(D, ), and let u C1loc(I D) be a classical solution to the Hamiltonian ODE


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(1.28). From the chain rule and (1.26), (1.27), we thus have the Poisson equation13

(1.33) d

dt E(u(t)) ={H, E}(u(t)).Let us say that a quantity E is conserved by (1.28) if E(u(t)) is constant forany (classical) solution u : I D of (1.28). From (1.33) and the anti-symmetry{H, E} = {E, H}of the Poisson bracket, we conclude

Theorem 1.29 (Noethers theorem). LetH andEbe two Hamiltonians on asymplectic phase space(D, ). Then the following are equivalent.

(a){H, E} = 0.(b) The quantityEis conserved by the Hamiltonian flow ofH.(c) The quantityHis conserved by the Hamiltonian flow ofE.

If any of the above three properties hold, we say that H and EPoisson com-

mute. As stated, Noethers theorem is symmetric in H and E. However, thistheorem is often interpreted in a somewhat asymmetric way. Assume for sake ofargument that the flow mapsSE(t) ofEare globally defined (this is the case, for in-stance, ifEis quadratic growth). We view the flow mapsSE(t) as a one-dimensionalgroup actionon the phase spaceD. Noethers theorem then asserts that Eis a con-served quantity for the equation (1.28) if and only ifH issymmetric(i.e. invariant)with respect to the group actionsSE(t); for a generalisation to higher-dimensionalgroup actions, see Exercise 1.34. Thus this theorem gives a very satisfactory linkbetween the symmetries of the Hamiltonian Hto the conserved quantities of theflow (1.28). The larger the group of symmetries, the more conserved quantities oneobtains14.

For instance, since H clearly Poisson commutes with itself, we see that H

itself is a conserved quantity, thus H(u(t0)) = H(u0) for any classical solutionu C1loc(I D) to the Cauchy problem(1.34) tu(t) = H(u(t)); u(t0) = u0.As another example, if (D, ) is the complex phase space given in Example 1.28,and the Hamiltonian is invariant under phase rotations, thus

H(eiz1, . . . , eizn) = H(z1, . . . , zn) for all z1, . . . , zn C, R

then the total chargenj=1 |zj |2 is conserved by the flow. Indeed, the phase rotation

is (up to a factor of two) nothing more than the Hamiltonian flow associated to thetotal charge.

Another class of important examples concerns the phase space (Rd

Rd)N of

Nparticles in Rd, parameterised by N position variables q1, . . . , q N Rd and N

13This equation is unrelated to the PDE u = , which is sometimes also referred to asPoissons equation.

14Provided, of course, that the symmetries themselves come from Hamiltonian flows. Certainsymmetries, notably scaling symmetries, are thus difficult to place in this framework, as they

typically violate Louvilles theorem and thus cannot be Hamiltonian flows, though they do tendto generate almost conserved quantities, such as that arising in the virial identity. Also, discretesymmetries such as time reversal symmetry or permutation symmetry also are not in the rangeof applicability for Noethers theorem.


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Table 1. Some common symmetry groups and their associatedconservation laws (or approximate conservation laws, in the case of

the virial identity). Not all of these follow directly from Noetherstheorem as stated, and are best viewed instead using the La-grangian approach to this theorem. In some cases, the interpre-tation of the conserved quantity depends on the equation; for in-stance spatial translation corresponds to momentum for wave andSchrodinger equations, but corresponds instead to mass for KdVtype equations.

Symmetry Conserved quantitytime translation energy / Hamiltonianspatial translation momentum / massspatial rotation angular momentumGalilean transformation (renormalised) centre-of-mass

Lorentz transformation (renormalised) centre-of-energyscaling (virial identity)base space diffeomorphism stress-energyphase rotation mass / probability / chargegauge transform charge

momentum variables p1, . . . , pN Rd, with the symplectic form

:=Nj=1

dqj dpj =Nj=1

di=1

dqj,i dpj,i.

If a Hamiltonian H(q1, . . . , q N, p1, . . . , pN) is invariant under spatial translations,

thus

H(q1 x , . . . , q N x, p1, . . . , pN) =H(q1, . . . , q N, p1, . . . , pN)for allx, p1, . . . , pN, q1, . . . , q N Rd, then Noethers theorem implies that the totalmomentum p =

Nj=1pj is conserved by the flow. If the Hamiltonian takes the

form

(1.35) H(q1, . . . , q N, p1, . . . , pN) =Nj=1

1

2mi|pi|2 + V(q1, . . . , q N)

for some (translation invariant) potential V C2loc((Rd)N R), then the totalmomentum takes the familiar form

p=

Nj=1

mj dqjdt

.

Similarly, if the Hamiltonian is invariant under angular rotations U : Rd Rd,thus

H(U q1, . . . , U q N, U p1, . . . , U pN) = H(q1, . . . , q N, p1, . . . , pN)

for allp1, . . . , pN, q1, . . . , q N Rd andU SO(d), then Noethers theorem (or moreprecisely the generalisation in Exercise 1.34) implies that the angular momentum

L:=Nj=1 qjpj

2Rd is also preserved by the flow.


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H = const

z(t)

i |z(t)| z(t)2

Figure 9. The complex scalar OD

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