ÒÄÆÉÖÌÄ. ÐÄÒÉÏÃÖËÉ ÀÌÏÝÀÍÉÓ x = f(t, x), x(0) = x(1) ÀÌÏáÓÍÀÃÏÁÉÓ ÊÀÒÂÀÃ ÝÍÏÁÉËÉ
|
|
- Charles Moore
- 5 years ago
- Views:
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 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 informationG 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 informationDiscontinuous 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 informatione 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 informationMA 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 informationIntroduction 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 informationSplitting 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 informationComplex 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 informationMathematics 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 informationVerona 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 informationChapter 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 informationEE 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 informationBANDLIMITED 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 informationComputing 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 informationMultiple 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 information1 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 informationDIFFERENTIAL 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 informationHandout 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 informationComplex 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 informationBoundary 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 informationPart 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 informationCalculus 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 informationDifferentiability 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 informationApplications 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 informationPAPER 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 informationOptimal 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 informationOptimal 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 informationINTRODUCTION 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 informationComplex 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 informationFrom 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 information7.2. 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 informationAPPM 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 informationArc 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 informationEXISTENCE 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 informationSimplified 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 informationSecond 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 informationRunge-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 informationRates 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 informationEE 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 informationControl 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 informationJoint work with Nguyen Hoang (Univ. Concepción, Chile) Padova, Italy, May 2018
EXTENDED EULER-LAGRANGE AND HAMILTONIAN CONDITIONS IN OPTIMAL CONTROL OF SWEEPING PROCESSES WITH CONTROLLED MOVING SETS BORIS MORDUKHOVICH Wayne State University Talk given at the conference Optimization,
More informationSurface 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 information1. 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 informationDistributed 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 informationGroup 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 informationEXAMPLE 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 informationSection 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 informationChapter 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 informationLecture 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 informationECEN 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 informationPart 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 informationCalculus 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 informationCalculus 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 informationInfinite-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 informationf(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 informationThe 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 informationSYNCHRONIZATION 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 information1 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 informationProf. 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 informationCalculus 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 informationMath 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 information2905 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 informationOrder 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 informationOutline. 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 informationVibrating 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 informationThe 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 informationLecture 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 informationWe 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 informationMATH 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 informationLTI 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 informationOrdinal 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 informationSTAT215: 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 informationWaves 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 informationUNIVERSITY 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 informationIntroduction 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 informationCIMPA 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 informationData-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 informationFinal 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 informationSolutions 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 informationPROBABILITY: LIMIT THEOREMS II, SPRING HOMEWORK PROBLEMS
PROBABILITY: LIMIT THEOREMS II, SPRING 218. HOMEWORK PROBLEMS PROF. YURI BAKHTIN Instructions. You are allowed to work on solutions in groups, but you are required to write up solutions on your own. Please
More informationIntroduction 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 informationLattice 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 informationSpatial 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 informationCalculus 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 informationDifferential 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 informationPiecewise 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 informationEE 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 informationX 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 informationThe 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 informationThe 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 informationMath 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 informationWeak 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 informationSolution: 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 informationUNIVERSITY OF MANITOBA
Question Points Score INSTRUCTIONS TO STUDENTS: This is a 6 hour examination. No extra time will be given. No texts, notes, or other aids are permitted. There are no calculators, cellphones or electronic
More informationRiemann-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 informationMath 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 informationOn 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 informationWith 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 informationChapter 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 informationCONSECUTIVE 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