ÒÄÆÉÖÌÄ. ÐÄÒÉÏÃÖËÉ ÀÌÏÝÀÍÉÓ x = f(t, x), x(0) = x(1) ÀÌÏáÓÍÀÃÏÁÉÓ ÊÀÒÂÀÃ ÝÍÏÁÉËÉ

Size: px
Start display at page:

Download "ÒÄÆÉÖÌÄ. ÐÄÒÉÏÃÖËÉ ÀÌÏÝÀÍÉÓ x = f(t, x), x(0) = x(1) ÀÌÏáÓÍÀÃÏÁÉÓ ÊÀÒÂÀÃ ÝÍÏÁÉËÉ"

Transcription

1

2 x = f(t, x x( = x(1 1 1 x(1 = dh(s x(s x( = dh(s x(s h ÒÄÆÉÖÌÄ. ÐÄÒÉÏÃÖËÉ ÀÌÏÝÀÍÉÓ x = f(t, x, x( = x(1 ÀÌÏáÓÍÀÃÏÁÉÓ ÊÀÒÂÀà ÝÍÏÁÉËÉ 1 ÊÒÀÓÍÏÓÄËÓÊÉÓ ÃÀ ÂÖÓÔÀÅÓÏÍ-ÛÌÉÔÉÓ ÛÄÃÄÂÄÁÉ ÂÀÍÆÏÂÀÃÄÁÖËÉ ÉØÍÀ x(1 = dh(s x(s ÃÀ x( = 1 dh(s x(s ÓÀáÉÓ ÀÒÀËÏÊÀËÖÒÉ ÓÀÓÀÆÙÅÒÏ ÐÉÒÏÁÄÁÉÓ ÛÄÌÈáÅÄÅÀÛÉ (h ÍÀÌÃÅÉËÉ ÀÒÀÊËÄÁÀÃÉ ÖÍØÝÉÀÀ, ÒÏÌËÄÁÉÝ ÌÏÉÝÀÅÄÍ ÐÄÒÉÏÃÖË ÓÀÓÀÆÙÅÒÏ ÐÉÒÏÁÄÁÓ, ÒÏÂÏÒÝ ÊÄÒÞÏ ÛÄÌÈáÅÄÅÄÁÓ. ÊÏÍÔÒÌÀÂÀËÉÈÄÁÉÓ ÓÀÛÖÀËÄÁÉÈ ÛÄÃÀÒÄÁÖËÉÀ ÐÄÒÉÏÃÖËÉ ÃÀ ÀÒÀ- ËÏÊÀËÖÒÉ ÓÀÓÀÆÙÅÒÏ ÐÉÒÏÁÄÁÉÓ ÛÄÌÈáÅÄÅÄÁÉ, ÒÏÌËÄÁÉÝ ÀÅËÄÍÄÍ ÐÄÒÉÏÃÖËÉ ÛÄÌÈáÅÄÅÉÓ ÓÐÄÝÉÀËÖÒ áàóéàèó. ÀÍÀËÏÂÉÖÒÉ ÊÏÍÔÒÌÀÂÀËÉÈÄÁÉ ÀÂÒÄÈÅÄ ÂÅÉÜÅÄÍÄÁÄÍ, ÒÏÌ ÌÄÏÒÄ ÒÉÂÉÓ ÓÉÓÔÄÌÄÁÉÓ ÛÄÌÈáÅÄÅÀÛÉ ÂÀÒÊÅÄÖËÉ ÀÒÀËÏÊÀËÖÒÉ ÓÀÓÀÆÙÅÒÏ ÐÉÒÏÁÄÁÉÈ ÆÏÂÉÄÒÈÉ ÖÔÏËÏÁÉÓ ÍÉÛÍÉÓ ÛÄÁÒÖÍÄÁÀ ÀÒ ÛÄÉÞËÄÁÀ.

3 R n B R R n R f : [, 1] R n R n x = f(t, x, x( = x(1. R > u f(t, u (t, u [, 1] B R, u f(t, u (t, u [, 1] B R, x([, 1] B R τ = 1 t e C([, 1], R n λ R \ {} x = λx + e(t, x( = x(1 d dt R n f : [, 1] H H f(t, t [, 1] R > u f(t, u < (t, u [, 1] B R C R n Φ i C 1 (R n, R (i = 1,..., r C = {u R n : Φ i (u (i = 1,..., r} Φ i (u Φ i (u = u C Φi (u f(t, u (t, u [, 1] C i α(u, Φi (u f(t, u (t, u [, 1] C i α(u, α(u := {i {1,..., r} : Φ i (u = } x([, 1] C C = B R r = 1 Φ 1 (u = 1 ( u R C R n R n u C ν(u R n \ {} ν(u u > C {v R n : ν(u v u < } ν(u C u ν : C R n \ {} C ν ν(u C u C R n ν C ν(u f(t, u > (t, u [, 1] C,

4 ν(u f(t, u < (t, u [, 1] C, x([, 1] C C = B R C C C u C i α(u Φ i (u C u 1 x = f(t, x, x(1 = dh(s x(s, x = f(t, x, x( = 1 dh(s x(s h : [, 1] R 1 dh(s = 1. x( = x(1 x( x(1 x [, 1] h( < h(α α (, 1 C R n ν C ν(u f(t, u (t, u [, 1] C, x x([, 1] C h(α < h(1 α (, 1 C R n ν C ν(u f(t, u (t, u [, 1] C, x x([, 1] C C = B R h( < h(α α (, 1 R > u f(t, u (t, u [, 1] B R, x x([, 1] B R

5 h(α < h(1 α (, 1 R > u f(t, u (t, u [, 1] B R, x x([, 1] B R x = λx + e(t, x(1 = 1 [ ( 1 ] x + x(, x ( = 1 [ ( 1 ] x + x(1, e C([, 1], R n λ R \ {} d dt f λ e C([, 1], C z = λz + e(t, z : [, 1] C x 1 = Rz, x = Iz, f 1 (t, x = R(λz + e(t, f (t, x = I(λz + e(t, u f(t, u t [, 1] u 3 n x, f(t, x x = R R > x = g(t, x, x, x( =, x (1 = dh(s x (s, 1 g : [, 1] R n R n R n h : [, 1] R 1 dh(s = 1 h( < h(α α (, 1 C R n ν C ν(v g(t, u, v (t, u, v [, 1] C C, x x([, 1] C x ([, 1] C

6 C = B R h( < h(α α (, 1 R > v g(t, u, v (t, u, v [, 1] B R B R, x x([, 1] B R x ([, 1] B R x = g(t, x, x, x( =, x ( = dh(s x (s, 1 h(α < h(1 α (, 1 g h(α < h(1 α (, 1 C R n ν C ν(v g(t, u, v (t, u, v [, 1] C C, x x([, 1] C x ([, 1] C x = g(t, x, x, x( =, x ( = x (1 R > v g(t, u, v (t, u, v [, 1] B R B R, v g(t, u, v (t, u, v [, 1] B R B R. z (t = λz(t, z(1 = 1 [ ( 1 z( + z, ] λ C z : [, 1] C s =, h(s = 1/ s (, 1/] 1 s (1/, 1]. λ tc,1,k = k(πi λ tc,,k = 4 + (k + 1(πi (k Z

7 λ C e λ = eλ/. µ := e λ/ µ µ 1 µ 1 =, µ tc,1 = 1 µ tc, = 1 eλ/ = µ tc,1 = 1 λ = kπi (k Z λ tc,1,k = k(πi (k Z. e λ/ = µ tc, = 1 λ = + πi + kπi = + (k + 1πi (k Z λ tc,,k = 4 + (k + 1(πi (k Z. z (t = λz(t, z( = 1 [ ( 1 ] z + z(1, λ C z : [, 1] C s [, 1/, h(s = 1/ s [1/, 1, 1 s = 1. λ ic,1,k = k(πi λ ic,,k = 4 + (k + 1(πi (k Z λ C µ := e λ/ µ 1 = 1 eλ/ + 1 eλ. 1 µ + 1 µ 1 = µ ic,1 = 1 µ ic, = λ ic,1,k = k(πi (k Z λ ic,,k = 4 + (k + 1(πi (k Z. z = λz, z( = z(1 λ p,k = k(πi (k Z Rz = 4 Rz = 4

8 λ ( e ( Lz := z z = e(t, z( = 1 ( 1 z + 1 z(1 Mz := z + z = e(t, z( = 1 z ( z(1 z = L 1 e z = M 1 e e C([, 1], C L 1 M 1 C([, 1], C z λz = e(t, z(1 = 1 z( + 1 ( 1 z z + λz = e(t, z( = 1 z ( 1 z = (λ 1L 1 z + L 1 e, z = (λ + 1M 1 z + M 1 e, + 1 z(1 λ tc,, = 4 + (4k + πi e : [, 1] C z (t = ( 4 + πiz(t + e(t, z(1 = 1 z( + 1 ( 1 z z(t = x 1 (t + ix (t e(t = e 1 (t + ie (t x 1(t = ( 4x 1 (t πx (t + e 1 (t, x (t = πx 1 (t ( 4x (t + e (t, x 1 (1 = 1 x 1( + 1 ( 1 x 1, x (1 = 1 x ( + 1 ( 1 x. f(t, u := ( ( 4u 1 πu + e 1 (t, πu 1 ( 4u + e (t. u f(t, u = u 1 [ ( 4u1 πu + e 1 (t ] + u [ πu1 ( 4u + e (t ] = ( 4(u 1 + u + u 1 e 1 (t + u e (t ( 4 u + e(t u <,

9 u R R f R > u f(t, u (t, u [, 1] B R, λ ic,, = 4 + πi e : [, 1] C z (t = ( 4 + πiz(t + e(t, z(1 = 1 z( + 1 ( 1 z z(t = x 1 (t + ix (t e(t = e 1 (t + ie (t x 1(t = ( 4x 1 (t πx (t + e 1 (t, x (t = πx 1 (t + ( 4x (t + e (t, x 1 ( = 1 ( 1 x x 1(1, x ( = 1 ( 1 x + 1 x (1. f(t, u := ( ( 4u 1 πu + e 1 (t, πu 1 + ( 4u + e (t. u f(t, u = u 1 [( 4u 1 πu + e 1 (t] + u [ πu1 + ( 4u + e (t ] = ( 4(u 1 + u + u 1 e 1 (t + u e (t ( 4 u e(t u >, u R R f R > u f(t, u (t, u [, 1] B R, f n = 1 n n = 3, x 3 = ( 4x (x 1 + x, x 3 (1 = 1 [ ( 1 ] x 3 ( + x 3 x 3 = ( 4x (x 1 + x, x 3 ( = 1 [ ( 1 ] x 3 + x 3 (1 4 n = n = 3 n n = 1

10 z = πiz + e πit, z( = z(1 z (e πit z = 1, [, 1] z = x 1 + ix x 1 = πx + (πt, x = πx 1 + (πt, x 1 ( = x 1 (1, x ( = x (1, f 1 (t, x 1, x = πx + (πt, f (t, x 1, x = πx 1 + (πt, x = (x 1, x, f(t, x = ( f 1 (t, x 1, x, f (t, x 1, x, x, f(t, x = πx x 1 + (πtx 1 + πx 1 x + (πtx = (πtx 1 + (πtx. x = R[ (πθ, (πθ] B R (θ [, 1] x, f(t, x = R [ (πt (πθ + (πt (πθ ] = R [π(t θ] (t, θ [, 1], t [, 1] x, f(t, x B R n z (t = λz (t, z( =, z (1 = 1 z ( + 1 z ( 1, λ C x : [, 1] C z λz λz λ bc,j,k (j = 1, k Z 4 w(t = z (t z( = z(t = w (t = λw(t, w(1 = 1 w( + 1 w ( 1 t w(s ds λ e,

11 z λz = e(t, z( =, z (1 = 1 z ( + 1 z ( 1 w = z w λw = e(t, w(1 = 1 [ ( 1 w( + w, ] λ bc,, = 4 + πi e : [, 1] C z (t = ( 4 + πiz (t + e(t, z( =, z (1 = 1 z ( + 1 z ( 1 z(t = x 1 (t + ix (t e(t = e 1 (t + ie (t x 1(t = ( 4x 1(t πx (t + e 1 (t, x (t = πx 1(t ( 4x (t + e (t, x 1 ( =, x 1(1 = 1 x 1( + 1 ( 1 x 1, x ( =, x (1 = 1 x ( + 1 ( 1 x. g(t, v := ( ( 4v 1 (t πv (t + e 1 (t, πv 1 (t ( 4v (t + e (t. v, g(t, v < v R R g R > v, g(t, u, v (t, u, v [, 1] B R B R,

12 p

Inference. Jesús Fernández-Villaverde University of Pennsylvania

Inference. Jesús Fernández-Villaverde University of Pennsylvania Inference Jesús Fernández-Villaverde University of Pennsylvania 1 A Model with Sticky Price and Sticky Wage Household j [0, 1] maximizes utility function: X E 0 β t t=0 G t ³ C j t 1 1 σ 1 1 σ ³ N j t

More information

G P P (A G ) (A G ) P (A G )

G P P (A G ) (A G ) P (A G ) 1 1 1 G P P (A G ) A G G (A G ) P (A G ) P (A G ) (A G ) (A G ) A G P (A G ) (A G ) (A G ) A G G A G i, j A G i j C = {0, 1,..., k} i j c > 0 c v k k + 1 k = 4 k = 5 5 5 R(4, 3, 3) 30 n {1,..., n} true

More information

Discontinuous Galerkin methods for fractional diffusion problems

Discontinuous Galerkin methods for fractional diffusion problems Discontinuous Galerkin methods for fractional diffusion problems Bill McLean Kassem Mustapha School of Maths and Stats, University of NSW KFUPM, Dhahran Leipzig, 7 October, 2010 Outline Sub-diffusion Equation

More information

e st f (t) dt = e st tf(t) dt = L {t f(t)} s

e st f (t) dt = e st tf(t) dt = L {t f(t)} s Additional operational properties How to find the Laplace transform of a function f (t) that is multiplied by a monomial t n, the transform of a special type of integral, and the transform of a periodic

More information

MA 201, Mathematics III, July-November 2018, Laplace Transform (Contd.)

MA 201, Mathematics III, July-November 2018, Laplace Transform (Contd.) MA 201, Mathematics III, July-November 2018, Laplace Transform (Contd.) Lecture 19 Lecture 19 MA 201, PDE (2018) 1 / 24 Application of Laplace transform in solving ODEs ODEs with constant coefficients

More information

Introduction to Discrete Optimization

Introduction to Discrete Optimization Prof. Friedrich Eisenbrand Martin Niemeier Due Date: April 28, 2009 Discussions: April 2, 2009 Introduction to Discrete Optimization Spring 2009 s 8 Exercise What is the smallest number n such that an

More information

Splitting methods with boundary corrections

Splitting methods with boundary corrections Splitting methods with boundary corrections Alexander Ostermann University of Innsbruck, Austria Joint work with Lukas Einkemmer Verona, April/May 2017 Strang s paper, SIAM J. Numer. Anal., 1968 S (5)

More information

Complex Analysis, Stein and Shakarchi The Fourier Transform

Complex Analysis, Stein and Shakarchi The Fourier Transform Complex Analysis, Stein and Shakarchi Chapter 4 The Fourier Transform Yung-Hsiang Huang 2017.11.05 1 Exercises 1. Suppose f L 1 (), and f 0. Show that f 0. emark 1. This proof is observed by Newmann (published

More information

Mathematics of Physics and Engineering II: Homework answers You are encouraged to disagree with everything that follows. P R and i j k 2 1 1

Mathematics of Physics and Engineering II: Homework answers You are encouraged to disagree with everything that follows. P R and i j k 2 1 1 Mathematics of Physics and Engineering II: Homework answers You are encouraged to disagree with everything that follows Homework () P Q = OQ OP =,,,, =,,, P R =,,, P S = a,, a () The vertex of the angle

More information

Verona Course April Lecture 1. Review of probability

Verona Course April Lecture 1. Review of probability Verona Course April 215. Lecture 1. Review of probability Viorel Barbu Al.I. Cuza University of Iaşi and the Romanian Academy A probability space is a triple (Ω, F, P) where Ω is an abstract set, F is

More information

Chapter 31. The Laplace Transform The Laplace Transform. The Laplace transform of the function f(t) is defined. e st f(t) dt, L[f(t)] =

Chapter 31. The Laplace Transform The Laplace Transform. The Laplace transform of the function f(t) is defined. e st f(t) dt, L[f(t)] = Chapter 3 The Laplace Transform 3. The Laplace Transform The Laplace transform of the function f(t) is defined L[f(t)] = e st f(t) dt, for all values of s for which the integral exists. The Laplace transform

More information

EE 3054: Signals, Systems, and Transforms Summer It is observed of some continuous-time LTI system that the input signal.

EE 3054: Signals, Systems, and Transforms Summer It is observed of some continuous-time LTI system that the input signal. EE 34: Signals, Systems, and Transforms Summer 7 Test No notes, closed book. Show your work. Simplify your answers. 3. It is observed of some continuous-time LTI system that the input signal = 3 u(t) produces

More information

BANDLIMITED APPROXIMATIONS TO THE TRUNCATED GAUSSIAN AND APPLICATIONS

BANDLIMITED APPROXIMATIONS TO THE TRUNCATED GAUSSIAN AND APPLICATIONS BANDLIMITED APPROXIMATIONS TO THE TRUNCATED GAUSSIAN AND APPLICATIONS EMANUEL CARNEIRO AND FRIEDRICH LITTMANN Abstract. In this paper we extend the theory of optimal approximations of functions f : R R

More information

Computing inverse Laplace Transforms.

Computing inverse Laplace Transforms. Review Exam 3. Sections 4.-4.5 in Lecture Notes. 60 minutes. 7 problems. 70 grade attempts. (0 attempts per problem. No partial grading. (Exceptions allowed, ask you TA. Integration table included. Complete

More information

Multiple Choice Review Problems

Multiple Choice Review Problems Multiple Choice Review Problems 1. (NC) Which graph best represents the position of a particle, st ( ), as a function of time, if the particle's velocity is negative and the particle's acceleration is

More information

1 Infinitely Divisible Random Variables

1 Infinitely Divisible Random Variables ENSAE, 2004 1 2 1 Infinitely Divisible Random Variables 1.1 Definition A random variable X taking values in IR d is infinitely divisible if its characteristic function ˆµ(u) =E(e i(u X) )=(ˆµ n ) n where

More information

DIFFERENTIAL CRITERIA FOR POSITIVE DEFINITENESS

DIFFERENTIAL CRITERIA FOR POSITIVE DEFINITENESS PROCEEDINGS OF THE AMERICAN MATHEMATICAL SOCIETY Volume, Number, Pages S -9939(XX)- DIFFERENTIAL CRITERIA FOR POSITIVE DEFINITENESS J. A. PALMER Abstract. We show how the Mellin transform can be used to

More information

Handout 1 - Contour Integration

Handout 1 - Contour Integration Handout 1 - Contour Integration Will Matern September 19, 214 Abstract The purpose of this handout is to summarize what you need to know to solve the contour integration problems you will see in SBE 3.

More information

Complex Analysis. Travis Dirle. December 4, 2016

Complex Analysis. Travis Dirle. December 4, 2016 Complex Analysis 2 Complex Analysis Travis Dirle December 4, 2016 2 Contents 1 Complex Numbers and Functions 1 2 Power Series 3 3 Analytic Functions 7 4 Logarithms and Branches 13 5 Complex Integration

More information

Boundary Value Problems in Cylindrical Coordinates

Boundary Value Problems in Cylindrical Coordinates Boundary Value Problems in Cylindrical Coordinates 29 Outline Differential Operators in Various Coordinate Systems Laplace Equation in Cylindrical Coordinates Systems Bessel Functions Wave Equation the

More information

Part IB. Complex Analysis. Year

Part IB. Complex Analysis. Year Part IB Complex Analysis Year 2018 2017 2016 2015 2014 2013 2012 2011 2010 2009 2008 2007 2006 2005 2018 Paper 1, Section I 2A Complex Analysis or Complex Methods 7 (a) Show that w = log(z) is a conformal

More information

Calculus of Variations Summer Term 2015

Calculus of Variations Summer Term 2015 Calculus of Variations Summer Term 2015 Lecture 14 Universität des Saarlandes 24. Juni 2015 c Daria Apushkinskaya (UdS) Calculus of variations lecture 14 24. Juni 2015 1 / 20 Purpose of Lesson Purpose

More information

Differentiability of solutions with respect to the delay function in functional differential equations

Differentiability of solutions with respect to the delay function in functional differential equations Electronic Journal of Qualitative Theory of Differential Equations 216, No. 73, 1 16; doi: 1.14232/ejqtde.216.1.73 http://www.math.u-szeged.hu/ejqtde/ Differentiability of solutions with respect to the

More information

Applications of Ito s Formula

Applications of Ito s Formula CHAPTER 4 Applications of Ito s Formula In this chapter, we discuss several basic theorems in stochastic analysis. Their proofs are good examples of applications of Itô s formula. 1. Lévy s martingale

More information

PAPER 44 ADVANCED QUANTUM FIELD THEORY

PAPER 44 ADVANCED QUANTUM FIELD THEORY MATHEMATICAL TRIPOS Part III Friday, 3 May, 203 9:00 am to 2:00 pm PAPER 44 ADVANCED QUANTUM FIELD THEORY Attempt no more than THREE questions. There are FOUR questions in total. The questions carry equal

More information

Optimal Linear Feedback Control for Incompressible Fluid Flow

Optimal Linear Feedback Control for Incompressible Fluid Flow Optimal Linear Feedback Control for Incompressible Fluid Flow Miroslav K. Stoyanov Thesis submitted to the Faculty of the Virginia Polytechnic Institute and State University in partial fulfillment of the

More information

Optimal investment strategies for an index-linked insurance payment process with stochastic intensity

Optimal investment strategies for an index-linked insurance payment process with stochastic intensity for an index-linked insurance payment process with stochastic intensity Warsaw School of Economics Division of Probabilistic Methods Probability space ( Ω, F, P ) Filtration F = (F(t)) 0 t T satisfies

More information

INTRODUCTION TO COMPLEX ANALYSIS W W L CHEN

INTRODUCTION TO COMPLEX ANALYSIS W W L CHEN INTRODUTION TO OMPLEX NLYSIS W W L HEN c W W L hen, 1986, 28. This chapter originates from material used by the author at Imperial ollege, University of London, between 1981 and 199. It is available free

More information

Complex Inversion Formula for Exponential Integral Transform with Applications

Complex Inversion Formula for Exponential Integral Transform with Applications Int. J. Contemp. Math. Sciences, Vol. 3, 28, no. 6, 78-79 Complex Inversion Formula for Exponential Integral Transform with Applications A. Aghili and Z. Kavooci Department of Mathematics, Faculty of Sciences

More information

From the Heisenberg group to Carnot groups

From the Heisenberg group to Carnot groups From the Heisenberg group to Carnot groups p. 1/47 From the Heisenberg group to Carnot groups Irina Markina, University of Bergen, Norway Summer school Analysis - with Applications to Mathematical Physics

More information

7.2. Matrix Multiplication. Introduction. Prerequisites. Learning Outcomes

7.2. Matrix Multiplication. Introduction. Prerequisites. Learning Outcomes Matrix Multiplication 7.2 Introduction When we wish to multiply matrices together we have to ensure that the operation is possible - and this is not always so. Also, unlike number arithmetic and algebra,

More information

APPM GRADUATE PRELIMINARY EXAMINATION PARTIAL DIFFERENTIAL EQUATIONS SOLUTIONS

APPM GRADUATE PRELIMINARY EXAMINATION PARTIAL DIFFERENTIAL EQUATIONS SOLUTIONS Thursday August 24, 217, 1AM 1PM There are five problems. Solve any four of the five problems. Each problem is worth 25 points. On the front of your bluebook please write: (1) your name and (2) a grading

More information

Arc Length. Philippe B. Laval. Today KSU. Philippe B. Laval (KSU) Arc Length Today 1 / 12

Arc Length. Philippe B. Laval. Today KSU. Philippe B. Laval (KSU) Arc Length Today 1 / 12 Philippe B. Laval KSU Today Philippe B. Laval (KSU) Arc Length Today 1 / 12 Introduction In this section, we discuss the notion of curve in greater detail and introduce the very important notion of arc

More information

EXISTENCE OF NEUTRAL STOCHASTIC FUNCTIONAL DIFFERENTIAL EQUATIONS WITH ABSTRACT VOLTERRA OPERATORS

EXISTENCE OF NEUTRAL STOCHASTIC FUNCTIONAL DIFFERENTIAL EQUATIONS WITH ABSTRACT VOLTERRA OPERATORS International Journal of Differential Equations and Applications Volume 7 No. 1 23, 11-17 EXISTENCE OF NEUTRAL STOCHASTIC FUNCTIONAL DIFFERENTIAL EQUATIONS WITH ABSTRACT VOLTERRA OPERATORS Zephyrinus C.

More information

Simplified Microphysics. condensation evaporation. evaporation

Simplified Microphysics. condensation evaporation. evaporation Simplified Microphysics water vapor condensation evaporation cloud droplets evaporation condensation collection rain drops fall out (precipitation) = 0 (reversible) = (irreversible) Simplified Microphysics

More information

Second In-Class Exam Solutions Math 246, Professor David Levermore Thursday, 31 March 2011

Second In-Class Exam Solutions Math 246, Professor David Levermore Thursday, 31 March 2011 Second In-Class Exam Solutions Math 246, Professor David Levermore Thursday, 31 March 211 (1) [6] Give the interval of definition for the solution of the initial-value problem d 4 y dt 4 + 7 1 t 2 dy dt

More information

Runge-Kutta and Collocation Methods Florian Landis

Runge-Kutta and Collocation Methods Florian Landis Runge-Kutta and Collocation Methods Florian Landis Geometrical Numetric Integration p.1 Overview Define Runge-Kutta methods. Introduce collocation methods. Identify collocation methods as Runge-Kutta methods.

More information

Rates of Convergence to Self-Similar Solutions of Burgers Equation

Rates of Convergence to Self-Similar Solutions of Burgers Equation Rates of Convergence to Self-Similar Solutions of Burgers Equation by Joel Miller Andrew Bernoff, Advisor Advisor: Committee Member: May 2 Department of Mathematics Abstract Rates of Convergence to Self-Similar

More information

EE 261 The Fourier Transform and its Applications Fall 2007 Solutions to Midterm Exam

EE 261 The Fourier Transform and its Applications Fall 2007 Solutions to Midterm Exam EE 26 The Fourier Transform and its Applications Fall 27 Solutions to Midterm Exam There are 5 questions for a total of points. Please write your answers in the exam booklet provided, and make sure that

More information

Control Systems. Frequency domain analysis. L. Lanari

Control Systems. Frequency domain analysis. L. Lanari Control Systems m i l e r p r a in r e v y n is o Frequency domain analysis L. Lanari outline introduce the Laplace unilateral transform define its properties show its advantages in turning ODEs to algebraic

More information

Joint work with Nguyen Hoang (Univ. Concepción, Chile) Padova, Italy, May 2018

Joint work with Nguyen Hoang (Univ. Concepción, Chile) Padova, Italy, May 2018 EXTENDED EULER-LAGRANGE AND HAMILTONIAN CONDITIONS IN OPTIMAL CONTROL OF SWEEPING PROCESSES WITH CONTROLLED MOVING SETS BORIS MORDUKHOVICH Wayne State University Talk given at the conference Optimization,

More information

Surface x(u, v) and curve α(t) on it given by u(t) & v(t). Math 4140/5530: Differential Geometry

Surface x(u, v) and curve α(t) on it given by u(t) & v(t). Math 4140/5530: Differential Geometry Surface x(u, v) and curve α(t) on it given by u(t) & v(t). α du dv (t) x u dt + x v dt Surface x(u, v) and curve α(t) on it given by u(t) & v(t). α du dv (t) x u dt + x v dt ( ds dt )2 Surface x(u, v)

More information

1. COMPLEX NUMBERS. z 1 + z 2 := (a 1 + a 2 ) + i(b 1 + b 2 ); Multiplication by;

1. COMPLEX NUMBERS. z 1 + z 2 := (a 1 + a 2 ) + i(b 1 + b 2 ); Multiplication by; 1. COMPLEX NUMBERS Notations: N the set of the natural numbers, Z the set of the integers, R the set of real numbers, Q := the set of the rational numbers. Given a quadratic equation ax 2 + bx + c = 0,

More information

Distributed Set Reachability

Distributed Set Reachability Dstt St Rty S Gj Mt T Mx-P Isttt Its, Usty U Gy SIGMOD 2016, S Fs, USA Dstt St Rty Dstt St Rty (DSR) s zt ty xt t sts stt stt Dstt St Rty 2 Dstt St Rty Dstt St Rty (DSR) s zt ty xt t sts stt stt Dstt St

More information

Group Actions and Cohomology in the Calculus of Variations

Group Actions and Cohomology in the Calculus of Variations Group Actions and Cohomology in the Calculus of Variations JUHA POHJANPELTO Oregon State and Aalto Universities Focused Research Workshop on Exterior Differential Systems and Lie Theory Fields Institute,

More information

EXAMPLE OF LARGE DEVIATIONS FOR STATIONARY PROCESS. O.V.Gulinskii*, and R.S.Liptser**

EXAMPLE OF LARGE DEVIATIONS FOR STATIONARY PROCESS. O.V.Gulinskii*, and R.S.Liptser** EXAMPLE OF LARGE DEVIATIONS FOR STATIONARY PROCESS O.V.Gulinskii*, and R.S.Liptser** *Institute for Problems of Information Transmission Moscow, RUSSIA **Department of Electrical Engineering-Systems Tel

More information

Section 3.1 Homework Solutions. 1. y = 5, so dy dx = y = 3x, so dy dx = y = x 12, so dy. dx = 12x11. dx = 12x 13

Section 3.1 Homework Solutions. 1. y = 5, so dy dx = y = 3x, so dy dx = y = x 12, so dy. dx = 12x11. dx = 12x 13 Math 122 1. y = 5, so dx = 0 2. y = 3x, so dx = 3 3. y = x 12, so dx = 12x11 4. y = x 12, so dx = 12x 13 5. y = x 4/3, so dx = 4 3 x1/3 6. y = 8t 3, so = 24t2 7. y = 3t 4 2t 2, so = 12t3 4t 8. y = 5x +

More information

Chapter 6 Integral Transform Functional Calculi

Chapter 6 Integral Transform Functional Calculi Chapter 6 Integral Transform Functional Calculi In this chapter we continue our investigations from the previous one and encounter functional calculi associated with various semigroup representations.

More information

Lecture Notes for Math 524

Lecture Notes for Math 524 Lecture Notes for Math 524 Dr Michael Y Li October 19, 2009 These notes are based on the lecture notes of Professor James S Muldowney, the books of Hale, Copple, Coddington and Levinson, and Perko They

More information

ECEN 605 LINEAR SYSTEMS. Lecture 7 Solution of State Equations 1/77

ECEN 605 LINEAR SYSTEMS. Lecture 7 Solution of State Equations 1/77 1/77 ECEN 605 LINEAR SYSTEMS Lecture 7 Solution of State Equations Solution of State Space Equations Recall from the previous Lecture note, for a system: ẋ(t) = A x(t) + B u(t) y(t) = C x(t) + D u(t),

More information

Part IB. Further Analysis. Year

Part IB. Further Analysis. Year Year 2004 2003 2002 2001 10 2004 2/I/4E Let τ be the topology on N consisting of the empty set and all sets X N such that N \ X is finite. Let σ be the usual topology on R, and let ρ be the topology on

More information

Calculus of Variations Summer Term 2016

Calculus of Variations Summer Term 2016 Calculus of Variations Summer Term 2016 Lecture 14 Universität des Saarlandes 28. Juni 2016 c Daria Apushkinskaya (UdS) Calculus of variations lecture 14 28. Juni 2016 1 / 31 Purpose of Lesson Purpose

More information

Calculus II Practice Test Problems for Chapter 7 Page 1 of 6

Calculus II Practice Test Problems for Chapter 7 Page 1 of 6 Calculus II Practice Test Problems for Chapter 7 Page of 6 This is a set of practice test problems for Chapter 7. This is in no way an inclusive set of problems there can be other types of problems on

More information

Infinite-dimensional methods for path-dependent equations

Infinite-dimensional methods for path-dependent equations Infinite-dimensional methods for path-dependent equations (Università di Pisa) 7th General AMaMeF and Swissquote Conference EPFL, Lausanne, 8 September 215 Based on Flandoli F., Zanco G. - An infinite-dimensional

More information

f(t, ε) = o(g(t, ε)) as ε 0

f(t, ε) = o(g(t, ε)) as ε 0 SET 9 MATH 543: PERTURBATION Reference: Dvid Logn. About the uniform convergence of perturbtion series we hve minly the following three definitions Definition 1: Let f(t, ε) nd g(t, ε) be defined for ll

More information

The Left Invariant Metrics which is Defined on Heisenberg Group

The Left Invariant Metrics which is Defined on Heisenberg Group International Mathematical Forum, 1, 2006, no. 38, 1887-1892 The Left Invariant Metrics which is Defined on Heisenberg Group Essin TURHAN and Necdet CATALBAS Fırat University, Department of Mathematics

More information

SYNCHRONIZATION OF NONAUTONOMOUS DYNAMICAL SYSTEMS

SYNCHRONIZATION OF NONAUTONOMOUS DYNAMICAL SYSTEMS Electronic Journal of Differential Equations, Vol. 003003, No. 39, pp. 1 10. ISSN: 107-6691. URL: http://ejde.math.swt.edu or http://ejde.math.unt.edu ftp ejde.math.swt.edu login: ftp SYNCHRONIZATION OF

More information

1 The functional equation for ζ

1 The functional equation for ζ 18.785: Analytic Number Theory, MIT, spring 27 (K.S. Kedlaya) The functional equation for the Riemann zeta function In this unit, we establish the functional equation property for the Riemann zeta function,

More information

Prof. Erhan Bayraktar (University of Michigan)

Prof. Erhan Bayraktar (University of Michigan) September 17, 2012 KAP 414 2:15 PM- 3:15 PM Prof. (University of Michigan) Abstract: We consider a zero-sum stochastic differential controller-and-stopper game in which the state process is a controlled

More information

Calculus II/III Summer Packet

Calculus II/III Summer Packet Calculus II/III Summer Packet First of all, have a great summer! Enjoy your time away from school. Come back fired up and ready to learn. I know that I will be ready to have a great year of calculus with

More information

Math 2413 Exam 2 Litong Name Test No

Math 2413 Exam 2 Litong Name Test No Math 413 Eam Litong Name Test No Find the equation for the tangent to the curve at the given point. 1) f() = - ; (1, 0) C) The graph of a function is given. Choose the answer that represents the graph

More information

2905 Queueing Theory and Simulation PART III: HIGHER DIMENSIONAL AND NON-MARKOVIAN QUEUES

2905 Queueing Theory and Simulation PART III: HIGHER DIMENSIONAL AND NON-MARKOVIAN QUEUES 295 Queueing Theory and Simulation PART III: HIGHER DIMENSIONAL AND NON-MARKOVIAN QUEUES 16 Queueing Systems with Two Types of Customers In this section, we discuss queueing systems with two types of customers.

More information

Order Preserving Properties of Vehicle Dynamics with Respect to the Driver s Input

Order Preserving Properties of Vehicle Dynamics with Respect to the Driver s Input Order Preserving Properties of Vehicle Dynamics with Respect to the Driver s Input Mojtaba Forghani and Domitilla Del Vecchio Massachusetts Institute of Technology September 19, 214 1 Introduction In this

More information

Outline. Sets. Relations. Functions. Products. Sums 2 / 40

Outline. Sets. Relations. Functions. Products. Sums 2 / 40 Mathematical Background Outline Sets Relations Functions Products Sums 2 / 40 Outline Sets Relations Functions Products Sums 3 / 40 Sets Basic Notations x S membership S T subset S T proper subset S fin

More information

Vibrating Strings and Heat Flow

Vibrating Strings and Heat Flow Vibrating Strings and Heat Flow Consider an infinite vibrating string Assume that the -ais is the equilibrium position of the string and that the tension in the string at rest in equilibrium is τ Let u(,

More information

The Expansion of the Confluent Hypergeometric Function on the Positive Real Axis

The Expansion of the Confluent Hypergeometric Function on the Positive Real Axis Applied Mathematical Sciences, Vol. 12, 2018, no. 1, 19-26 HIKARI Ltd, www.m-hikari.com https://doi.org/10.12988/ams.2018.712351 The Expansion of the Confluent Hypergeometric Function on the Positive Real

More information

Lecture 2. x if x X B n f(x) = α(x) if x S n 1 D n

Lecture 2. x if x X B n f(x) = α(x) if x S n 1 D n Lecture 2 1.10 Cell attachments Let X be a topological space and α : S n 1 X be a map. Consider the space X D n with the disjoint union topology. Consider further the set X B n and a function f : X D n

More information

We start with a simple result from Fourier analysis. Given a function f : [0, 1] C, we define the Fourier coefficients of f by

We start with a simple result from Fourier analysis. Given a function f : [0, 1] C, we define the Fourier coefficients of f by Chapter 9 The functional equation for the Riemann zeta function We will eventually deduce a functional equation, relating ζ(s to ζ( s. There are various methods to derive this functional equation, see

More information

MATH 251 Final Examination August 14, 2015 FORM A. Name: Student Number: Section:

MATH 251 Final Examination August 14, 2015 FORM A. Name: Student Number: Section: MATH 251 Final Examination August 14, 2015 FORM A Name: Student Number: Section: This exam has 11 questions for a total of 150 points. Show all your work! In order to obtain full credit for partial credit

More information

LTI system response. Daniele Carnevale. Dipartimento di Ing. Civile ed Ing. Informatica (DICII), University of Rome Tor Vergata

LTI system response. Daniele Carnevale. Dipartimento di Ing. Civile ed Ing. Informatica (DICII), University of Rome Tor Vergata LTI system response Daniele Carnevale Dipartimento di Ing. Civile ed Ing. Informatica (DICII), University of Rome Tor Vergata Fondamenti di Automatica e Controlli Automatici A.A. 2014-2015 1 / 15 Laplace

More information

Ordinal symbolic dynamics

Ordinal symbolic dynamics Ordinal symbolic dynamics Karsten Keller and Mathieu Sinn Mathematical Institute, Wallstraße 40, 23560 Lübeck, Germany Abstract Ordinal time series analysis is a new approach to the investigation of long

More information

STAT215: Solutions for Homework 1

STAT215: Solutions for Homework 1 STAT25: Solutions for Homework Due: Wednesday, Jan 30. (0 pt) For X Be(α, β), Evaluate E[X a ( X) b ] for all real numbers a and b. For which a, b is it finite? (b) What is the MGF M log X (t) for the

More information

Waves and the Schroedinger Equation

Waves and the Schroedinger Equation Waves and the Schroedinger Equation 5 april 010 1 The Wave Equation We have seen from previous discussions that the wave-particle duality of matter requires we describe entities through some wave-form

More information

UNIVERSITY OF LONDON IMPERIAL COLLEGE LONDON

UNIVERSITY OF LONDON IMPERIAL COLLEGE LONDON UNIVERSITY OF LONDON IMPERIAL COLLEGE LONDON BSc and MSci EXAMINATIONS (MATHEMATICS) MAY JUNE 23 This paper is also taken for the relevant examination for the Associateship. M3S4/M4S4 (SOLUTIONS) APPLIED

More information

Introduction to Complex Analysis MSO 202 A

Introduction to Complex Analysis MSO 202 A Introduction to Complex Analysis MSO 202 A Sameer Chavan Semester I, 2016-17 Course Structure This course will be conducted in Flipped Classroom Mode. Every Friday evening, 3 to 7 videos of total duration

More information

CIMPA Summer School on Current Research on Finite Element Method Lecture 1. IIT Bombay. Introduction to feedback stabilization

CIMPA Summer School on Current Research on Finite Element Method Lecture 1. IIT Bombay. Introduction to feedback stabilization 1/51 CIMPA Summer School on Current Research on Finite Element Method Lecture 1 IIT Bombay July 6 July 17, 215 Introduction to feedback stabilization Jean-Pierre Raymond Institut de Mathématiques de Toulouse

More information

Data-Generating process

Data-Generating process Notes on Recursive Models December 15, 2009; last revision July 16, 2010 Backus, Chernov & Zin Data-Generating process We represent a joint data-generating process for consumption growth (log g and variance

More information

Final Exam - MATH 630: Solutions

Final Exam - MATH 630: Solutions Final Exam - MATH 630: Solutions Problem. Find all x R satisfying e xeix e ix. Solution. Comparing the moduli of both parts, we obtain e x cos x, and therefore, x cos x 0, which is possible only if x 0

More information

Solutions for Math 411 Assignment #10 1

Solutions for Math 411 Assignment #10 1 Solutions for Math 4 Assignment # AA. Compute the following integrals: a) + sin θ dθ cos x b) + x dx 4 Solution of a). Let z = e iθ. By the substitution = z + z ), sin θ = i z z ) and dθ = iz dz and Residue

More information

PROBABILITY: LIMIT THEOREMS II, SPRING HOMEWORK PROBLEMS

PROBABILITY: LIMIT THEOREMS II, SPRING HOMEWORK PROBLEMS PROBABILITY: LIMIT THEOREMS II, SPRING 218. HOMEWORK PROBLEMS PROF. YURI BAKHTIN Instructions. You are allowed to work on solutions in groups, but you are required to write up solutions on your own. Please

More information

Introduction to Orientifolds.

Introduction to Orientifolds. Introduction to Orientifolds http://www.physto.se/~mberg Overview Orientability in Quantum Field Theory: spinors S R(2π) ψ = ψ Orientability in Quantum Field Theory: spinors (S R(2π) ) 2 ψ =+ ψ S R(2π)

More information

Lattice sums arising from the Poisson equation

Lattice sums arising from the Poisson equation Lattice sums arising from the Poisson equation D H Bailey 1, J M Borwein, R E Crandall 3 1947-01), I J Zucker 4 1 Lawrence Berkeley National Lab, Berkeley, CA 9470; University of California, Davis, Department

More information

Spatial Statistics with Image Analysis. Lecture L08. Computer exercise 3. Lecture 8. Johan Lindström. November 25, 2016

Spatial Statistics with Image Analysis. Lecture L08. Computer exercise 3. Lecture 8. Johan Lindström. November 25, 2016 C3 Repetition Creating Q Spectral Non-grid Spatial Statistics with Image Analysis Lecture 8 Johan Lindström November 25, 216 Johan Lindström - johanl@maths.lth.se FMSN2/MASM25L8 1/39 Lecture L8 C3 Repetition

More information

Calculus of variations - Lecture 11

Calculus of variations - Lecture 11 Calculus of variations - Lecture 11 1 Introduction It is easiest to formulate the calculus of variations problem with a specific example. The classical problem of the brachistochrone (1696 Johann Bernoulli)

More information

Differential equations

Differential equations Differential equations Math 7 Spring Practice problems for April Exam Problem Use the method of elimination to find the x-component of the general solution of x y = 6x 9x + y = x 6y 9y Soln: The system

More information

Piecewise Linear Continuous Approximations and Filtering for DSGE Models with Occasionally-Binding Constraints

Piecewise Linear Continuous Approximations and Filtering for DSGE Models with Occasionally-Binding Constraints The views expressed in this presentation are those of the authors and do not necessarily reflect those of the Board of Governors or the Federal Reserve System. Piecewise Linear Continuous Approximations

More information

EE 261 The Fourier Transform and its Applications Fall 2006 Final Exam Solutions. Notes: There are 7 questions for a total of 120 points

EE 261 The Fourier Transform and its Applications Fall 2006 Final Exam Solutions. Notes: There are 7 questions for a total of 120 points EE 6 The Fourier Transform and its Applications Fall 6 Final Exam Solutions Notes: There are 7 questions for a total of points Write all your answers in your exam booklets When there are several parts

More information

X i, AX i X i. (A λ) k x = 0.

X i, AX i X i. (A λ) k x = 0. Chapter 4 Spectral Theory In the previous chapter, we studied spacial operators: the self-adjoint operator and normal operators. In this chapter, we study a general linear map A that maps a finite dimensional

More information

The problem is to infer on the underlying probability distribution that gives rise to the data S.

The problem is to infer on the underlying probability distribution that gives rise to the data S. Basic Problem of Statistical Inference Assume that we have a set of observations S = { x 1, x 2,..., x N }, xj R n. The problem is to infer on the underlying probability distribution that gives rise to

More information

The first order quasi-linear PDEs

The first order quasi-linear PDEs Chapter 2 The first order quasi-linear PDEs The first order quasi-linear PDEs have the following general form: F (x, u, Du) = 0, (2.1) where x = (x 1, x 2,, x 3 ) R n, u = u(x), Du is the gradient of u.

More information

Math General Topology Fall 2012 Homework 6 Solutions

Math General Topology Fall 2012 Homework 6 Solutions Math 535 - General Topology Fall 202 Homework 6 Solutions Problem. Let F be the field R or C of real or complex numbers. Let n and denote by F[x, x 2,..., x n ] the set of all polynomials in n variables

More information

Weak convergence and averaging for ODE

Weak convergence and averaging for ODE Weak convergence and averaging for ODE Lawrence C. Evans and Te Zhang Department of Mathematics University of California, Berkeley Abstract This mostly expository paper shows how weak convergence methods

More information

Solution: Homework 3 Biomedical Signal, Systems and Control (BME )

Solution: Homework 3 Biomedical Signal, Systems and Control (BME ) Solution: Homework Biomedical Signal, Systems and Control (BME 80.) Instructor: René Vidal, E-mail: rvidal@cis.jhu.edu TA: Donavan Cheng, E-mail: donavan.cheng@gmail.com TA: Ertan Cetingul, E-mail: ertan@cis.jhu.edu

More information

UNIVERSITY OF MANITOBA

UNIVERSITY OF MANITOBA Question Points Score INSTRUCTIONS TO STUDENTS: This is a 6 hour examination. No extra time will be given. No texts, notes, or other aids are permitted. There are no calculators, cellphones or electronic

More information

Riemann-Lebesgue Lemma. Some pictures.

Riemann-Lebesgue Lemma. Some pictures. Riemann-Lebesgue Lemma. Some pictures. Definition Let f be a differentiable, complex-valued function on R such that f t) dt is convergent. For such a function define f λ) = the Fourier Transform of f.

More information

Math 113 Homework 5 Solutions (Starred problems) Solutions by Guanyang Wang, with edits by Tom Church.

Math 113 Homework 5 Solutions (Starred problems) Solutions by Guanyang Wang, with edits by Tom Church. Math 113 Homework 5 Solutions (Starred problems) Solutions by Guanyang Wang, with edits by Tom Church. Exercise 5.C.1 Suppose T L(V ) is diagonalizable. Prove that V = null T range T. Proof. Let v 1,...,

More information

On the consistent discretization in time of nonlinear thermo-elastodynamics

On the consistent discretization in time of nonlinear thermo-elastodynamics of nonlinear thermo-elastodynamics Chair of Computational Mechanics University of Siegen GAMM Annual Meeting, 3.03.00 0000 0000 Thermodynamic double pendulum Structure preserving integrators Great interest

More information

With this expanded version of what we mean by a solution to an equation we can solve equations that previously had no solution.

With this expanded version of what we mean by a solution to an equation we can solve equations that previously had no solution. M 74 An introduction to Complex Numbers. 1 Solving equations Throughout the calculus sequence we have limited our discussion to real valued solutions to equations. We know the equation x 1 = 0 has distinct

More information

Chapter 1, Exercise 22

Chapter 1, Exercise 22 Chapter, Exercise 22 Let N = {,2,3,...} denote the set of positive integers. A subset S N is said to be in arithmetic progression if S = {a,a+d,a+2d,a+3d,...} where a,d N. Here d is called the step of

More information

CONSECUTIVE PRIMES AND BEATTY SEQUENCES

CONSECUTIVE PRIMES AND BEATTY SEQUENCES CONSECUTIVE PRIMES AND BEATTY SEQUENCES WILLIAM D. BANKS AND VICTOR Z. GUO Abstract. Fix irrational numbers α, ˆα > 1 of finite type and real numbers β, ˆβ 0, and let B and ˆB be the Beatty sequences B.=

More information