LECTURE NOTES ON QUANTUM CHAOS COURSE , MIT, APRIL 2016, VERSION 2

Size: px
Start display at page:

Download "LECTURE NOTES ON QUANTUM CHAOS COURSE , MIT, APRIL 2016, VERSION 2"

Transcription

1 LECTUE NOTES ON QUANTUM CHAOS COUSE , MIT, APIL 2016, VESION 2 SEMYON DYATLOV Abstract. We give an overview of te quantum ergodicity result. 1. Quantum ergodicity in te pysical space 1.1. Concentration of eigenfunctions. First, let us consider te case wen M 2 is a bounded domain wit piecewise C boundary and we take te operator = 2 x 1 2 x 2. We study te Diriclet eigenvalues λ 0 < λ 1 λ 2... (wit multiplicities taken into account) and te corresponding L 2 normalized eigenfunctions u j H 1 0(M), so u j = λ 2 ju j, u j M = 0, u j L 2 (M) = 1. (1.1) We are interested in te following question regarding te ig energy limit: Question 1.1. How do u j concentrate as j? In general, u j become rapidly oscillating at ig energies, so we ave to study teir concentration in some roug sense. A natural way to do tat is to take te weak limits of te measures u j (x) 2 dx along subsequences: Definition 1.2. Let u jk be a subsequence of (u j ) and µ a probability measure on M. We say tat u jk µ weakly, if a(x) u jk (x) 2 dx a(x) dµ(x) for all a C0 (M) (1.2) We say tat u jk M M equidistributes in M, if it converges weakly to te volume measure: u jk dx Vol(M). (1.3) emarks. 1. By a standard density argument, once (1.2) olds for all a C 0 (M), it olds for all a C(M). 2. By a diagonal argument (see [Zw, Teorem 5.2]), tere always exists a subsequence of u j converging to some measure. 1

2 2 SEMYON DYATLOV As a basic example, consider te square M = [0, 1] 2. Te Diriclet eigenfunctions ave te form u jl (x 1, x 2 ) = 2 sin(jπx 1 ) sin(lπx 2 ), j, l N; λ jl = π j 2 + l 2. Exercise 1.3. Sow tat as j, u jj dx 1 dx 2 ; u 1j 2 sin 2 (πx 1 ) dx 1 dx 2, tat is te sequence u jj equidistributes in M but te sequence u 1j does not. More generally, we will consider te case wen (M, g) is a compact iemannian manifold wit piecewise smoot boundary and replace by te Laplace Beltrami operator g wic can be defined using te identity du, dv g d Vol g = ( g u)v d Vol g, u, v C0 (M). M M Te generalization of te measure (1.3) to tis case is given by te iemannian volume measure d Vol g Vol g (M). Exercise 1.4. Let M = S 2 be te two-dimensional spere embedded into 3. (a) Using te expression for Laplacian on 3 in sperical coordinates, sow tat eac omogeneous armonic polynomial v on 3 of degree m, te restriction u := v S 2 is an eigenfunction of S 2 wit eigenvalue m(m + 1). (In fact, wit a bit more work one can see tat all eigenfunctions of S 2 are obtained in tis way.) (b) Using te coordinates (x 1, x 2, x 3 ) in n, define for eac m N 0, v ± m = (x 1 ± ix 2 ) m, u ± m := c m v ± m S 2. were te constant c m is cosen so tat u ± m L 2 (S 2 ) = 1. Sow tat as m, u ± m converge weakly to a probability measure on S 2 wic is supported on te equator {x x 2 2 = 1, x 3 = 0}. We see tat te limit of u jk may depend on te coice of te sequence. It turns out tat te limits in fact also depend in an essential way on te dynamics of a natural flow on (M, g), and quantum caos studies in particular ow te dynamical properties of (M, g) influence te beavior of eigenstates.

3 LECTUE NOTES ON QUANTUM CHAOS Caotic dynamics and ergodicity. For (M, g) a iemannian manifold, define te unit cotangent bundle Wen M is a domain in 2, we can write S M = {(x, ξ) T M : ξ g = 1}. S M = {(x, ξ) M 2 : ξ = 1} and parametrize tis space by (x 1, x 2, θ) were ξ = (cos θ, sin θ). (Te unit tangent and cotangent bundles can be identified wit eac oter using te metric g, and it will become apparent later wy it is muc more convenient for us to use te cotangent bundle ere.) We consider te geodesic billiard ball flow on M, ϕ t : S M S M, t. For M a domain in 2, every trajectory of ϕ t follows a straigt line wit velocity vector ξ until it its te boundary, wen it bounces off by te law of reflection. For (M, g) a iemannian manifold, straigt lines are replaced by geodesics induced by te metric g. If M as a boundary, ten te resulting map is not continuous and it is defined everywere except a measure zero set in S M, corresponding to trajectories tat eiter it non-smoot parts of te boundary or become tangent to te boundary. We will ignore tese issues in our note and send te reader to [ZeZw] for a detailed explanation of ow tey can be andled. A natural probability measure on S M is te Liouville measure, defined for a general iemannian manifold by dµ L = d Vol g(x)dµ S n 1(ξ), n = dim M, Vol g (M) Vol(S n 1 ) were µ S n 1 is te standard surface measure on te spere, transported to a measure on eac fiber of S M. For M a domain in 2, in coordinates (x 1, x 2, θ) we ave dµ L = dx 1dx 2 dθ 2π Vol(M). Te measure µ L is invariant under te flow: µ L (ϕ t (U)) = µ L (U), U S M, t. We now introduce te notion of ergodicity for te flow ϕ t, wic is a rater weak way of saying tat ϕ t is a caotic flow: Definition 1.5. We say tat ϕ t is ergodic wit respect to µ L, if for eac flow invariant set U S M; ϕ t (U) = U, t, we ave eiter µ L (U) = 0 or µ L (U) = 1.

4 4 SEMYON DYATLOV One important consequence of ergodicity is te following statement about ergodic averages a T := 1 T T 0 a ϕ t dt, T > 0, a L 1 (S M; µ L ). (1.4) Teorem 1 (L 2 ergodic teorem). Assume tat ϕ t is ergodic wit respect to µ L. Ten for eac a L 2 (S M; µ L ), a T a dµ L in L 2 (S M; µ L ). S M Proof. We will only sketc te proof, sending te reader to [Zw, Teorem 15.1] for an alternative proof, and we restrict ourselves to te case wen M as no boundary. Consider te vector field X on S M generating te flow, so tat ϕ t = exp(tx). Tis vector field gives rise to a first order differential operator, still denotes X. Since µ L is a ϕ t -invariant measure, we ave L X µ L = 0 and tus ix is an unbounded self-adjoint operator on L 2 (S M). Let de X be te spectral measure of ix, wic is an operator-valued measure on wic is constructed via te spectral teorem for unbounded self-adjoint operators. Ten for a L 2 (S M; µ L ), a ϕ t = exp(tx)a = e itλ de X (λ)a. Terefore, for T > 0 a T = ( 1 T T 0 ) e itλ dt de X (λ)a = e it λ 1 it λ de X(λ)a. Now te function eit λ 1 is bounded uniformly in T, λ, and it as te pointwise in λ it λ limit { e it λ 1 1, λ = 0; 1l {0} (λ) = as T. it λ 0, λ 0, Since integral over te spectral measure is a strongly continuous function of te interval, one can see from ere tat a T de X (λ)a in L 2 (S M, µ L ). (1.5) {0} Te rigt-and side is te ortogonal projection of a onto te space V 0 L 2 (S M, µ L ) of functions satisfying te equation Xf = 0. However, for eac suc f we ave f ϕ t = ϕ t and tus te sublevel sets {f c} are invariant under te flow (modulo a measure zero set wic can be removed). By ergodicity, V 0 must ten consist of

5 LECTUE NOTES ON QUANTUM CHAOS 5 constant functions. Ten te rigt-and side of (1.5) is te integral of a wit respect to µ L, finising te proof. Exercise 1.6. Sow tat neiter [0, 1] 2 nor S 2 ave ergodic ϕ t. (Hint: on S 2, te angular momentum wit respect to any axis gives a conserved quantity. Any sublevel set of tis function will be invariant under te flow.) Tere are many important examples of ergodic systems, including Sinai billiards; Bunimovic stadiums; iemannian manifolds (M, g) witout boundary wic ave negative sectional curvature, in particular closed negatively curved surfaces Statement of quantum ergodicity. Te following teorem (togeter wit its generalizations is Teorems 4, 8 below) is te main result to be proved in tis course: Teorem 2 (Quantum ergodicity in te pysical space). Assume tat ϕ t is ergodic wit respect to µ L. Ten tere exists a density 1 subsequence λ jk, tat is #{k λ jk } #{j λ j } suc tat u jk equidistributes in M: u jk 1 as, d Vol g Vol g (M). Tis teorem was stated by Snirelman [S] and proved by Zelditc [Ze] and Colin de Verdère [CdV]. Te case of te domains wit boundary was establised by Zelditc Zworski [ZeZw]. See [Zw, Teorem 15.5] for a detailed proof in te boundaryless case. (All of te results mentioned above prove te more general Teorems 4,8.) We see tat Teorem 2 uses information about te cotangent bundle on M to derive a statement on te manifold M itself. It turns out tat to prove it, we sould generalize te statement of equidistribution to T M, wic we call te pase space. 2. Pase space concentration and proof of quantum ergodicity We will encefort assume tat M as no boundary, referring te reader to [ZeZw] for te boundary case Semiclassical quantization. Assume tat a C 0 (T M). Semiclassical quantization associates to a, wic is called symbol or classical observable, an operator Op (a) : L 2 (M) L 2 (M)

6 6 SEMYON DYATLOV wic is called a semiclassical pseudodifferential operator or quantum observable. Tis procedure depends on a parameter > 0, called te semiclassical parameter, and we will be interested in te limit 0. Originally referred to (a dimensionless version of) Planck constant; in general it is te wavelengt at wic we want to study our eigenfunctions. We will not give a definition of Op (a) ere but will instead send te reader to [Zw, Capters 4 and 14], and will give some explanations regarding semiclassical quantization later in te course. We remark tat te procedure is independent of te coice of coordinates on M only modulo an O() remainder in te symbol, but te defined class of operators is geometrically invariant. In fact we may define Op (a) for a in a more general class S m (T M), m, given by te conditions a S m (T M) α x β ξ a(x, ξ) C αβ(1 + ξ ) m β. Te resulting operator acts on Sobolev spaces Op (a) : H s (M) H s m (M), s. We note tat if a(x, ξ) is a polynomial in ξ, a(x, ξ) = a γ (x)ξ γ, γ m a γ (x) C (M), ten Op is a differential operator; on n, te standard quantization procedure gives Op (a) = a γ (x)(d x ) γ, D x = 1 i x. (2.1) γ m In particular, if a(x, ξ) = a(x), ten we get a multiplication operator and on n, Op (a)u(x) = a(x)u(x), Op (ξ j ) = D xj = i x j. Also, if X is a vector field on M, ten we ave i X = Op (p X ) + O(), p X (x, ξ) = ξ, X(x). Tis explains wy our symbols are functions on te cotangent bundle rater tan te tangent bundle a vector field naturally gives a linear function on te fibers of te cotangent bundle. We list below some fundamental properties of te quantization operation. We leave te remainders ambiguous, but tey will ave appropriate mapping properties in Sobolev spaces.

7 LECTUE NOTES ON QUANTUM CHAOS 7 Teorem 3. For a S m (T M), b S k (T M), we ave Op (a) = Op (ā) + O(), (2.2) Op (a) Op (b) = Op (ab) + O(), (2.3) [Op (a), Op (b)] = i Op ({a, b}) + O( 2 ), (2.4) were {a, b} is te Poisson bracket, given in coordinates by {a, b} = j ( ξj a xj b xj a ξj b). Moreover, for a S 0 (T M) te operator norm of Op (a) on L 2 can be estimated as follows [Zw, Teorem 5.1]: for some constant C independent of a,, lim sup Op (a) L 2 L 2 C a L (T M). (2.5) Quantum ergodicity in pase space. We now generalize Teorem 2 to a statement about quantum observables Op (a)u j, u j L 2, a S 0 (T M). For tat we need to pick te value of and it will be convenient to put j = 1 λ j. We ten define V j (a) := Op j (a)u j, u j L 2 (M), a S 0 (T M). Definition 2.1 (Weak limits in pase space). Let u jk be a subsequence of u j and µ be a measure on T M. We say tat u jk µ in te sense of semiclassical measures, if V jk (a) a dµ for all a S 0 (T M). T M We say tat u jk equidistribute in pase space if tey converge to te Liouville measure: u jk µ L. emark. Tere is always a subsequence converging to some measure, and all resulting measures are supported on te unit cospere bundle S M and invariant under te flow ϕ t see [Zw, Capter 5] and (2.8), (2.10) below. Teorem 4 (Quantum ergodicity in pase space). Assume ϕ t is ergodic wit respect to µ L. Ten tere exists a density 1 subsequence u jk suc tat u jk equidistribute in te pase space.

8 8 SEMYON DYATLOV Teorem 2 follows from ere by taking a to be a function of x, so tat V j (a(x)) = a(x) u j (x) 2 dx, and using te fact tat te pusforward of µ L to M is te volume measure: 1 a(x) dµ L = a(x) d Vol g. Vol g (M) S M In te rest of tis section, we prove Teorem 4, following several steps. M 2.3. Step 1: using te eigenfunction equation. We first rewrite te eigenfunction equation in te form were te symbol is cosen so tat g u j = λ 2 ju j M Op j (p)u j = 0 (2.6) p(x, ξ) = p 0 (x, ξ) + O(), p 0 (x, ξ) = ξ 2 g 1 2 P = Op (p) = 2 g 1. 2 Define te Hamiltonian vector field (2.7) H p0 = j ξj p xj xj p ξj, and note tat Define te flow H p0 a = {p 0, a}, ϕ t = exp(th p0 ), a C (T M). ten (explaining te coice of 1 2 in te definition of p 0) te restriction of ϕ t to S M is te geodesic flow. A key tool in te proof is te Scrödinger propagator ( U(t) = U(t; ) = exp itp ) : L 2 (M) L 2 (M). It quantizes te flow ϕ t as made precise by te following Teorem 5 (Egorov s Teorem). For a C 0 (T M), we ave U( t) Op (a)u(t) = Op (a ϕ t ) + O() L 2 (M) L 2 (M).

9 LECTUE NOTES ON QUANTUM CHAOS 9 Proof. We only sketc te proof, see [Zw, Teorem 15.2] for details. It is enoug to prove tat, denoting a t := a ϕ t, t ( U(t) Op (a t )U( t)) = O() L 2 (M) L 2 (M) Te left-and side is ( U(t) Op ( t a t ) i [ ]) P, Op (a t ) U( t) By (2.4), tis becomes U(t) Op ( t a t {p 0, a t })U( t) + O() and it remains to use tat t a t = {p 0, a t }. We ten ave te following Lemma 2.2. Assume tat a C0 (T M). Ten for any T > 0, ( ) V j (a) = V j a T + OT ( j ) were a T is defined in (1.4) and te constant in te remainder depends on T. Proof. We ave for eac t, U(t; j )u j = u j and tus V j (a) = Op j (a)u j, u j L 2 = U( t; j ) Op j (a)u(t; j )u j, u j L 2 = Op j (a ϕ t )u j, u j L 2 + O t ( j ) = V j (a ϕ t ) + O t ( j ) and it remains to average bot sides over t [0, T ]. (2.8) Tis statement uses te fact tat u j are eigenfunctions and features ergodic averages along te flow ϕ t Step 2: basic bounds. We record ere a few standard bounds on V j (a). First of all, by (2.5) we ave for some global constant C and eac a S 0 (T M), Moreover, if a vanises on S M, ten as follows immediately from lim sup V j (a) C a L (T M). (2.9) j lim V j(a) = 0 (2.10) j Lemma 2.3 (Elliptic bound). Assume a S 0 (T M) and a S M = 0. Ten as j, Op j (a)u j L 2 = O( j ).

10 10 SEMYON DYATLOV Proof. Since a vanises on S M, we may write a = bp 0 = bp + O(), were p, p 0 are defined in (2.7) and b S 2 (T M). Ten by (2.3), Op j (a) = Op j (b) Op j (p) + O( j ) L 2 L 2. Since Op j (p)u j = 0 by (2.6), te proof is finised Step 3: bounding averages over eigenfunctions. We know by Teorem 1 tat for large T, te average a T is close to te integral of a, but only in L 2 (T M). If we ad an L estimate instead, ten we could use (2.9) to control V j (a) for all j in te limit j. However, ergodic averages typically do not converge in L (tis can be seen for instance by considering a closed geodesic). Terefore we will ave to make te best out of te L 2 bound on a. It turns out tat it produces a bound on V j (a) on average in j see Lemma 2.4 below. Te key statement is te following teorem, wic we will try to prove later in te course (see 3.3): ( ) Teorem 6 (Local Weyl Law). Assume tat χ C0 (0, ) and a S 0 (T M). Ten as, ( λj χ j ) ( ) ( n V j (a) = χ ( ) ξ g a (x, 2π T M ξ ) ) dxdξ + O( 1 ). ξ g Taking a = 1 in Teorem 6, we in particular get ( λj ) ( ) n ( χ = χ ( ) ) ξ g dxdξ + O( 1 ). 2π j T M Approximating χ = 1l [0,1] by functions in C 0 ( (0, ) ), tis gives Teorem 7 (Weyl Law). We ave as, #{j λ j } = ω n (2π) Vol g(m) n + o( n ) n were ω n > 0 is te volume of te unit ball in n. Note also tat te integral on te rigt-and side in Teorem 6 is zero if a vanises on S M; tis is in line wit Lemma 2.3. For te proof of quantum ergodicity, we use te following corollary of Teorem 6: Lemma 2.4 (Variance bound). We ave for eac a S 0 (T M), as n V j (a) 2 C a 2 dµ L + O( 1 ) λ j [,2] S M were te constant C depends on M, but not on a or.

11 LECTUE NOTES ON QUANTUM CHAOS 11 ( ) Proof. Take nonnegative χ C0 (0, ) wit χ = 1 on [1, 2], ten it is enoug to estimate ( n λj ) χ Op j (a)u j 2 L = ( λj ) 2 n χ Op j (a) Op j (a)u j, u j L 2 j j and te rigt-and side is bounded by Teorem 6 using tat by (2.2) and (2.3) Op j (a) Op j (a) = Op j ( a 2 ) + O( j ) = Op j ( a 2 ) + O( 1 ) Step 4: integrated quantum ergodicity. We can now prove te following integrated (or, strictly speaking, summed) form of Teorem 4: Teorem 8 (Integrated quantum ergodicity). Assume tat a S 0 (T M) and L a = a dµ L. (2.11) Ten as, n λ j [,2] S M V j (a) L a 2 0. Proof. By subtracting L a from a and using tat Op (1) is te identity operator, we reduce to te case L a = 0: a dµ L = 0. S M Moreover, by (2.10) we may assume tat a C 0 (T M). Take some T > 0. By Lemma 2.2 and ten Lemma 2.4, we ave ( ) n V j (a) 2 n Vj a T 2 + OT ( 1 ) λ j [,2] λ j [,2] C a T 2 L 2 (S M,µ L ) + O T ( 1 ) were te constant C is independent of T and. Taking te limit as, we ave lim sup n V j (a) 2 C a T 2 L 2 (S M,µ L ). λ j [,2] Te left-and side does not depend on T, and te rigt-and side converges to 0 by Teorem Step 5: end of te proof. It remains to derive Teorem 4 from Teorem 8, tat is to extract a density 1 sequence of eigenfunctions wic equidistributes in pase space. For tat we use Cebysev inequality and a diagonal argument on dyadic pieces of te spectrum. More precisely, for r N let N r := #{j λ j [2 r, 2 r+1 )},

12 12 SEMYON DYATLOV ten N r 2 nr as r by te Weyl law (Teorem 7). Take a sequence a s C 0 (T M), s = 1, 2,... wic is dense in C0 (T M) wit respect to te uniform norm. Put L s = a S M s dµ L and ( 1 ε l,r := max V j (a s ) L s ). 2 s l N r λ j [2 r,2 r+1 ) Ten ε l,r 0 as r for eac l by Teorem 8. We pick r(l) suc tat r(l+1) > r(l) and ε l,r < 100 l for r r(l). Define te disjoint collection of sets J l N as follows: j J l λ j [2 r(l), 2 r(l+1) ) and max s l V j(a s ) L s < 2 l. By Cebysev inequality, for r(l) r < r(l + 1), terefore # ( {j λ j [2 r, 2 r+1 )} \ J l ) lε l,r 2 2l N r 2 l N r, 1 #(J l ) # ( {j λ j [2 r(l), 2 r(l+1) )} 2 l. It follows from ere and te Weyl law tat te sequence j k, {j k } = l J l is a density one subsequence in N. On te oter and, we ave for eac s, V jk (a s ) a s dµ L as k. S M Using te bound (2.9) and te fact tat {a s } is dense in C0 we see tat V jk (a) a dµ L as k S M wit te uniform norm, for all a C 0 (T M). By (2.10) same is true for all a S 0 (T M), finising te proof. 3. Overview of semiclassical quantization We now briefly discuss ow to define te quantization procedure Op, sending te reader to [Zw] for details.

13 LECTUE NOTES ON QUANTUM CHAOS Quantization on n. We consider te following symbol classes on T n = 2n, S m (T n ) C ( n ), m, defined as follows: a(x, ξ; ) S m (T n ) if for eac multiiindices α, β tere exists a constant C αβ suc tat for all x, ξ and small, α x β ξ a(x, ξ; ) C αβ(1 + ξ ) m β. Note tat for m N 0 tis class includes polynomials of order m in ξ wit coefficients bounded wit all derivatives in x. For a S m(t n ), we define te operator Op (a) on functions on n as follows: Op (a)f(x) = (2π) n e i x y,ξ a(x, ξ)f(y) dydξ. (3.1) 2n Te integral (3.1) does not always converge in te usual sense, so some explanations are in order. Assume first a is smoot and compactly supported, or more generally a lies in te Scwartz class S (T n ). If f S ( n ), ten integral in (3.1) converges absolutely and gives a Scwartz function. Wen a S m (T n ), we see using te semiclassical Fourier transform F f(ξ) = (2π) n/2 y,ξ f(y) dy tat e i n Op (a)f(x) = (2π) n/2 e i x,ξ a(x, ξ)f f(ξ) dξ (3.2) n and since F f(ξ) is Scwartz, te integral still converges; integrating by parts in ξ, we see tat it still gives a Scwartz function. In fact, for any a S m (T n ), one can define Op (a)f for f S ( n ), were S ( n ), te dual to S ( n ), is te space of tempered distributions. Tis can be seen eiter by duality or by treating (3.1) as an oscillatory integral, or by first considering te case of a S (T n ) and extending to general a by density. In eiter case, we obtain te quantization procedure on n, a S m (T n ) Op (a) : S ( n ) S ( n ), S ( n ) S ( n ). Moreover, rapidly decaying symbols produce smooting operators: a S (T n ) = Op (a) : S ( n ) S ( n ). Note tat te mapping properties above are for any fixed ; we make no statement about te uniformity of norms as 0 at tis point. Exercise 3.1. Using (3.2), sow tat wen a S m (T n ) is polynomial in ξ, te operator Op (a) is te differential operator defined in (2.1).

14 14 SEMYON DYATLOV In wat follows, we will often ignore wat appens as x, ξ, so our proofs would immediately work for Scwartz symbols a S (T n ) and wit more work can be extended to general symbols Basic properties of quantization and stationary pase. We now want to establis some properties of te quantization procedure Op. We start wit te product formula (2.3). Assume tat a, b S (T n ) uniformly in. We would like to write Op (a) Op (b) = Op (c), c(x, ξ; ) S (T n ), (3.3) and understand te asymptotics of c as 0. We first find a formula for c using te following statement, known as oscillatory testing; see [Zw, Teorem 4.19]: Lemma 3.2. Assume tat a S (T n ). Ten for eac fixed > 0, 1. We can recover te symbol a from te operator A = Op (a) as follows: a(x, ξ) = e i x,ξ A(e i,ξ ). (3.4) 2. If A : S ( n ) S ( n ) and te function a S (T n ) satisfies (3.4), ten A = Op (a). We now write out te symbol c from (3.3) as follows: c(x, ξ) = e i x,ξ Op (a) Op (b)(e i,ξ ) = e i x,ξ Op (a)(b(, ξ; )e i,ξ ) = (2π) n e i x y,η ξ a(x, η; )b(y, ξ; ) dydη. n (3.5) To understand te beavior of c as 0, we use te following (see [Zw, Teorem 3.16]) Teorem 9 (Metod of stationary pase). Assume U n is an open set and ϕ C (U; ) as only one critical point x 0 U, tat is ϕ 0 on U \ {x 0 }. Assume also tat x 0 is a nondegenerate critical point, tat is te Hessian 2 ϕ(x 0 ) gives a nondegenerate quadratic form. Denote by sgn( 2 ϕ(x 0 )) te signature of tis form (te number of positive eigenvalues minus te number of negative eigenvalues). Ten for eac a C0 (U; C), we ave as 0 e iϕ(x) a(x) dx (2π) n/2 e iϕ(x 0 ) j L j (a) x=x0 (3.6) U were eac L j is a ϕ-dependent linear differential operator of order 2j. In particular j=0 L 0 (a) x=x0 = e iπ 4 sgn 2 ϕ(x 0 ) det 2 ϕ(x 0 ) 1/2 a(x 0 ).

15 LECTUE NOTES ON QUANTUM CHAOS 15 Proof. We only sketc a proof in a special case known as quadratic stationary pase: n = 1, ϕ(x) = x2 2. By Fubini s Teorem and a linear cange of variables, one can pass from ere to te case wen ϕ is a nondegenerate quadratic form in iger dimensions. Te general case can ten be andled by te Morse Lemma, wic gives a cange of variables conjugating a general pase ϕ locally to a quadratic form. We compute in terms of te standard (nonsemiclassical) Fourier transform â(ξ), e ix2 iπ 2 a(x) dx = e 4 e iξ2 2 â(ξ) dξ. (3.7) 2π Tis follows from te more general statement true for any z C, e z 0, z 0: e zx2 1 2 a(x) dx = e ξ2 2z â(ξ) dξ. (3.8) 2πz Te statement (3.8) follows for z > 0 by direct calculation using te Fourier transform of te Gaussian and for all z by analytic continuation. Now, taking te Taylor expansion of e iξ2 /2 as 0 and using tat â S, we get e ix2 iπ 1 ( ) jâ(ξ) 2 a(x) dx e 4 iξ2 dξ 2π j! 2 finising te proof. j=0 e iπ 1 i ) j 4 2π 2j x a(0) j!( 2 In te case (3.5) we integrate over y, η, tus te dimension is 2n. Te pase is given by j=0 (y, η) x y, η ξ, and te only critical point is y = x, η = ξ. Te value of te pase at te critical point is equal to 0. Te expansion (3.6) can be computed explicitly from quadratic stationary pase and yields c(x, ξ) a(x, ξ)b(x, ξ) + i n ξj a(x, ξ) xj b(x, ξ) + O( 2 ), j=1 explaining (2.3), (2.4).

16 16 SEMYON DYATLOV 3.3. More on semiclassical quantization. On a manifold M, we define te quantization Op (a) by covering M wit a locally finite system of coordinate carts, splitting a into pieces using a partition of unity, quantizing it separately on eac cart using (3.1), and adding te pieces back togeter. However, if we take different carts or te partition of unity, te resulting operator will cange by an operator wit symbol in S m 1 (T M). Terefore, it is more convenient to consider te class of semiclassical pseudodifferential operators Ψ m (M) = {Op (a) a S m (T M)} wic is independent of te coice of quantization, and te principal symbol map σ : Ψ m (M) S m (T M)/S m 1 (T M), σ (Op (a)) = a + S m 1 (T M) wic is also independent of te quantization. We ave te sort exact sequence 0 Ψ m 1 (M) Ψ m (M) σ S m (T M)/S m 1 (T M) 0. Te symbolic calculus makes it possible to construct more pseudodifferential operators by calculating teir symbol term by term. For instance, we can find approximate inverses of operators wit nonvanising symbols: Proposition 3.3. Assume a S 0(T M), p S m(t M), and p 0 on supp a. Ten tere exists b S m (T M) suc tat Op (a) = Op (b) Op (p) + O( ). emark. Tis generalizes Lemma 2.3 in te following sense: if ( 2 g 1)u = 0, ten Op (a)u L 2 = O( ) u L 2 for all a S 0 (T M), supp a { ξ g = 1} =. Sketc of proof. We first take b 0 = a p S m (T M). Ten by (2.3) we ave for some r 1 S 1 (T M), Op (a) = Op (b 0 ) Op (p) + Op (r 1 ) + O( ). Moreover, one can arrange so tat supp r 1 {a 0}. Ten we repeat te procedure, putting b 1 = r 1 p S m 1 (T M). Arguing tis way we construct some symbols b j j S m j (T M) and it remains to take b suc tat b b j. j

17 LECTUE NOTES ON QUANTUM CHAOS 17 We can also take functions of pseudodifferential operators, wic we present in a special case. Namely we ave Teorem 10. Assume (M, g) is a compact iemannian manifold witout boundary, and put P = 2 g = Op (p) + O() Ψ 1 were p(x, ξ) = ξ 2 g. Ten for eac χ C0 () and all N, we ave χ(p ) Ψ N (M); σ (χ(p )) = χ(p). Sketc of proof. We write using te Fourier transform ˆχ, χ(p ) = 1 ˆχ(t)e itp dt. (3.9) 2π For bounded t, we ave as can be done solving te equation e itp = Op (p t ), p t = e itp + O(), (3.10) t Op (p t ) = ip Op (p t ) in symbolic calculus. Wen ˆχ is compactly supported, we get te desired formula. Oterwise we can write (3.10) up to t ε for some small ε, were te symbol p t will ave derivatives mildly growing in, and use te integral (3.9) wit te fact tat ˆχ is Scwartz. From Teorem 10 we can derive te following version of local Weyl law of Teorem 6 (te original version, wit depending on u j, can be proved using a rescaling and Lemma 2.3): Teorem 11. For χ C0 (), a S 0 (T M), and λ j, u j defined in (1.1), we ave as 0 χ( 2 λ j ) Op (a)u j, u j = (2π) n χ(p(x, ξ))a(x, ξ) dxdξ + O( 1 n ). j T M Proof. Putting P := 2 g, te left-and side is te trace tr ( χ(p ) Op (a) ). However, we know tat χ(p ) Op (a) Ψ N (M) for all N, so we write χ(p ) Op (a) = Op (b) + O( ) were b = χ(p)a + O() is rapidly decreasing in ξ. It remains to use te following trace formula for pseudodifferential operators: tr Op (b) = (2π) n b(x, ξ) dxdξ + O( 1 n ) T M wic reduces to te case of quantization on n and tere te trace can be computed by integrating te Scwartz kernel.

18 18 SEMYON DYATLOV 3.4. Anoter application of stationary pase: concentration of Lagrangian states. Assume tat U n is open and we are given a pase function ϕ C (U; ) and an amplitude b C 0 (U; C). We define te family of functions u C 0 (U; C), > 0, by u (x) = e iϕ(x)/ b(x). We would like to understand te limits as 0 of observables Op (a)u, u L 2, a C 0 (T n ), specifically to write for some measure µ, Op (a)u, u L 2 a dµ. T n (3.11) Tis can be done by applying te metod of stationary pase to Op (a)u (x) = (2π) n e i ( x y,ξ +ϕ(y)) a(x, ξ)b(y) dydξ. 2n Te pase function is te stationary point is given by (y, ξ) x y, ξ + ϕ(y), x = y, ξ = ϕ(x), and te value of te pase at te stationary point is equal to ϕ(x). Applying (3.6), we obtain Op (a)u (x) = e iϕ(x)/ a(x, ϕ(x))b(x) + O(). Terefore we ave te limit (3.11) wit µ given by a dµ = a(x, ϕ(x)) b(x) 2 dx. In particular, µ lives on T M U Λ ϕ = {(x, ϕ(x)) x U} wic is a Lagrangian submanifold of T n. Exercise 3.4. Find te semiclassical limits (in te sense of Definition 2.1) of te functions u 1j and u jj from Exercise 1.3. (Ignore te boundary issues by testing tese functions against operators supported strictly inside te square.)

19 LECTUE NOTES ON QUANTUM CHAOS 19 eferences [CdV] Yves Colin de Verdière, Ergodicité et fonctions propres du Laplacien, Comm. Mat. Pys. 102(1985), [S] Alexander Snirelman, Ergodic properties of eigenfunctions, Usp. Mat. Nauk. 29(1974), [Ze] Steve Zelditc, Uniform distribution of eigenfunctions on compact yperbolic surfaces, Duke Mat. J. 55(1987), [ZeZw] Steve Zelditc and Maciej Zworski, Ergodicity of eigenfunctions for ergodic billiards, Comm. Mat. Pys. 175(1996), [Zw] Maciej Zworski, Semiclassical analysis, Graduate Studies in Matematics 138, AMS, 2012.

Variations on Quantum Ergodic Theorems. Michael Taylor

Variations on Quantum Ergodic Theorems. Michael Taylor Notes available on my website, under Downloadable Lecture Notes 8. Seminar talks and AMS talks See also 4. Spectral theory 7. Quantum mechanics connections Basic quantization: a function on phase space

More information

MA455 Manifolds Solutions 1 May 2008

MA455 Manifolds Solutions 1 May 2008 MA455 Manifolds Solutions 1 May 2008 1. (i) Given real numbers a < b, find a diffeomorpism (a, b) R. Solution: For example first map (a, b) to (0, π/2) and ten map (0, π/2) diffeomorpically to R using

More information

Section 2.7 Derivatives and Rates of Change Part II Section 2.8 The Derivative as a Function. at the point a, to be. = at time t = a is

Section 2.7 Derivatives and Rates of Change Part II Section 2.8 The Derivative as a Function. at the point a, to be. = at time t = a is Mat 180 www.timetodare.com Section.7 Derivatives and Rates of Cange Part II Section.8 Te Derivative as a Function Derivatives ( ) In te previous section we defined te slope of te tangent to a curve wit

More information

Poisson Equation in Sobolev Spaces

Poisson Equation in Sobolev Spaces Poisson Equation in Sobolev Spaces OcMountain Dayligt Time. 6, 011 Today we discuss te Poisson equation in Sobolev spaces. It s existence, uniqueness, and regularity. Weak Solution. u = f in, u = g on

More information

3.4 Worksheet: Proof of the Chain Rule NAME

3.4 Worksheet: Proof of the Chain Rule NAME Mat 1170 3.4 Workseet: Proof of te Cain Rule NAME Te Cain Rule So far we are able to differentiate all types of functions. For example: polynomials, rational, root, and trigonometric functions. We are

More information

Differentiation in higher dimensions

Differentiation in higher dimensions Capter 2 Differentiation in iger dimensions 2.1 Te Total Derivative Recall tat if f : R R is a 1-variable function, and a R, we say tat f is differentiable at x = a if and only if te ratio f(a+) f(a) tends

More information

Precalculus Test 2 Practice Questions Page 1. Note: You can expect other types of questions on the test than the ones presented here!

Precalculus Test 2 Practice Questions Page 1. Note: You can expect other types of questions on the test than the ones presented here! Precalculus Test 2 Practice Questions Page Note: You can expect oter types of questions on te test tan te ones presented ere! Questions Example. Find te vertex of te quadratic f(x) = 4x 2 x. Example 2.

More information

Consider a function f we ll specify which assumptions we need to make about it in a minute. Let us reformulate the integral. 1 f(x) dx.

Consider a function f we ll specify which assumptions we need to make about it in a minute. Let us reformulate the integral. 1 f(x) dx. Capter 2 Integrals as sums and derivatives as differences We now switc to te simplest metods for integrating or differentiating a function from its function samples. A careful study of Taylor expansions

More information

4. The slope of the line 2x 7y = 8 is (a) 2/7 (b) 7/2 (c) 2 (d) 2/7 (e) None of these.

4. The slope of the line 2x 7y = 8 is (a) 2/7 (b) 7/2 (c) 2 (d) 2/7 (e) None of these. Mat 11. Test Form N Fall 016 Name. Instructions. Te first eleven problems are wort points eac. Te last six problems are wort 5 points eac. For te last six problems, you must use relevant metods of algebra

More information

A SHORT INTRODUCTION TO BANACH LATTICES AND

A SHORT INTRODUCTION TO BANACH LATTICES AND CHAPTER A SHORT INTRODUCTION TO BANACH LATTICES AND POSITIVE OPERATORS In tis capter we give a brief introduction to Banac lattices and positive operators. Most results of tis capter can be found, e.g.,

More information

Solutions to the Multivariable Calculus and Linear Algebra problems on the Comprehensive Examination of January 31, 2014

Solutions to the Multivariable Calculus and Linear Algebra problems on the Comprehensive Examination of January 31, 2014 Solutions to te Multivariable Calculus and Linear Algebra problems on te Compreensive Examination of January 3, 24 Tere are 9 problems ( points eac, totaling 9 points) on tis portion of te examination.

More information

SEMICLASSICAL LAGRANGIAN DISTRIBUTIONS

SEMICLASSICAL LAGRANGIAN DISTRIBUTIONS SEMICLASSICAL LAGRANGIAN DISTRIBUTIONS SEMYON DYATLOV Abstract. These are (rather sloppy) notes for the talk given in UC Berkeley in March 2012, attempting to explain the global theory of semiclassical

More information

MAT244 - Ordinary Di erential Equations - Summer 2016 Assignment 2 Due: July 20, 2016

MAT244 - Ordinary Di erential Equations - Summer 2016 Assignment 2 Due: July 20, 2016 MAT244 - Ordinary Di erential Equations - Summer 206 Assignment 2 Due: July 20, 206 Full Name: Student #: Last First Indicate wic Tutorial Section you attend by filling in te appropriate circle: Tut 0

More information

Test 2 Review. 1. Find the determinant of the matrix below using (a) cofactor expansion and (b) row reduction. A = 3 2 =

Test 2 Review. 1. Find the determinant of the matrix below using (a) cofactor expansion and (b) row reduction. A = 3 2 = Test Review Find te determinant of te matrix below using (a cofactor expansion and (b row reduction Answer: (a det + = (b Observe R R R R R R R R R Ten det B = (((det Hence det Use Cramer s rule to solve:

More information

Eigenvalues and eigenfunctions of the Laplacian. Andrew Hassell

Eigenvalues and eigenfunctions of the Laplacian. Andrew Hassell Eigenvalues and eigenfunctions of the Laplacian Andrew Hassell 1 2 The setting In this talk I will consider the Laplace operator,, on various geometric spaces M. Here, M will be either a bounded Euclidean

More information

Numerical Differentiation

Numerical Differentiation Numerical Differentiation Finite Difference Formulas for te first derivative (Using Taylor Expansion tecnique) (section 8.3.) Suppose tat f() = g() is a function of te variable, and tat as 0 te function

More information

arxiv: v3 [math.ap] 17 Jul 2018

arxiv: v3 [math.ap] 17 Jul 2018 SEMICLASSICAL MEASURES ON HYPERBOLIC SURFACES HAVE FULL SUPPORT SEMYON DYATLOV AND LONG JIN arxiv:1705.05019v3 [mat.ap] 17 Jul 2018 Abstract. We sow tat eac limiting semiclassical measure obtained from

More information

Name: Answer Key No calculators. Show your work! 1. (21 points) All answers should either be,, a (finite) real number, or DNE ( does not exist ).

Name: Answer Key No calculators. Show your work! 1. (21 points) All answers should either be,, a (finite) real number, or DNE ( does not exist ). Mat - Final Exam August 3 rd, Name: Answer Key No calculators. Sow your work!. points) All answers sould eiter be,, a finite) real number, or DNE does not exist ). a) Use te grap of te function to evaluate

More information

Notes on wavefunctions II: momentum wavefunctions

Notes on wavefunctions II: momentum wavefunctions Notes on wavefunctions II: momentum wavefunctions and uncertainty Te state of a particle at any time is described by a wavefunction ψ(x). Tese wavefunction must cange wit time, since we know tat particles

More information

ERROR BOUNDS FOR THE METHODS OF GLIMM, GODUNOV AND LEVEQUE BRADLEY J. LUCIER*

ERROR BOUNDS FOR THE METHODS OF GLIMM, GODUNOV AND LEVEQUE BRADLEY J. LUCIER* EO BOUNDS FO THE METHODS OF GLIMM, GODUNOV AND LEVEQUE BADLEY J. LUCIE* Abstract. Te expected error in L ) attimet for Glimm s sceme wen applied to a scalar conservation law is bounded by + 2 ) ) /2 T

More information

The Laplace equation, cylindrically or spherically symmetric case

The Laplace equation, cylindrically or spherically symmetric case Numerisce Metoden II, 7 4, und Übungen, 7 5 Course Notes, Summer Term 7 Some material and exercises Te Laplace equation, cylindrically or sperically symmetric case Electric and gravitational potential,

More information

POINTWISE BOUNDS ON QUASIMODES OF SEMICLASSICAL SCHRÖDINGER OPERATORS IN DIMENSION TWO

POINTWISE BOUNDS ON QUASIMODES OF SEMICLASSICAL SCHRÖDINGER OPERATORS IN DIMENSION TWO POINTWISE BOUNDS ON QUASIMODES OF SEMICLASSICAL SCHRÖDINGER OPERATORS IN DIMENSION TWO HART F. SMITH AND MACIEJ ZWORSKI Abstract. We prove optimal pointwise bounds on quasimodes of semiclassical Schrödinger

More information

Math 161 (33) - Final exam

Math 161 (33) - Final exam Name: Id #: Mat 161 (33) - Final exam Fall Quarter 2015 Wednesday December 9, 2015-10:30am to 12:30am Instructions: Prob. Points Score possible 1 25 2 25 3 25 4 25 TOTAL 75 (BEST 3) Read eac problem carefully.

More information

INTRODUCTION AND MATHEMATICAL CONCEPTS

INTRODUCTION AND MATHEMATICAL CONCEPTS Capter 1 INTRODUCTION ND MTHEMTICL CONCEPTS PREVIEW Tis capter introduces you to te basic matematical tools for doing pysics. You will study units and converting between units, te trigonometric relationsips

More information

NUMERICAL DIFFERENTIATION. James T. Smith San Francisco State University. In calculus classes, you compute derivatives algebraically: for example,

NUMERICAL DIFFERENTIATION. James T. Smith San Francisco State University. In calculus classes, you compute derivatives algebraically: for example, NUMERICAL DIFFERENTIATION James T Smit San Francisco State University In calculus classes, you compute derivatives algebraically: for example, f( x) = x + x f ( x) = x x Tis tecnique requires your knowing

More information

A h u h = f h. 4.1 The CoarseGrid SystemandtheResidual Equation

A h u h = f h. 4.1 The CoarseGrid SystemandtheResidual Equation Capter Grid Transfer Remark. Contents of tis capter. Consider a grid wit grid size and te corresponding linear system of equations A u = f. Te summary given in Section 3. leads to te idea tat tere migt

More information

Exercises for numerical differentiation. Øyvind Ryan

Exercises for numerical differentiation. Øyvind Ryan Exercises for numerical differentiation Øyvind Ryan February 25, 2013 1. Mark eac of te following statements as true or false. a. Wen we use te approximation f (a) (f (a +) f (a))/ on a computer, we can

More information

Math 212-Lecture 9. For a single-variable function z = f(x), the derivative is f (x) = lim h 0

Math 212-Lecture 9. For a single-variable function z = f(x), the derivative is f (x) = lim h 0 3.4: Partial Derivatives Definition Mat 22-Lecture 9 For a single-variable function z = f(x), te derivative is f (x) = lim 0 f(x+) f(x). For a function z = f(x, y) of two variables, to define te derivatives,

More information

Math 102 TEST CHAPTERS 3 & 4 Solutions & Comments Fall 2006

Math 102 TEST CHAPTERS 3 & 4 Solutions & Comments Fall 2006 Mat 102 TEST CHAPTERS 3 & 4 Solutions & Comments Fall 2006 f(x+) f(x) 10 1. For f(x) = x 2 + 2x 5, find ))))))))) and simplify completely. NOTE: **f(x+) is NOT f(x)+! f(x+) f(x) (x+) 2 + 2(x+) 5 ( x 2

More information

2.8 The Derivative as a Function

2.8 The Derivative as a Function .8 Te Derivative as a Function Typically, we can find te derivative of a function f at many points of its domain: Definition. Suppose tat f is a function wic is differentiable at every point of an open

More information

Microlocal limits of plane waves

Microlocal limits of plane waves Microlocal limits of plane waves Semyon Dyatlov University of California, Berkeley July 4, 2012 joint work with Colin Guillarmou (ENS) Semyon Dyatlov (UC Berkeley) Microlocal limits of plane waves July

More information

Recent Progress in the Integration of Poisson Systems via the Mid Point Rule and Runge Kutta Algorithm

Recent Progress in the Integration of Poisson Systems via the Mid Point Rule and Runge Kutta Algorithm Recent Progress in te Integration of Poisson Systems via te Mid Point Rule and Runge Kutta Algoritm Klaus Bucner, Mircea Craioveanu and Mircea Puta Abstract Some recent progress in te integration of Poisson

More information

Solution. Solution. f (x) = (cos x)2 cos(2x) 2 sin(2x) 2 cos x ( sin x) (cos x) 4. f (π/4) = ( 2/2) ( 2/2) ( 2/2) ( 2/2) 4.

Solution. Solution. f (x) = (cos x)2 cos(2x) 2 sin(2x) 2 cos x ( sin x) (cos x) 4. f (π/4) = ( 2/2) ( 2/2) ( 2/2) ( 2/2) 4. December 09, 20 Calculus PracticeTest s Name: (4 points) Find te absolute extrema of f(x) = x 3 0 on te interval [0, 4] Te derivative of f(x) is f (x) = 3x 2, wic is zero only at x = 0 Tus we only need

More information

arxiv: v2 [math.ap] 4 Jun 2013

arxiv: v2 [math.ap] 4 Jun 2013 QUANTUM ERGODIC RESTRICTION FOR CAUCHY DATA: INTERIOR QUE AND RESTRICTED QUE arxiv:05.086v [mat.ap] Jun 03 HANS CHRISTIANSON, JOHN A. TOTH, AND STEVE ZELDITCH Abstract. We prove a quantum ergodic restriction

More information

Exam 1 Review Solutions

Exam 1 Review Solutions Exam Review Solutions Please also review te old quizzes, and be sure tat you understand te omework problems. General notes: () Always give an algebraic reason for your answer (graps are not sufficient),

More information

Click here to see an animation of the derivative

Click here to see an animation of the derivative Differentiation Massoud Malek Derivative Te concept of derivative is at te core of Calculus; It is a very powerful tool for understanding te beavior of matematical functions. It allows us to optimize functions,

More information

How to Find the Derivative of a Function: Calculus 1

How to Find the Derivative of a Function: Calculus 1 Introduction How to Find te Derivative of a Function: Calculus 1 Calculus is not an easy matematics course Te fact tat you ave enrolled in suc a difficult subject indicates tat you are interested in te

More information

Lecture 15. Interpolation II. 2 Piecewise polynomial interpolation Hermite splines

Lecture 15. Interpolation II. 2 Piecewise polynomial interpolation Hermite splines Lecture 5 Interpolation II Introduction In te previous lecture we focused primarily on polynomial interpolation of a set of n points. A difficulty we observed is tat wen n is large, our polynomial as to

More information

MAT 145. Type of Calculator Used TI-89 Titanium 100 points Score 100 possible points

MAT 145. Type of Calculator Used TI-89 Titanium 100 points Score 100 possible points MAT 15 Test #2 Name Solution Guide Type of Calculator Used TI-89 Titanium 100 points Score 100 possible points Use te grap of a function sown ere as you respond to questions 1 to 8. 1. lim f (x) 0 2. lim

More information

Continuity and Differentiability Worksheet

Continuity and Differentiability Worksheet Continuity and Differentiability Workseet (Be sure tat you can also do te grapical eercises from te tet- Tese were not included below! Typical problems are like problems -3, p. 6; -3, p. 7; 33-34, p. 7;

More information

Chapter 1D - Rational Expressions

Chapter 1D - Rational Expressions - Capter 1D Capter 1D - Rational Expressions Definition of a Rational Expression A rational expression is te quotient of two polynomials. (Recall: A function px is a polynomial in x of degree n, if tere

More information

Chapter Seven The Quantum Mechanical Simple Harmonic Oscillator

Chapter Seven The Quantum Mechanical Simple Harmonic Oscillator Capter Seven Te Quantum Mecanical Simple Harmonic Oscillator Introduction Te potential energy function for a classical, simple armonic oscillator is given by ZÐBÑ œ 5B were 5 is te spring constant. Suc

More information

Exercise 19 - OLD EXAM, FDTD

Exercise 19 - OLD EXAM, FDTD Exercise 19 - OLD EXAM, FDTD A 1D wave propagation may be considered by te coupled differential equations u x + a v t v x + b u t a) 2 points: Derive te decoupled differential equation and give c in terms

More information

SFU UBC UNBC Uvic Calculus Challenge Examination June 5, 2008, 12:00 15:00

SFU UBC UNBC Uvic Calculus Challenge Examination June 5, 2008, 12:00 15:00 SFU UBC UNBC Uvic Calculus Callenge Eamination June 5, 008, :00 5:00 Host: SIMON FRASER UNIVERSITY First Name: Last Name: Scool: Student signature INSTRUCTIONS Sow all your work Full marks are given only

More information

The derivative function

The derivative function Roberto s Notes on Differential Calculus Capter : Definition of derivative Section Te derivative function Wat you need to know already: f is at a point on its grap and ow to compute it. Wat te derivative

More information

Continuity. Example 1

Continuity. Example 1 Continuity MATH 1003 Calculus and Linear Algebra (Lecture 13.5) Maoseng Xiong Department of Matematics, HKUST A function f : (a, b) R is continuous at a point c (a, b) if 1. x c f (x) exists, 2. f (c)

More information

Finite Difference Methods Assignments

Finite Difference Methods Assignments Finite Difference Metods Assignments Anders Söberg and Aay Saxena, Micael Tuné, and Maria Westermarck Revised: Jarmo Rantakokko June 6, 1999 Teknisk databeandling Assignment 1: A one-dimensional eat equation

More information

Function Composition and Chain Rules

Function Composition and Chain Rules Function Composition and s James K. Peterson Department of Biological Sciences and Department of Matematical Sciences Clemson University Marc 8, 2017 Outline 1 Function Composition and Continuity 2 Function

More information

Semigroups of Operators

Semigroups of Operators Lecture 11 Semigroups of Operators In tis Lecture we gater a few notions on one-parameter semigroups of linear operators, confining to te essential tools tat are needed in te sequel. As usual, X is a real

More information

HOMEWORK HELP 2 FOR MATH 151

HOMEWORK HELP 2 FOR MATH 151 HOMEWORK HELP 2 FOR MATH 151 Here we go; te second round of omework elp. If tere are oters you would like to see, let me know! 2.4, 43 and 44 At wat points are te functions f(x) and g(x) = xf(x)continuous,

More information

MATH745 Fall MATH745 Fall

MATH745 Fall MATH745 Fall MATH745 Fall 5 MATH745 Fall 5 INTRODUCTION WELCOME TO MATH 745 TOPICS IN NUMERICAL ANALYSIS Instructor: Dr Bartosz Protas Department of Matematics & Statistics Email: bprotas@mcmasterca Office HH 36, Ext

More information

1 Calculus. 1.1 Gradients and the Derivative. Q f(x+h) f(x)

1 Calculus. 1.1 Gradients and the Derivative. Q f(x+h) f(x) Calculus. Gradients and te Derivative Q f(x+) δy P T δx R f(x) 0 x x+ Let P (x, f(x)) and Q(x+, f(x+)) denote two points on te curve of te function y = f(x) and let R denote te point of intersection of

More information

Section 2.1 The Definition of the Derivative. We are interested in finding the slope of the tangent line at a specific point.

Section 2.1 The Definition of the Derivative. We are interested in finding the slope of the tangent line at a specific point. Popper 6: Review of skills: Find tis difference quotient. f ( x ) f ( x) if f ( x) x Answer coices given in audio on te video. Section.1 Te Definition of te Derivative We are interested in finding te slope

More information

SECTION 3.2: DERIVATIVE FUNCTIONS and DIFFERENTIABILITY

SECTION 3.2: DERIVATIVE FUNCTIONS and DIFFERENTIABILITY (Section 3.2: Derivative Functions and Differentiability) 3.2.1 SECTION 3.2: DERIVATIVE FUNCTIONS and DIFFERENTIABILITY LEARNING OBJECTIVES Know, understand, and apply te Limit Definition of te Derivative

More information

Polynomial Interpolation

Polynomial Interpolation Capter 4 Polynomial Interpolation In tis capter, we consider te important problem of approximatinga function fx, wose values at a set of distinct points x, x, x,, x n are known, by a polynomial P x suc

More information

NUMERICAL DIFFERENTIATION

NUMERICAL DIFFERENTIATION NUMERICAL IFFERENTIATION FIRST ERIVATIVES Te simplest difference formulas are based on using a straigt line to interpolate te given data; tey use two data pints to estimate te derivative. We assume tat

More information

Implicit-explicit variational integration of highly oscillatory problems

Implicit-explicit variational integration of highly oscillatory problems Implicit-explicit variational integration of igly oscillatory problems Ari Stern Structured Integrators Worksop April 9, 9 Stern, A., and E. Grinspun. Multiscale Model. Simul., to appear. arxiv:88.39 [mat.na].

More information

Lecture XVII. Abstract We introduce the concept of directional derivative of a scalar function and discuss its relation with the gradient operator.

Lecture XVII. Abstract We introduce the concept of directional derivative of a scalar function and discuss its relation with the gradient operator. Lecture XVII Abstract We introduce te concept of directional derivative of a scalar function and discuss its relation wit te gradient operator. Directional derivative and gradient Te directional derivative

More information

1. Consider the trigonometric function f(t) whose graph is shown below. Write down a possible formula for f(t).

1. Consider the trigonometric function f(t) whose graph is shown below. Write down a possible formula for f(t). . Consider te trigonometric function f(t) wose grap is sown below. Write down a possible formula for f(t). Tis function appears to be an odd, periodic function tat as been sifted upwards, so we will use

More information

Reflection Symmetries of q-bernoulli Polynomials

Reflection Symmetries of q-bernoulli Polynomials Journal of Nonlinear Matematical Pysics Volume 1, Supplement 1 005, 41 4 Birtday Issue Reflection Symmetries of q-bernoulli Polynomials Boris A KUPERSHMIDT Te University of Tennessee Space Institute Tullaoma,

More information

Analytic Functions. Differentiable Functions of a Complex Variable

Analytic Functions. Differentiable Functions of a Complex Variable Analytic Functions Differentiable Functions of a Complex Variable In tis capter, we sall generalize te ideas for polynomials power series of a complex variable we developed in te previous capter to general

More information

Quantum Numbers and Rules

Quantum Numbers and Rules OpenStax-CNX module: m42614 1 Quantum Numbers and Rules OpenStax College Tis work is produced by OpenStax-CNX and licensed under te Creative Commons Attribution License 3.0 Abstract Dene quantum number.

More information

LIMITATIONS OF EULER S METHOD FOR NUMERICAL INTEGRATION

LIMITATIONS OF EULER S METHOD FOR NUMERICAL INTEGRATION LIMITATIONS OF EULER S METHOD FOR NUMERICAL INTEGRATION LAURA EVANS.. Introduction Not all differential equations can be explicitly solved for y. Tis can be problematic if we need to know te value of y

More information

UNIVERSITY OF MANITOBA DEPARTMENT OF MATHEMATICS MATH 1510 Applied Calculus I FIRST TERM EXAMINATION - Version A October 12, :30 am

UNIVERSITY OF MANITOBA DEPARTMENT OF MATHEMATICS MATH 1510 Applied Calculus I FIRST TERM EXAMINATION - Version A October 12, :30 am DEPARTMENT OF MATHEMATICS MATH 1510 Applied Calculus I October 12, 2016 8:30 am LAST NAME: FIRST NAME: STUDENT NUMBER: SIGNATURE: (I understand tat ceating is a serious offense DO NOT WRITE IN THIS TABLE

More information

The Derivative as a Function

The Derivative as a Function Section 2.2 Te Derivative as a Function 200 Kiryl Tsiscanka Te Derivative as a Function DEFINITION: Te derivative of a function f at a number a, denoted by f (a), is if tis limit exists. f (a) f(a + )

More information

2.11 That s So Derivative

2.11 That s So Derivative 2.11 Tat s So Derivative Introduction to Differential Calculus Just as one defines instantaneous velocity in terms of average velocity, we now define te instantaneous rate of cange of a function at a point

More information

Time (hours) Morphine sulfate (mg)

Time (hours) Morphine sulfate (mg) Mat Xa Fall 2002 Review Notes Limits and Definition of Derivative Important Information: 1 According to te most recent information from te Registrar, te Xa final exam will be eld from 9:15 am to 12:15

More information

Combining functions: algebraic methods

Combining functions: algebraic methods Combining functions: algebraic metods Functions can be added, subtracted, multiplied, divided, and raised to a power, just like numbers or algebra expressions. If f(x) = x 2 and g(x) = x + 2, clearly f(x)

More information

Recent developments in mathematical Quantum Chaos, I

Recent developments in mathematical Quantum Chaos, I Recent developments in mathematical Quantum Chaos, I Steve Zelditch Johns Hopkins and Northwestern Harvard, November 21, 2009 Quantum chaos of eigenfunction Let {ϕ j } be an orthonormal basis of eigenfunctions

More information

MATH 173: Problem Set 5 Solutions

MATH 173: Problem Set 5 Solutions MATH 173: Problem Set 5 Solutions Problem 1. Let f L 1 and a. Te wole problem is a matter of cange of variables wit integrals. i Ff a ξ = e ix ξ f a xdx = e ix ξ fx adx = e ia+y ξ fydy = e ia ξ = e ia

More information

THE DISCRETE PLATEAU PROBLEM: CONVERGENCE RESULTS

THE DISCRETE PLATEAU PROBLEM: CONVERGENCE RESULTS MATHEMATICS OF COMPUTATION Volume 00, Number 0, Pages 000 000 S 0025-5718XX0000-0 THE DISCRETE PLATEAU PROBLEM: CONVERGENCE RESULTS GERHARD DZIUK AND JOHN E. HUTCHINSON Abstract. We solve te problem of

More information

158 Calculus and Structures

158 Calculus and Structures 58 Calculus and Structures CHAPTER PROPERTIES OF DERIVATIVES AND DIFFERENTIATION BY THE EASY WAY. Calculus and Structures 59 Copyrigt Capter PROPERTIES OF DERIVATIVES. INTRODUCTION In te last capter you

More information

232 Calculus and Structures

232 Calculus and Structures 3 Calculus and Structures CHAPTER 17 JUSTIFICATION OF THE AREA AND SLOPE METHODS FOR EVALUATING BEAMS Calculus and Structures 33 Copyrigt Capter 17 JUSTIFICATION OF THE AREA AND SLOPE METHODS 17.1 THE

More information

RIGIDITY OF TIME-FLAT SURFACES IN THE MINKOWSKI SPACETIME

RIGIDITY OF TIME-FLAT SURFACES IN THE MINKOWSKI SPACETIME RIGIDITY OF TIME-FLAT SURFACES IN THE MINKOWSKI SPACETIME PO-NING CHEN, MU-TAO WANG, AND YE-KAI WANG Abstract. A time-flat condition on spacelike 2-surfaces in spacetime is considered ere. Tis condition

More information

Math 31A Discussion Notes Week 4 October 20 and October 22, 2015

Math 31A Discussion Notes Week 4 October 20 and October 22, 2015 Mat 3A Discussion Notes Week 4 October 20 and October 22, 205 To prepare for te first midterm, we ll spend tis week working eamples resembling te various problems you ve seen so far tis term. In tese notes

More information

. If lim. x 2 x 1. f(x+h) f(x)

. If lim. x 2 x 1. f(x+h) f(x) Review of Differential Calculus Wen te value of one variable y is uniquely determined by te value of anoter variable x, ten te relationsip between x and y is described by a function f tat assigns a value

More information

Applications of the van Trees inequality to non-parametric estimation.

Applications of the van Trees inequality to non-parametric estimation. Brno-06, Lecture 2, 16.05.06 D/Stat/Brno-06/2.tex www.mast.queensu.ca/ blevit/ Applications of te van Trees inequality to non-parametric estimation. Regular non-parametric problems. As an example of suc

More information

POLYNOMIAL AND SPLINE ESTIMATORS OF THE DISTRIBUTION FUNCTION WITH PRESCRIBED ACCURACY

POLYNOMIAL AND SPLINE ESTIMATORS OF THE DISTRIBUTION FUNCTION WITH PRESCRIBED ACCURACY APPLICATIONES MATHEMATICAE 36, (29), pp. 2 Zbigniew Ciesielski (Sopot) Ryszard Zieliński (Warszawa) POLYNOMIAL AND SPLINE ESTIMATORS OF THE DISTRIBUTION FUNCTION WITH PRESCRIBED ACCURACY Abstract. Dvoretzky

More information

CHAPTER 4. Elliptic PDEs

CHAPTER 4. Elliptic PDEs CHAPTER 4 Elliptic PDEs One of te main advantages of extending te class of solutions of a PDE from classical solutions wit continuous derivatives to weak solutions wit weak derivatives is tat it is easier

More information

Symmetry Labeling of Molecular Energies

Symmetry Labeling of Molecular Energies Capter 7. Symmetry Labeling of Molecular Energies Notes: Most of te material presented in tis capter is taken from Bunker and Jensen 1998, Cap. 6, and Bunker and Jensen 2005, Cap. 7. 7.1 Hamiltonian Symmetry

More information

Polynomial Interpolation

Polynomial Interpolation Capter 4 Polynomial Interpolation In tis capter, we consider te important problem of approximating a function f(x, wose values at a set of distinct points x, x, x 2,,x n are known, by a polynomial P (x

More information

Differential Calculus (The basics) Prepared by Mr. C. Hull

Differential Calculus (The basics) Prepared by Mr. C. Hull Differential Calculus Te basics) A : Limits In tis work on limits, we will deal only wit functions i.e. tose relationsips in wic an input variable ) defines a unique output variable y). Wen we work wit

More information

1 The concept of limits (p.217 p.229, p.242 p.249, p.255 p.256) 1.1 Limits Consider the function determined by the formula 3. x since at this point

1 The concept of limits (p.217 p.229, p.242 p.249, p.255 p.256) 1.1 Limits Consider the function determined by the formula 3. x since at this point MA00 Capter 6 Calculus and Basic Linear Algebra I Limits, Continuity and Differentiability Te concept of its (p.7 p.9, p.4 p.49, p.55 p.56). Limits Consider te function determined by te formula f Note

More information

Dynamics and Relativity

Dynamics and Relativity Dynamics and Relativity Stepen Siklos Lent term 2011 Hand-outs and examples seets, wic I will give out in lectures, are available from my web site www.damtp.cam.ac.uk/user/stcs/dynamics.tml Lecture notes,

More information

1. Questions (a) through (e) refer to the graph of the function f given below. (A) 0 (B) 1 (C) 2 (D) 4 (E) does not exist

1. Questions (a) through (e) refer to the graph of the function f given below. (A) 0 (B) 1 (C) 2 (D) 4 (E) does not exist Mat 1120 Calculus Test 2. October 18, 2001 Your name Te multiple coice problems count 4 points eac. In te multiple coice section, circle te correct coice (or coices). You must sow your work on te oter

More information

Introduction In te long istory of completely integrable systems, an important object was discovered quite recently (Duistermaat [9]) : te monodromy of

Introduction In te long istory of completely integrable systems, an important object was discovered quite recently (Duistermaat [9]) : te monodromy of Bor-Sommerfeld conditions for Integrable Systems wit critical manifolds of focus-focus type V~u Ngọc San September 7, 998 Matematics Institute, Budapestlaan 6, University of Utrect, 358 TA Utrect, Te Neterlands.

More information

Calculus I - Spring 2014

Calculus I - Spring 2014 NAME: Calculus I - Spring 04 Midterm Exam I, Marc 5, 04 In all non-multiple coice problems you are required to sow all your work and provide te necessary explanations everywere to get full credit. In all

More information

Section 15.6 Directional Derivatives and the Gradient Vector

Section 15.6 Directional Derivatives and the Gradient Vector Section 15.6 Directional Derivatives and te Gradient Vector Finding rates of cange in different directions Recall tat wen we first started considering derivatives of functions of more tan one variable,

More information

THE IDEA OF DIFFERENTIABILITY FOR FUNCTIONS OF SEVERAL VARIABLES Math 225

THE IDEA OF DIFFERENTIABILITY FOR FUNCTIONS OF SEVERAL VARIABLES Math 225 THE IDEA OF DIFFERENTIABILITY FOR FUNCTIONS OF SEVERAL VARIABLES Mat 225 As we ave seen, te definition of derivative for a Mat 111 function g : R R and for acurveγ : R E n are te same, except for interpretation:

More information

Quantum Ergodicity for a Class of Mixed Systems

Quantum Ergodicity for a Class of Mixed Systems Quantum Ergodicity for a Class of Mixed Systems Jeffrey Galkowski University of California, Berkeley February 13, 2013 General Setup Let (M, g) be a smooth compact manifold with or without boundary. We

More information

7.1 Using Antiderivatives to find Area

7.1 Using Antiderivatives to find Area 7.1 Using Antiderivatives to find Area Introduction finding te area under te grap of a nonnegative, continuous function f In tis section a formula is obtained for finding te area of te region bounded between

More information

arxiv:submit/ [math.sp] 24 Apr 2015

arxiv:submit/ [math.sp] 24 Apr 2015 SPECTRAL GAPS, ADDITIVE ENERGY, AND A FRACTAL UNCERTAINTY PRINCIPLE SEMYON DYATLOV AND JOSHUA ZAHL arxiv:submit/139746 [mat.sp] 4 Apr 015 Abstract. We obtain an essential spectral gap for n-dimensional

More information

Quantum Mechanics Chapter 1.5: An illustration using measurements of particle spin.

Quantum Mechanics Chapter 1.5: An illustration using measurements of particle spin. I Introduction. Quantum Mecanics Capter.5: An illustration using measurements of particle spin. Quantum mecanics is a teory of pysics tat as been very successful in explaining and predicting many pysical

More information

5 Ordinary Differential Equations: Finite Difference Methods for Boundary Problems

5 Ordinary Differential Equations: Finite Difference Methods for Boundary Problems 5 Ordinary Differential Equations: Finite Difference Metods for Boundary Problems Read sections 10.1, 10.2, 10.4 Review questions 10.1 10.4, 10.8 10.9, 10.13 5.1 Introduction In te previous capters we

More information

Notes on Multigrid Methods

Notes on Multigrid Methods Notes on Multigrid Metods Qingai Zang April, 17 Motivation of multigrids. Te convergence rates of classical iterative metod depend on te grid spacing, or problem size. In contrast, convergence rates of

More information

BOUNDARY REGULARITY FOR SOLUTIONS TO THE LINEARIZED MONGE-AMPÈRE EQUATIONS

BOUNDARY REGULARITY FOR SOLUTIONS TO THE LINEARIZED MONGE-AMPÈRE EQUATIONS BOUNDARY REGULARITY FOR SOLUTIONS TO THE LINEARIZED MONGE-AMPÈRE EQUATIONS N. Q. LE AND O. SAVIN Abstract. We obtain boundary Hölder gradient estimates and regularity for solutions to te linearized Monge-Ampère

More information

Order of Accuracy. ũ h u Ch p, (1)

Order of Accuracy. ũ h u Ch p, (1) Order of Accuracy 1 Terminology We consider a numerical approximation of an exact value u. Te approximation depends on a small parameter, wic can be for instance te grid size or time step in a numerical

More information

, meant to remind us of the definition of f (x) as the limit of difference quotients: = lim

, meant to remind us of the definition of f (x) as the limit of difference quotients: = lim Mat 132 Differentiation Formulas Stewart 2.3 So far, we ave seen ow various real-world problems rate of cange and geometric problems tangent lines lead to derivatives. In tis section, we will see ow to

More information

2.1 THE DEFINITION OF DERIVATIVE

2.1 THE DEFINITION OF DERIVATIVE 2.1 Te Derivative Contemporary Calculus 2.1 THE DEFINITION OF DERIVATIVE 1 Te grapical idea of a slope of a tangent line is very useful, but for some uses we need a more algebraic definition of te derivative

More information

Phase space in classical physics

Phase space in classical physics Pase space in classical pysics Quantum mecanically, we can actually COU te number of microstates consistent wit a given macrostate, specified (for example) by te total energy. In general, eac microstate

More information