A general formula for the method of steepest descent using Perron s formula
|
|
- Meryl Walters
- 7 years ago
- Views:
Transcription
1 A general formula for the method of steepest descent using Perron s formula James Mathews December 6, 3 Introduction The method of steepest descent is one of the most used techniques in applied mathematics and asymptotics, but is often one of the hardest to apply. We want to find an approximation for the integral I(k) = g(z)e kf(z) dz as k C where f and g are (usually) analytic functions, k is (usually) a real parameter. The general idea is to use Cauchy s theorem which allows deformation of contours in the complex plane to simplify the problem. The idea was proposed by Debye, and the basic idea is to choose a new contour D such that D passes through one of more zeros of f (z) The imaginary part of f(z) is constant on D Since the imaginary part of the integral is constant, then if f(z) = g(z) + ih then we instead need to evaluate. I(k)e ikh g(z)e kg(z) dz as k, which can then be treated by Laplace s method.. Laplace s method Laplace s method says that to evaluate J(k) = C b a f(t)e kφ(t) dt as k, where φ(t) has a stationary point at an interior point c with non-zero second derivative (so f (t) = and f (t) ), then πf(c)e xφ(c) J(k). kφ (c)
2 If c is in fact an end point then we need to multiply this expression by a factor of /. If in fact we have no stationary point then we have or J(k) f(a)ekφ(a), kφ (a) J(k) f(b)ekφ(b), kφ (b) with the first case corresponding to the maximum of φ being at a, so φ (a) <, and the second corresponding to the maximum being at b, so φ (b) >. Finally, if c is a maximum with φ (c) = φ (c) =... φ p (c) = and φ p (c) < then we get J(k) Γ(/p)(p!)/p p[ kφ p (c)] /p f(c)exφ(c). See [Bender and Orszag, 978] for the proofs and examples of this method.. Difficulties with the method The main difficulty of the method, is of course, finding the new contour. We suppose the contour is not closed, and has end points a and b. Often, this contour consists of three parts; D which goes through a and has imaginary part I[f(a)] D which goes through b and has imaginary part I[f(b)] D 3 which joins the two contours at infinity It is hoped that the contribution of the integral from D 3 will become zero as D 3 becomes more infinite, whilst the other two contours are evaluated using Laplace s method. The examples in [Bender and Orszag, 978] show how to apply the procedure, with the latter examples showing just how complicated the contours become. Whilst we can always apply this method, it can be time consuming, intricate and often we would just like a general formula to be able to estimate, say the leading behaviour. Also, sometimes our function f will depend on another parameter, say λ and we want to know what the asymptotic approximation is as we vary over λ. Rather than having to find various curves of steepest descent, which will depend on λ and properties might change dramatically, if we have a general formula then it will be easier. Essentially, we want to use reasoning somewhat similar to Laplace s method; we find where the function R[f(z)] has a maximum (or maximum s) along a chosen contour, then since k is large the only contributions to the integral will come from these points..3 First (wrong) attempt at a general formula A naive way to estimate the leading order behaviour is given below. It is based upon the notes sad, which is one of the first results you come across on google for saddle point methods. The method suggests that if z is a (non-degenerate) saddle point, then
3 ( ) / [ ( π π I(k) = g(z)e kf(z) dz g(z C k f )e kf(z) exp i (z ) arg(f )] (z )). The reasoning given seems clear; you approximate f(z) by its Taylor expansion at z so f(z) f(z ) + f (z )(z z ) and g(z) by g(z ), then we just need to evaluate I(k) g(z )e kf(z ) exp k f (z )(z z ) dz. (.) We then switch to polar coordinates, extend the limits and perform the Gaussian integral to get the result. All this reasoning looks clear, until we examine it further and note two glaring problems: At no point did we use the fact that k is large, how can we construct an asymptotic approximation without using this property!? Just because f (z ) = does not mean that the real part of f(z) is maximised at z. These are in fact linked as explained above, we find where the real part of f(z) is maximised and then use the fact that k is large to show the only contribution to the integral is at this part. The argument above fails when we try and evaluate the integral in (.)..4 Example We end this section with a simple example showing why the general formula is useful, and why our first attempt is wrong. We consider the integral e k(z iz) dz. We can calculate that f(z) = z iz has a saddle point at z = i. The first attempt then suggest that the general behaviour can be calculated by only considering f(z) near the saddle point. But we can see that f( ) = + i, f(i) =, f() = i and at both end points and the saddle point R[f(z)] =, so we must consider the contribution from all the points, even though the endpoints are not saddle points. Using the formula we would say π e k(z iz) dz i k ek, which as we will see later, is plain wrong! If instead we try and solve the problem using the standard method of steepest descent, the contours we get are not so nice, but it will be possible to solve it. We can also see from Figure that there is going to very hard to pass through the saddle point using only curves of constant phase. In the Figure the curves of constant phase going through the points and are drawn in blue and green respectively. 3 Figure : Contours for method of steepest descent.
4 A general formula As our example clearly showed, the most important issue is showing that any point z we choose to approximate our integral at satisfies R[f(z) f(z )] < for all z on the contour we are integrating over, so we can in effect use Lapace s method. This critical condition, so often overlooked is the main point of this notes. If in fact R[f(z) f(z )] = for all z on the contour, then we can simply use the method of stationary phase, which is detailed in [Bender and Orszag, 978]. Our basic idea will be If R[f(z)] is maximised at one of the end points (or even both) then we can simply use Perron s Formula. If not, we find the saddle points of f, that is the points c i which satisfy f (c i ) =. We then deform the contour C to a new contour D which goes through the saddle points in the direction of steepest descent. We find the asymptotic points, that is the solutions to R[f (z i )] =, for z i which lie on the contour D, which has end points a and b. We also include a and b as asymptotic points. We order the z i into increasing order along the deformed contour (so if we start at a then as we go along the contour to b we will pass through the points in the order z, z,... z n ). We then split the deformed contour D in contours D [a,z ], D [z,z ],... C [zn,b], and if there is no z i then we simply take the deformed contour. Note that on each contour we have R[f(z) f(w)] < for all z on the contour D [zi,z i+ ], where w is either z i and z i+. We evaluate each of these integrals using Perron s formula. In the case where R[f(z)] is maximised at both end points a and b, we spilt the integral into two integrals by choosing an arbitary point c (a, b) and then applying Perron s formula to both. We assume that the R[f(z)] has only finitely many maxima and minima, and don t consider the case where we have infinitely many. We do this because it is unlikely the standard method of steepest descent would cope with this method. We also briefly recap how to find the direction of steepest descent at a saddle point c. If f is analytic at the critical point with f (c) = f (c) =... = f (N ) (c) = and f (N) = re iθ (with r > ) then the paths of steepest descent have direction (n + )π θ N for n =,,... N. In the case N = (so a non-degenerate saddle point) the paths of steepest descent have direction (π θ)/ and (3π θ)/.. Perron s Formula All that remains to be done is to evaluate integrals of the type I(k) = g(z)e kf(z) dz, (.) E 4
5 where E is a generic contour with end points a and b, and R[f(z) f(w)] < where w is either a or b. For simplicity we assume that it is at a for now. We now state Perron s formula (first described by Oscar Perron in 97) in the case that k is a real parameter, although with more assumptions we can state if for complex k. We also restrict to the case where f is analytic everywhere (or at least analytic at every z i ), which again simplifies the assumptions for the Theorem. The full theorem is stated in [Wong, ] and [Nemes, ], as well the proof, which we will not present here. Theorem. (Perron s Method) Assume that. D lies in the sector arg(f(a) f(z)) π/ δ for some fixed δ >. For all c a on D, there exists fixed ε(z) > such that f(z) f(a) ε(z) for all z on the contour between z = c and z = b. 3. In a neighbourhood of a, f(a) f(z) = a n (z a) n+α, where α N (since we have assumed f is analytic) and a. n= 4. In a neighbourhood of a, where β > and b. g(z) = b n (z a) n+β, n= Let 5. The integral (.) exists absolutely for each fixed k w = lim arg(z a), z D a then we require that for some fixed l we have the following inequality holds (to ensure the first condition holds) ( l ) ( π + δ arg a + αw l + ) π δ. Then where η s = I(k) e kf(a) αa (s+β)/α s! s m= s= b s m m! η s k (s+β)/α Γ ( s + β α ) e πil(s+β)/α, (.) [ d { ( a (z a) α ) }] (s+β)/α dz m f(a) f(z) The first two conditions are a slight strengthening of the condition R[f(z) f(a)] <, and can in most case be replaced by this condition. The third condition relates to the Taylor expansion of f at a, and we can see that if a is not a saddle point then α =, if a is a non-degenerate saddle point (f (a) = and f (a) ) then α =, and otherwise if f (a) = f (a) =... = f (n ) (a) = and f (n) (a) then α = n. Note also that a = f (α) (a)/α!, a = f (α+) (a)/(α + )! and a n = f (α+n) (a)/(α + n)!, 5 z=a (.3)
6 with the minus sign just comes since the left hand side is f(a) f(z) and not f(z) f(a). If g is analytic at a and g(a), then β = and b = g(a). Otherwise if g is analytic and g (a) = g (a) =... = g (m ) (a) = and g (m) (a) then we have β = m, and then b = g (β ) (a)/(β )!, b = g (β) (a)/β! and b n = g (β+n ) (a)/(β + n )!. The fifth condition ensures that the asymptotic approximation will make any sense!.. The first two coefficients Assuming that g is analytic, then we can calculate that the coefficient of leading order, η, will be given by η = b g (β ) (a)(α!) β/α = αa β/α α(β )![ f (α) (a)], β/α while the second coefficient is given by η = b αa (+β)/α ( + β)b a α a (+α+β)/α = g(β) (a)(α!) (+β)/α αβ![ f (α) (a)] (+β)/α + ( + β)g(β ) (a)f (α+) (a)(α!) (+β)/α (β )!α (α + )[ f (α) (a)] (+α+β)/α, with subsequent coefficients becoming more and more complicated. I think the third coefficient is given by [ ( + β)a η = b b b ( ) ( ( ) )] + β a + β αa α α + a αa (+β)/α.. Maximum at b If instead we have the maximum of the real part of f being at b rather than a, then the formula is modified slightly. We replace Conditions 3 and 4 in Theorem. by the conditions a a 3*. In a neighbourhood of b, where α N and c. 4*. In a neighbourhood of b, where β > and d. f(b) f(z) = c n (b z) n+α, n= g(z) = d n (b z) n+β, n= Then we have but we replace (.3) by η s = I(k) e kf(b) αc (s+β)/α s! s= η s k (s+β)/α Γ ( s + β α ) e πil(s+β)/α, (.4) [ { s ( ) }] d s m ( ) m d c (b z) α (s+β)/α m! dz m f(b) f(z) m= 6 z=b (.5)
7 We can caclulate that if g is analytic then we have c n = ( )α+n f (α+n) (b) (α + n)! and d n = ( )β+n g (β+n ) (b). (β + n )! In the case of the maximum being at the end point b, we can calculate that the coefficient of leading order, η, will be given by η = d αc β/α = ( )(β ) g (β ) (a)(α!) β/α α(β )![( ) (α ) f (α) (a)] β/α.. Special case: Contour is on the real line In the case where the contour is on the real line, w = thus we have l =. Furthermore, if we assume that g is analytic at a with g(a) then β = and to leading order we have kf(a) g(a)γ(/α)(α!)(/α) I(k) e α[ f (α) (a)] /α k /α g(a)γ(/α)(α!)(/α) + ekf(a) (.6) α[ f (α) (a)] /α k /α when the maximum is at the endpoint a. If instead g is analytic at b with g(b) then we have when the maximum is at the endpoint b. I(k) e kf(b) g(b)γ(/α)(α!) (/α), (.7) α[( ) (α ) f (α) (b)] /α k/α.3 Other attempts at a General Formula The same result is proved in [Ferreira et al., 7], under the same conditions, although they only calculate the order of the terms in the approximation, and not the constants (although the coefficients can be calculated using Laplace s method). The paper would also seem to suggest that the method is uniform in the sense that if f depends on a parameter that changes, then the approximation is uniformly valid. In Sections 3 and 4 of [Ferreira et al., 7], the authors suggest a different way to deal with multiple asymptotic points, rather than splitting the contour up. However, both methods still rely on calculating the asymptotic points, and in my opinion the method we have presented here is easier..4 Example (revisited) Returning to the example before, we can see that R[f(z)] = z has a maximum at both end points. We choose to split the integral at the point, but this is arbitary, and so we have I(k) = e k(z iz) dz = I (k) + I (k), where I (k) = e k(z iz) dz and I (k) = e k(z iz) dz. 7
8 Now I is of the form (.6) since the maximum occurs at the lower of the limits, and since f( ) = + i and f ( ) = i we have α = and hence I (k) ek(+i) ( + i)k. Similarly, I is of the form (.7) since the maximum occurs at the upper of the limits, and since f() = i and f () = i we have α = and hence Thus I(k) I (k) ek( i) ( i)k. ek(+i) ( + i)k + ek( i) ( i)k = ek [sin(k) + cos(k)]. k Below we plot I(k)/{ ek [sin(k) + cos(k)]}, and shows that the asymptotic approximation we k have chosen is indeed an asymptotic relation! k The spikes are caused by the fact that the zeros of I(k) and ek [sin(k) + cos(k)] don t quite line k up until k gets large, but either way we have shown our method is easy to use for this example, and significantly easier than the standard method of steepest descent..5 Example We now take the example I(k) = z i ek(iz z) dz, This has asymptotic points at, and since f (z) = i z. Suppose we just split the integral up up into two integrals, I between and and I between and. This is not what out method says, but we try it anyway. Using Perron s method we see that α = β = at the end point, and furthermore f() =, f () = i and g() = i/. Hence we have I (k) 4k and I (k) 4k, 8
9 and hence when we sum the two asymptotic approximations they cancel! Furthermore, if we take the next term in Perron s method (using f () = and g () = /4 we have that I (k) 4k + 3 6k and I (k) 4k 6k, and they still cancel! We can numerically calculate the integral when k = as I(k) 7.97i, which is what we don t see at all from using Perron s method. The trouble is that the imaginary behaviour of the integral is asymptotically small compared to the real part, so we don t see it! Thus, we should deform the contour to go through the critical point in the direction of steepest descent first before worrying about splitting the integrals up. The critical point lies at z = i, so we deform the contour to go throught this point. To keep things simple, we have the new contour going from to + i, from + i to + i and then from + i to. We can check that indeed we have gone through the critical point in the direction of steepest descent. Thus, we need to evaluate the integrals I (k) = +i i z i ek(iz z) dz, I (k) = +i z i ek(iz z) dz and +i I 3 (k) = i z i ek(iz z) dz, I 4 (k) = +i z i ek(iz z) dz. We can write the second and third integrals as I (k) = x i ek( x ) dx, and I 3 (k) = x i ek( x ) dx, and then using Perron s method (or even just Laplace s method) we can calculate I (k) i π k e k and I 3 (k) i π k e k. We can write the first and fourth integrals as I (k) = i ix i ek[x x 4+4i(x )] dx, and I 4 (k) = i ix + i ek[x x 4+4i( x)] dx, and then using Perron s method with α =, β = since they both have the real part of the exponentianted function having amaximum at the end point x = we have and hence I (k) e 4k 4ik i 4(i 3)k and I 4(k) e 4k+4ik i 4(3 + i)k I (k) + I 4 (k) e 4k i 4k [ ] e 4ik i 3 e4ik i + 3 = e 4k i (sin(4k) + 3 cos(3k)), k and hence is expoentially smaller than contributions from I and I 3. Hence, we get the correct asympotic behavour of I(k) i π k e k. 9
10 References Saddle point notes. Accessed: --3. Carl M Bender and Steven A Orszag. Advanced mathematical methods for scientists and engineers I: Asymptotic methods and perturbation theory, volume. Springer, 978. Chelo Ferreira, José L López, Pedro Pagola, and E Pérez Sinusía. The laplaces and steepest descents methods revisited. In Int. Math. Forum, volume, pages 97 34, 7. Gergo Nemes. Asymptotic expansions for integrals.. Roderick Wong. Asymptotic approximation of integrals, volume 34. SIAM,.
6. Define log(z) so that π < I log(z) π. Discuss the identities e log(z) = z and log(e w ) = w.
hapter omplex integration. omplex number quiz. Simplify 3+4i. 2. Simplify 3+4i. 3. Find the cube roots of. 4. Here are some identities for complex conjugate. Which ones need correction? z + w = z + w,
More information3 Contour integrals and Cauchy s Theorem
3 ontour integrals and auchy s Theorem 3. Line integrals of complex functions Our goal here will be to discuss integration of complex functions = u + iv, with particular regard to analytic functions. Of
More information5.3 Improper Integrals Involving Rational and Exponential Functions
Section 5.3 Improper Integrals Involving Rational and Exponential Functions 99.. 3. 4. dθ +a cos θ =, < a
More informationMATH 52: MATLAB HOMEWORK 2
MATH 52: MATLAB HOMEWORK 2. omplex Numbers The prevalence of the complex numbers throughout the scientific world today belies their long and rocky history. Much like the negative numbers, complex numbers
More informationDifferentiation and Integration
This material is a supplement to Appendix G of Stewart. You should read the appendix, except the last section on complex exponentials, before this material. Differentiation and Integration Suppose we have
More informationLecture 8: More Continuous Random Variables
Lecture 8: More Continuous Random Variables 26 September 2005 Last time: the eponential. Going from saying the density e λ, to f() λe λ, to the CDF F () e λ. Pictures of the pdf and CDF. Today: the Gaussian
More information1 The Brownian bridge construction
The Brownian bridge construction The Brownian bridge construction is a way to build a Brownian motion path by successively adding finer scale detail. This construction leads to a relatively easy proof
More informationx a x 2 (1 + x 2 ) n.
Limits and continuity Suppose that we have a function f : R R. Let a R. We say that f(x) tends to the limit l as x tends to a; lim f(x) = l ; x a if, given any real number ɛ > 0, there exists a real number
More informationMetric Spaces. Chapter 7. 7.1. Metrics
Chapter 7 Metric Spaces A metric space is a set X that has a notion of the distance d(x, y) between every pair of points x, y X. The purpose of this chapter is to introduce metric spaces and give some
More informationSOLUTIONS TO EXERCISES FOR. MATHEMATICS 205A Part 3. Spaces with special properties
SOLUTIONS TO EXERCISES FOR MATHEMATICS 205A Part 3 Fall 2008 III. Spaces with special properties III.1 : Compact spaces I Problems from Munkres, 26, pp. 170 172 3. Show that a finite union of compact subspaces
More information1 if 1 x 0 1 if 0 x 1
Chapter 3 Continuity In this chapter we begin by defining the fundamental notion of continuity for real valued functions of a single real variable. When trying to decide whether a given function is or
More informationNotes on metric spaces
Notes on metric spaces 1 Introduction The purpose of these notes is to quickly review some of the basic concepts from Real Analysis, Metric Spaces and some related results that will be used in this course.
More information2. Length and distance in hyperbolic geometry
2. Length and distance in hyperbolic geometry 2.1 The upper half-plane There are several different ways of constructing hyperbolic geometry. These different constructions are called models. In this lecture
More informationConstrained optimization.
ams/econ 11b supplementary notes ucsc Constrained optimization. c 2010, Yonatan Katznelson 1. Constraints In many of the optimization problems that arise in economics, there are restrictions on the values
More informationChapter 2. Complex Analysis. 2.1 Analytic functions. 2.1.1 The complex plane
Chapter 2 Complex Analysis In this part of the course we will study some basic complex analysis. This is an extremely useful and beautiful part of mathematics and forms the basis of many techniques employed
More informationVector and Matrix Norms
Chapter 1 Vector and Matrix Norms 11 Vector Spaces Let F be a field (such as the real numbers, R, or complex numbers, C) with elements called scalars A Vector Space, V, over the field F is a non-empty
More information1 Sufficient statistics
1 Sufficient statistics A statistic is a function T = rx 1, X 2,, X n of the random sample X 1, X 2,, X n. Examples are X n = 1 n s 2 = = X i, 1 n 1 the sample mean X i X n 2, the sample variance T 1 =
More informationNormal distribution. ) 2 /2σ. 2π σ
Normal distribution The normal distribution is the most widely known and used of all distributions. Because the normal distribution approximates many natural phenomena so well, it has developed into a
More informationMath 319 Problem Set #3 Solution 21 February 2002
Math 319 Problem Set #3 Solution 21 February 2002 1. ( 2.1, problem 15) Find integers a 1, a 2, a 3, a 4, a 5 such that every integer x satisfies at least one of the congruences x a 1 (mod 2), x a 2 (mod
More informationSolutions for Math 311 Assignment #1
Solutions for Math 311 Assignment #1 (1) Show that (a) Re(iz) Im(z); (b) Im(iz) Re(z). Proof. Let z x + yi with x Re(z) and y Im(z). Then Re(iz) Re( y + xi) y Im(z) and Im(iz) Im( y + xi) x Re(z). () Verify
More informationMATH 381 HOMEWORK 2 SOLUTIONS
MATH 38 HOMEWORK SOLUTIONS Question (p.86 #8). If g(x)[e y e y ] is harmonic, g() =,g () =, find g(x). Let f(x, y) = g(x)[e y e y ].Then Since f(x, y) is harmonic, f + f = and we require x y f x = g (x)[e
More informationUnified Lecture # 4 Vectors
Fall 2005 Unified Lecture # 4 Vectors These notes were written by J. Peraire as a review of vectors for Dynamics 16.07. They have been adapted for Unified Engineering by R. Radovitzky. References [1] Feynmann,
More informationPractice with Proofs
Practice with Proofs October 6, 2014 Recall the following Definition 0.1. A function f is increasing if for every x, y in the domain of f, x < y = f(x) < f(y) 1. Prove that h(x) = x 3 is increasing, using
More information4. Expanding dynamical systems
4.1. Metric definition. 4. Expanding dynamical systems Definition 4.1. Let X be a compact metric space. A map f : X X is said to be expanding if there exist ɛ > 0 and L > 1 such that d(f(x), f(y)) Ld(x,
More informationThe Math Circle, Spring 2004
The Math Circle, Spring 2004 (Talks by Gordon Ritter) What is Non-Euclidean Geometry? Most geometries on the plane R 2 are non-euclidean. Let s denote arc length. Then Euclidean geometry arises from the
More informationLecture 21 and 22: The Prime Number Theorem
Lecture and : The Prime Number Theorem (New lecture, not in Tet) The location of rime numbers is a central question in number theory. Around 88, Legendre offered eerimental evidence that the number π()
More informationRAJALAKSHMI ENGINEERING COLLEGE MA 2161 UNIT I - ORDINARY DIFFERENTIAL EQUATIONS PART A
RAJALAKSHMI ENGINEERING COLLEGE MA 26 UNIT I - ORDINARY DIFFERENTIAL EQUATIONS. Solve (D 2 + D 2)y = 0. 2. Solve (D 2 + 6D + 9)y = 0. PART A 3. Solve (D 4 + 4)x = 0 where D = d dt 4. Find Particular Integral:
More informationChapter 3 RANDOM VARIATE GENERATION
Chapter 3 RANDOM VARIATE GENERATION In order to do a Monte Carlo simulation either by hand or by computer, techniques must be developed for generating values of random variables having known distributions.
More informationProbability and Statistics Prof. Dr. Somesh Kumar Department of Mathematics Indian Institute of Technology, Kharagpur
Probability and Statistics Prof. Dr. Somesh Kumar Department of Mathematics Indian Institute of Technology, Kharagpur Module No. #01 Lecture No. #15 Special Distributions-VI Today, I am going to introduce
More information1.7 Graphs of Functions
64 Relations and Functions 1.7 Graphs of Functions In Section 1.4 we defined a function as a special type of relation; one in which each x-coordinate was matched with only one y-coordinate. We spent most
More informationα α λ α = = λ λ α ψ = = α α α λ λ ψ α = + β = > θ θ β > β β θ θ θ β θ β γ θ β = γ θ > β > γ θ β γ = θ β = θ β = θ β = β θ = β β θ = = = β β θ = + α α α α α = = λ λ λ λ λ λ λ = λ λ α α α α λ ψ + α =
More informationLinear Algebra Notes for Marsden and Tromba Vector Calculus
Linear Algebra Notes for Marsden and Tromba Vector Calculus n-dimensional Euclidean Space and Matrices Definition of n space As was learned in Math b, a point in Euclidean three space can be thought of
More information4. Complex integration: Cauchy integral theorem and Cauchy integral formulas. Definite integral of a complex-valued function of a real variable
4. Complex integration: Cauchy integral theorem and Cauchy integral formulas Definite integral of a complex-valued function of a real variable Consider a complex valued function f(t) of a real variable
More information1 Prior Probability and Posterior Probability
Math 541: Statistical Theory II Bayesian Approach to Parameter Estimation Lecturer: Songfeng Zheng 1 Prior Probability and Posterior Probability Consider now a problem of statistical inference in which
More informationLINEAR INEQUALITIES. Mathematics is the art of saying many things in many different ways. MAXWELL
Chapter 6 LINEAR INEQUALITIES 6.1 Introduction Mathematics is the art of saying many things in many different ways. MAXWELL In earlier classes, we have studied equations in one variable and two variables
More informationMathematics. (www.tiwariacademy.com : Focus on free Education) (Chapter 5) (Complex Numbers and Quadratic Equations) (Class XI)
( : Focus on free Education) Miscellaneous Exercise on chapter 5 Question 1: Evaluate: Answer 1: 1 ( : Focus on free Education) Question 2: For any two complex numbers z1 and z2, prove that Re (z1z2) =
More informationUndergraduate Notes in Mathematics. Arkansas Tech University Department of Mathematics
Undergraduate Notes in Mathematics Arkansas Tech University Department of Mathematics An Introductory Single Variable Real Analysis: A Learning Approach through Problem Solving Marcel B. Finan c All Rights
More informationVieta s Formulas and the Identity Theorem
Vieta s Formulas and the Identity Theorem This worksheet will work through the material from our class on 3/21/2013 with some examples that should help you with the homework The topic of our discussion
More informationCOMPLEX NUMBERS. a bi c di a c b d i. a bi c di a c b d i For instance, 1 i 4 7i 1 4 1 7 i 5 6i
COMPLEX NUMBERS _4+i _-i FIGURE Complex numbers as points in the Arg plane i _i +i -i A complex number can be represented by an expression of the form a bi, where a b are real numbers i is a symbol with
More informationSECOND DERIVATIVE TEST FOR CONSTRAINED EXTREMA
SECOND DERIVATIVE TEST FOR CONSTRAINED EXTREMA This handout presents the second derivative test for a local extrema of a Lagrange multiplier problem. The Section 1 presents a geometric motivation for the
More informationTOPIC 4: DERIVATIVES
TOPIC 4: DERIVATIVES 1. The derivative of a function. Differentiation rules 1.1. The slope of a curve. The slope of a curve at a point P is a measure of the steepness of the curve. If Q is a point on the
More informationt := maxγ ν subject to ν {0,1,2,...} and f(x c +γ ν d) f(x c )+cγ ν f (x c ;d).
1. Line Search Methods Let f : R n R be given and suppose that x c is our current best estimate of a solution to P min x R nf(x). A standard method for improving the estimate x c is to choose a direction
More informationBANACH AND HILBERT SPACE REVIEW
BANACH AND HILBET SPACE EVIEW CHISTOPHE HEIL These notes will briefly review some basic concepts related to the theory of Banach and Hilbert spaces. We are not trying to give a complete development, but
More informationMA106 Linear Algebra lecture notes
MA106 Linear Algebra lecture notes Lecturers: Martin Bright and Daan Krammer Warwick, January 2011 Contents 1 Number systems and fields 3 1.1 Axioms for number systems......................... 3 2 Vector
More informationCOMPLEX NUMBERS AND SERIES. Contents
COMPLEX NUMBERS AND SERIES MIKE BOYLE Contents 1. Complex Numbers Definition 1.1. A complex number is a number z of the form z = x + iy, where x and y are real numbers, and i is another number such that
More informationMaximum Likelihood Estimation
Math 541: Statistical Theory II Lecturer: Songfeng Zheng Maximum Likelihood Estimation 1 Maximum Likelihood Estimation Maximum likelihood is a relatively simple method of constructing an estimator for
More informationLectures 5-6: Taylor Series
Math 1d Instructor: Padraic Bartlett Lectures 5-: Taylor Series Weeks 5- Caltech 213 1 Taylor Polynomials and Series As we saw in week 4, power series are remarkably nice objects to work with. In particular,
More information6.2 Permutations continued
6.2 Permutations continued Theorem A permutation on a finite set A is either a cycle or can be expressed as a product (composition of disjoint cycles. Proof is by (strong induction on the number, r, of
More informationsin(x) < x sin(x) x < tan(x) sin(x) x cos(x) 1 < sin(x) sin(x) 1 < 1 cos(x) 1 cos(x) = 1 cos2 (x) 1 + cos(x) = sin2 (x) 1 < x 2
. Problem Show that using an ɛ δ proof. sin() lim = 0 Solution: One can see that the following inequalities are true for values close to zero, both positive and negative. This in turn implies that On the
More informationExamples of Functions
Chapter 3 Examples of Functions Obvious is the most dangerous word in mathematics. E. T. Bell 3.1 Möbius Transformations The first class of functions that we will discuss in some detail are built from
More informationComplex Numbers. w = f(z) z. Examples
omple Numbers Geometrical Transformations in the omple Plane For functions of a real variable such as f( sin, g( 2 +2 etc ou are used to illustrating these geometricall, usuall on a cartesian graph. If
More informationMath 4310 Handout - Quotient Vector Spaces
Math 4310 Handout - Quotient Vector Spaces Dan Collins The textbook defines a subspace of a vector space in Chapter 4, but it avoids ever discussing the notion of a quotient space. This is understandable
More informationGeneral Theory of Differential Equations Sections 2.8, 3.1-3.2, 4.1
A B I L E N E C H R I S T I A N U N I V E R S I T Y Department of Mathematics General Theory of Differential Equations Sections 2.8, 3.1-3.2, 4.1 Dr. John Ehrke Department of Mathematics Fall 2012 Questions
More informationA characterization of trace zero symmetric nonnegative 5x5 matrices
A characterization of trace zero symmetric nonnegative 5x5 matrices Oren Spector June 1, 009 Abstract The problem of determining necessary and sufficient conditions for a set of real numbers to be the
More informationInner Product Spaces
Math 571 Inner Product Spaces 1. Preliminaries An inner product space is a vector space V along with a function, called an inner product which associates each pair of vectors u, v with a scalar u, v, and
More informationWalrasian Demand. u(x) where B(p, w) = {x R n + : p x w}.
Walrasian Demand Econ 2100 Fall 2015 Lecture 5, September 16 Outline 1 Walrasian Demand 2 Properties of Walrasian Demand 3 An Optimization Recipe 4 First and Second Order Conditions Definition Walrasian
More informationDIFFERENTIABILITY OF COMPLEX FUNCTIONS. Contents
DIFFERENTIABILITY OF COMPLEX FUNCTIONS Contents 1. Limit definition of a derivative 1 2. Holomorphic functions, the Cauchy-Riemann equations 3 3. Differentiability of real functions 5 4. A sufficient condition
More informationContinued Fractions and the Euclidean Algorithm
Continued Fractions and the Euclidean Algorithm Lecture notes prepared for MATH 326, Spring 997 Department of Mathematics and Statistics University at Albany William F Hammond Table of Contents Introduction
More informationAbout the Gamma Function
About the Gamma Function Notes for Honors Calculus II, Originally Prepared in Spring 995 Basic Facts about the Gamma Function The Gamma function is defined by the improper integral Γ) = The integral is
More informationHomework 2 Solutions
Homework Solutions 1. (a) Find the area of a regular heagon inscribed in a circle of radius 1. Then, find the area of a regular heagon circumscribed about a circle of radius 1. Use these calculations to
More informationCHAPTER 5 Round-off errors
CHAPTER 5 Round-off errors In the two previous chapters we have seen how numbers can be represented in the binary numeral system and how this is the basis for representing numbers in computers. Since any
More informationLecture L3 - Vectors, Matrices and Coordinate Transformations
S. Widnall 16.07 Dynamics Fall 2009 Lecture notes based on J. Peraire Version 2.0 Lecture L3 - Vectors, Matrices and Coordinate Transformations By using vectors and defining appropriate operations between
More informationG. GRAPHING FUNCTIONS
G. GRAPHING FUNCTIONS To get a quick insight int o how the graph of a function looks, it is very helpful to know how certain simple operations on the graph are related to the way the function epression
More informationFIRST YEAR CALCULUS. Chapter 7 CONTINUITY. It is a parabola, and we can draw this parabola without lifting our pencil from the paper.
FIRST YEAR CALCULUS WWLCHENW L c WWWL W L Chen, 1982, 2008. 2006. This chapter originates from material used by the author at Imperial College, University of London, between 1981 and 1990. It It is is
More informationTHE BANACH CONTRACTION PRINCIPLE. Contents
THE BANACH CONTRACTION PRINCIPLE ALEX PONIECKI Abstract. This paper will study contractions of metric spaces. To do this, we will mainly use tools from topology. We will give some examples of contractions,
More informationSection 1.3 P 1 = 1 2. = 1 4 2 8. P n = 1 P 3 = Continuing in this fashion, it should seem reasonable that, for any n = 1, 2, 3,..., = 1 2 4.
Difference Equations to Differential Equations Section. The Sum of a Sequence This section considers the problem of adding together the terms of a sequence. Of course, this is a problem only if more than
More informationAn Introduction to Partial Differential Equations in the Undergraduate Curriculum
An Introduction to Partial Differential Equations in the Undergraduate Curriculum J. Tolosa & M. Vajiac LECTURE 11 Laplace s Equation in a Disk 11.1. Outline of Lecture The Laplacian in Polar Coordinates
More informationA power series about x = a is the series of the form
POWER SERIES AND THE USES OF POWER SERIES Elizabeth Wood Now we are finally going to start working with a topic that uses all of the information from the previous topics. The topic that we are going to
More informationLecture 17: Conformal Invariance
Lecture 17: Conformal Invariance Scribe: Yee Lok Wong Department of Mathematics, MIT November 7, 006 1 Eventual Hitting Probability In previous lectures, we studied the following PDE for ρ(x, t x 0 ) that
More informationInequalities of Analysis. Andrejs Treibergs. Fall 2014
USAC Colloquium Inequalities of Analysis Andrejs Treibergs University of Utah Fall 2014 2. USAC Lecture: Inequalities of Analysis The URL for these Beamer Slides: Inequalities of Analysis http://www.math.utah.edu/~treiberg/inequalitiesslides.pdf
More informationMATH10212 Linear Algebra. Systems of Linear Equations. Definition. An n-dimensional vector is a row or a column of n numbers (or letters): a 1.
MATH10212 Linear Algebra Textbook: D. Poole, Linear Algebra: A Modern Introduction. Thompson, 2006. ISBN 0-534-40596-7. Systems of Linear Equations Definition. An n-dimensional vector is a row or a column
More informationMASSACHUSETTS INSTITUTE OF TECHNOLOGY 6.436J/15.085J Fall 2008 Lecture 5 9/17/2008 RANDOM VARIABLES
MASSACHUSETTS INSTITUTE OF TECHNOLOGY 6.436J/15.085J Fall 2008 Lecture 5 9/17/2008 RANDOM VARIABLES Contents 1. Random variables and measurable functions 2. Cumulative distribution functions 3. Discrete
More informationElasticity. I. What is Elasticity?
Elasticity I. What is Elasticity? The purpose of this section is to develop some general rules about elasticity, which may them be applied to the four different specific types of elasticity discussed in
More informationSolutions to Practice Problems
Higher Geometry Final Exam Tues Dec 11, 5-7:30 pm Practice Problems (1) Know the following definitions, statements of theorems, properties from the notes: congruent, triangle, quadrilateral, isosceles
More informationSection 4.4 Inner Product Spaces
Section 4.4 Inner Product Spaces In our discussion of vector spaces the specific nature of F as a field, other than the fact that it is a field, has played virtually no role. In this section we no longer
More information4.3 Lagrange Approximation
206 CHAP. 4 INTERPOLATION AND POLYNOMIAL APPROXIMATION Lagrange Polynomial Approximation 4.3 Lagrange Approximation Interpolation means to estimate a missing function value by taking a weighted average
More informationSolutions for Review Problems
olutions for Review Problems 1. Let be the triangle with vertices A (,, ), B (4,, 1) and C (,, 1). (a) Find the cosine of the angle BAC at vertex A. (b) Find the area of the triangle ABC. (c) Find a vector
More informationSolutions to Homework 10
Solutions to Homework 1 Section 7., exercise # 1 (b,d): (b) Compute the value of R f dv, where f(x, y) = y/x and R = [1, 3] [, 4]. Solution: Since f is continuous over R, f is integrable over R. Let x
More informationn k=1 k=0 1/k! = e. Example 6.4. The series 1/k 2 converges in R. Indeed, if s n = n then k=1 1/k, then s 2n s n = 1 n + 1 +...
6 Series We call a normed space (X, ) a Banach space provided that every Cauchy sequence (x n ) in X converges. For example, R with the norm = is an example of Banach space. Now let (x n ) be a sequence
More informationEXIT TIME PROBLEMS AND ESCAPE FROM A POTENTIAL WELL
EXIT TIME PROBLEMS AND ESCAPE FROM A POTENTIAL WELL Exit Time problems and Escape from a Potential Well Escape From a Potential Well There are many systems in physics, chemistry and biology that exist
More information1. Prove that the empty set is a subset of every set.
1. Prove that the empty set is a subset of every set. Basic Topology Written by Men-Gen Tsai email: b89902089@ntu.edu.tw Proof: For any element x of the empty set, x is also an element of every set since
More informationSmooth functions statistics
Smooth functions statistics V. I. rnold To describe the topological structure of a real smooth function one associates to it the graph, formed by the topological variety, whose points are the connected
More informationMOP 2007 Black Group Integer Polynomials Yufei Zhao. Integer Polynomials. June 29, 2007 Yufei Zhao yufeiz@mit.edu
Integer Polynomials June 9, 007 Yufei Zhao yufeiz@mit.edu We will use Z[x] to denote the ring of polynomials with integer coefficients. We begin by summarizing some of the common approaches used in dealing
More informationMetric Spaces Joseph Muscat 2003 (Last revised May 2009)
1 Distance J Muscat 1 Metric Spaces Joseph Muscat 2003 (Last revised May 2009) (A revised and expanded version of these notes are now published by Springer.) 1 Distance A metric space can be thought of
More informationTilings of the sphere with right triangles III: the asymptotically obtuse families
Tilings of the sphere with right triangles III: the asymptotically obtuse families Robert J. MacG. Dawson Department of Mathematics and Computing Science Saint Mary s University Halifax, Nova Scotia, Canada
More informationMath 181 Handout 16. Rich Schwartz. March 9, 2010
Math 8 Handout 6 Rich Schwartz March 9, 200 The purpose of this handout is to describe continued fractions and their connection to hyperbolic geometry. The Gauss Map Given any x (0, ) we define γ(x) =
More information36 CHAPTER 1. LIMITS AND CONTINUITY. Figure 1.17: At which points is f not continuous?
36 CHAPTER 1. LIMITS AND CONTINUITY 1.3 Continuity Before Calculus became clearly de ned, continuity meant that one could draw the graph of a function without having to lift the pen and pencil. While this
More informationTHE NUMBER OF GRAPHS AND A RANDOM GRAPH WITH A GIVEN DEGREE SEQUENCE. Alexander Barvinok
THE NUMBER OF GRAPHS AND A RANDOM GRAPH WITH A GIVEN DEGREE SEQUENCE Alexer Barvinok Papers are available at http://www.math.lsa.umich.edu/ barvinok/papers.html This is a joint work with J.A. Hartigan
More informationA Uniform Asymptotic Estimate for Discounted Aggregate Claims with Subexponential Tails
12th International Congress on Insurance: Mathematics and Economics July 16-18, 2008 A Uniform Asymptotic Estimate for Discounted Aggregate Claims with Subexponential Tails XUEMIAO HAO (Based on a joint
More informationDifferentiation of vectors
Chapter 4 Differentiation of vectors 4.1 Vector-valued functions In the previous chapters we have considered real functions of several (usually two) variables f : D R, where D is a subset of R n, where
More informationk, then n = p2α 1 1 pα k
Powers of Integers An integer n is a perfect square if n = m for some integer m. Taking into account the prime factorization, if m = p α 1 1 pα k k, then n = pα 1 1 p α k k. That is, n is a perfect square
More informationCritical points of once continuously differentiable functions are important because they are the only points that can be local maxima or minima.
Lecture 0: Convexity and Optimization We say that if f is a once continuously differentiable function on an interval I, and x is a point in the interior of I that x is a critical point of f if f (x) =
More informationMMGF30, Transformteori och analytiska funktioner
MATEMATIK Göteborgs universitet Tentamen 06-03-8, 8:30-:30 MMGF30, Transformteori och analytiska funktioner Examiner: Mahmood Alaghmandan, tel: 77 53 74, Email: mahala@chalmers.se Telefonvakt: 07 97 5630
More informationTaylor and Maclaurin Series
Taylor and Maclaurin Series In the preceding section we were able to find power series representations for a certain restricted class of functions. Here we investigate more general problems: Which functions
More information5 Numerical Differentiation
D. Levy 5 Numerical Differentiation 5. Basic Concepts This chapter deals with numerical approximations of derivatives. The first questions that comes up to mind is: why do we need to approximate derivatives
More informationAlgebraic and Transcendental Numbers
Pondicherry University July 2000 Algebraic and Transcendental Numbers Stéphane Fischler This text is meant to be an introduction to algebraic and transcendental numbers. For a detailed (though elementary)
More informationRepresentation of functions as power series
Representation of functions as power series Dr. Philippe B. Laval Kennesaw State University November 9, 008 Abstract This document is a summary of the theory and techniques used to represent functions
More informationMath 21a Curl and Divergence Spring, 2009. 1 Define the operator (pronounced del ) by. = i
Math 21a url and ivergence Spring, 29 1 efine the operator (pronounced del by = i j y k z Notice that the gradient f (or also grad f is just applied to f (a We define the divergence of a vector field F,
More informationOnline Appendix to Stochastic Imitative Game Dynamics with Committed Agents
Online Appendix to Stochastic Imitative Game Dynamics with Committed Agents William H. Sandholm January 6, 22 O.. Imitative protocols, mean dynamics, and equilibrium selection In this section, we consider
More informationThe sample space for a pair of die rolls is the set. The sample space for a random number between 0 and 1 is the interval [0, 1].
Probability Theory Probability Spaces and Events Consider a random experiment with several possible outcomes. For example, we might roll a pair of dice, flip a coin three times, or choose a random real
More information