Ordinary Differential Equations
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Ordinary Differential Equation 2
Unit - II Linear Systems- Let us consider a system of first order differential equations of the form dx = F(,)x y dt (1) dy = G(,)x y dt Where t is an independent variable. And x & y are dependent variables. The system (1) is called a linear system if both F(x, y) and G(x, y) are linear in x and y. dx = a ()t x + b ()t y + f() t dt 1 1 1 Also system (1) can be written as (2) dy = a ()t x + b ()t y + f() t dt 2 2 2 ∀ = [ ] Where ai t)( , bi t)( and fi () t i 2,1 are continuous functions on a, b . Homogeneous and Non-Homogeneous Linear Systems- The system (2) is called a homogeneous linear system, if both f1 ( t )and f2 ( t ) are identically zero and if both f1 ( t )and f2 t)( are not equal to zero, then the system (2) is called a non-homogeneous linear system. x = x() t Solution- A pair of functions defined on [a, b]is said to be a solution of (2) if it satisfies y = y() t (2). dx = 4x − y ........ A dt Example- (3) dy = 2x + y ........ B dt dx d 2 x dx From A, y = 4x − putting in B we obtain − 5 + 6x = 0is a 2 nd order differential dt dt 2 dt x = e2t equation. The auxiliary equation is m2 − 5m + 6 = 0 ⇒ m = 3,2 so putting x = e2t in A, we x = e3t obtain y = 2e2t again putting x = e3t in A, we obtain y = e3t . -
Parametrizations of K-Nonnegative Matrices
Parametrizations of k-Nonnegative Matrices Anna Brosowsky, Neeraja Kulkarni, Alex Mason, Joe Suk, Ewin Tang∗ October 2, 2017 Abstract Totally nonnegative (positive) matrices are matrices whose minors are all nonnegative (positive). We generalize the notion of total nonnegativity, as follows. A k-nonnegative (resp. k-positive) matrix has all minors of size k or less nonnegative (resp. positive). We give a generating set for the semigroup of k-nonnegative matrices, as well as relations for certain special cases, i.e. the k = n − 1 and k = n − 2 unitriangular cases. In the above two cases, we find that the set of k-nonnegative matrices can be partitioned into cells, analogous to the Bruhat cells of totally nonnegative matrices, based on their factorizations into generators. We will show that these cells, like the Bruhat cells, are homeomorphic to open balls, and we prove some results about the topological structure of the closure of these cells, and in fact, in the latter case, the cells form a Bruhat-like CW complex. We also give a family of minimal k-positivity tests which form sub-cluster algebras of the total positivity test cluster algebra. We describe ways to jump between these tests, and give an alternate description of some tests as double wiring diagrams. 1 Introduction A totally nonnegative (respectively totally positive) matrix is a matrix whose minors are all nonnegative (respectively positive). Total positivity and nonnegativity are well-studied phenomena and arise in areas such as planar networks, combinatorics, dynamics, statistics and probability. The study of total positivity and total nonnegativity admit many varied applications, some of which are explored in “Totally Nonnegative Matrices” by Fallat and Johnson [5]. -
Phase Plane Diagrams of Difference Equations
PHASE PLANE DIAGRAMS OF DIFFERENCE EQUATIONS TANYA DEWLAND, JEROME WESTON, AND RACHEL WEYRENS Abstract. We will be determining qualitative features of a dis- crete dynamical system of homogeneous difference equations with constant coefficients. By creating phase plane diagrams of our system we can visualize these features, such as convergence, equi- librium points, and stability. 1. Introduction Continuous systems are often approximated as discrete processes, mean- ing that we look only at the solutions for positive integer inputs. Dif- ference equations are recurrence relations, and first order difference equations only depend on the previous value. Using difference equa- tions, we can model discrete dynamical systems. The observations we can determine, by analyzing phase plane diagrams of difference equa- tions, are if we are modeling decay or growth, convergence, stability, and equilibrium points. For this paper, we will only consider two dimensional systems. Suppose x(k) and y(k) are two functions of the positive integers that satisfy the following system of difference equations: x(k + 1) = ax(k) + by(k) y(k + 1) = cx(k) + dy(k): This system is more simply written in matrix from z(k + 1) = Az(k); x(k) a b where z(k) = is a column vector and A = . If z(0) = z y(k) c d 0 is the initial condition then the solution to the initial value prob- lem z(k + 1) = Az(k); z(0) = z0; 1 2 TANYA DEWLAND, JEROME WESTON, AND RACHEL WEYRENS is k z(k) = A z0: Each z(k) is represented as a point (x(k); y(k)) in the Euclidean plane. -
Differential Equations We Will Use “Dsolve” to Get an Analytical Solution to the DE Y’(T) = 2Ty(T)
Differential Equations - Theory and Applications - Version: Fall 2017 Andr´asDomokos , PhD California State University, Sacramento Contents Chapter 0. Introduction 3 Chapter 1. Calculus review. Differentiation and integration rules. 4 1.1. Derivatives 4 1.2. Antiderivatives and Indefinite Integrals 7 1.3. Definite Integrals 11 Chapter 2. Introduction to Differential Equations 15 2.1. Definitions 15 2.2. Initial value problems 20 2.3. Classifications of DEs 23 2.4. Examples of DEs modelling real-life phenomena 25 Chapter 3. First order differential equations solvable by analytical methods 27 3.1. Differential equations with separable variables 27 3.2. First order linear differential equations 31 3.3. Bernoulli's differential equations 36 3.4. Non-linear homogeneous differential equations 38 3.5. Differential equations of the form y0(t) = f(at + by(t) + c). 40 3.6. Second order differential equations reducible to first order differential equations 42 Chapter 4. General theory of differential equations of first order 45 4.1. Slope fields (or direction fields) 45 4.1.1. Autonomous first order differential equations. 49 4.2. Existence and uniqueness of solutions for initial value problems 53 4.3. The method of successive approximations 59 4.4. Numerical methods for Differential equations 62 4.4.1. The Euler's method 62 4.4.2. The improved Euler (or Heun) method 67 4.4.3. The fourth order Runge-Kutta method 68 4.4.4. NDSolve command in Mathematica 71 Chapter 5. Higher order linear differential equations 75 5.1. General theory 75 5.2. Linear and homogeneous DEs with constant coefficients 78 5.3. -
Section 2.4–2.5 Partitioned Matrices and LU Factorization
Section 2.4{2.5 Partitioned Matrices and LU Factorization Gexin Yu [email protected] College of William and Mary Gexin Yu [email protected] Section 2.4{2.5 Partitioned Matrices and LU Factorization One approach to simplify the computation is to partition a matrix into blocks. 2 3 0 −1 5 9 −2 3 Ex: A = 4 −5 2 4 0 −3 1 5. −8 −6 3 1 7 −4 This partition can also be written as the following 2 × 3 block matrix: A A A A = 11 12 13 A21 A22 A23 3 0 −1 In the block form, we have blocks A = and so on. 11 −5 2 4 partition matrices into blocks In real world problems, systems can have huge numbers of equations and un-knowns. Standard computation techniques are inefficient in such cases, so we need to develop techniques which exploit the internal structure of the matrices. In most cases, the matrices of interest have lots of zeros. Gexin Yu [email protected] Section 2.4{2.5 Partitioned Matrices and LU Factorization 2 3 0 −1 5 9 −2 3 Ex: A = 4 −5 2 4 0 −3 1 5. −8 −6 3 1 7 −4 This partition can also be written as the following 2 × 3 block matrix: A A A A = 11 12 13 A21 A22 A23 3 0 −1 In the block form, we have blocks A = and so on. 11 −5 2 4 partition matrices into blocks In real world problems, systems can have huge numbers of equations and un-knowns. -
Phase Plane Methods
Chapter 10 Phase Plane Methods Contents 10.1 Planar Autonomous Systems . 680 10.2 Planar Constant Linear Systems . 694 10.3 Planar Almost Linear Systems . 705 10.4 Biological Models . 715 10.5 Mechanical Models . 730 Studied here are planar autonomous systems of differential equations. The topics: Planar Autonomous Systems: Phase Portraits, Stability. Planar Constant Linear Systems: Classification of isolated equilib- ria, Phase portraits. Planar Almost Linear Systems: Phase portraits, Nonlinear classi- fications of equilibria. Biological Models: Predator-prey models, Competition models, Survival of one species, Co-existence, Alligators, doomsday and extinction. Mechanical Models: Nonlinear spring-mass system, Soft and hard springs, Energy conservation, Phase plane and scenes. 680 Phase Plane Methods 10.1 Planar Autonomous Systems A set of two scalar differential equations of the form x0(t) = f(x(t); y(t)); (1) y0(t) = g(x(t); y(t)): is called a planar autonomous system. The term autonomous means self-governing, justified by the absence of the time variable t in the functions f(x; y), g(x; y). ! ! x(t) f(x; y) To obtain the vector form, let ~u(t) = , F~ (x; y) = y(t) g(x; y) and write (1) as the first order vector-matrix system d (2) ~u(t) = F~ (~u(t)): dt It is assumed that f, g are continuously differentiable in some region D in the xy-plane. This assumption makes F~ continuously differentiable in D and guarantees that Picard's existence-uniqueness theorem for initial d ~ value problems applies to the initial value problem dt ~u(t) = F (~u(t)), ~u(0) = ~u0. -
Calculus and Differential Equations II
Calculus and Differential Equations II MATH 250 B Linear systems of differential equations Linear systems of differential equations Calculus and Differential Equations II Second order autonomous linear systems We are mostly interested with2 × 2 first order autonomous systems of the form x0 = a x + b y y 0 = c x + d y where x and y are functions of t and a, b, c, and d are real constants. Such a system may be re-written in matrix form as d x x a b = M ; M = : dt y y c d The purpose of this section is to classify the dynamics of the solutions of the above system, in terms of the properties of the matrix M. Linear systems of differential equations Calculus and Differential Equations II Existence and uniqueness (general statement) Consider a linear system of the form dY = M(t)Y + F (t); dt where Y and F (t) are n × 1 column vectors, and M(t) is an n × n matrix whose entries may depend on t. Existence and uniqueness theorem: If the entries of the matrix M(t) and of the vector F (t) are continuous on some open interval I containing t0, then the initial value problem dY = M(t)Y + F (t); Y (t ) = Y dt 0 0 has a unique solution on I . In particular, this means that trajectories in the phase space do not cross. Linear systems of differential equations Calculus and Differential Equations II General solution The general solution to Y 0 = M(t)Y + F (t) reads Y (t) = C1 Y1(t) + C2 Y2(t) + ··· + Cn Yn(t) + Yp(t); = U(t) C + Yp(t); where 0 Yp(t) is a particular solution to Y = M(t)Y + F (t). -
Reverse Mathematics & Nonstandard Analysis
CORE Metadata, citation and similar papers at core.ac.uk Provided by Ghent University Academic Bibliography REVERSE MATHEMATICS & NONSTANDARD ANALYSIS: TOWARDS A DISPENSABILITY ARGUMENT SAM SANDERS Abstract. Reverse Mathematics is a program in the foundations of math- ematics initiated by Harvey Friedman ([8, 9]) and developed extensively by Stephen Simpson ([19]). Its aim is to determine which minimal axioms prove theorems of ordinary mathematics. Nonstandard Analysis plays an impor- tant role in this program ([14, 25]). We consider Reverse Mathematics where equality is replaced by the predicate ≈, i.e. equality up to infinitesimals from Nonstandard Analysis. This context allows us to model mathematical practice in Physics particularly well. In this way, our mathematical results have impli- cations for Ontology and the Philosophy of Science. In particular, we prove the dispensability argument, which states that the very nature of Mathematics in Physics implies that real numbers are not essential (i.e. dispensable) for Physics (cf. the Quine-Putnam indispensability argument). There are good reasons to believe that nonstandard analysis, in some version or another, will be the analysis of the future. Kurt G¨odel 1. Introducing Reverse Mathematics 1.1. Classical Reverse Mathematics. Reverse Mathematics is a program in Foundations of Mathematics founded around 1975 by Harvey Friedman ([8] and [9]) and developed intensely by Stephen Simpson and others; for an overview of the subject, see [19] and [20]. The goal of Reverse Mathematics is to determine what minimal axiom system is needed to prove a particular theorem from ordi- nary mathematics. By now, it is well-known that large portions of mathematics (especially so in analysis) can be carried out in systems far weaker than ZFC, the `usual' background theory for mathematics. -
TWO DIMENSIONAL FLOWS Lecture 4: Linear and Nonlinear Systems
TWO DIMENSIONAL FLOWS Lecture 4: Linear and Nonlinear Systems 4. Linear and Nonlinear Systems in 2D In higher dimensions, trajectories have more room to manoeuvre, and hence a wider range of behaviour is possible. 4.1 Linear systems: definitions and examples A 2-dimensional linear system has the form x˙ = ax + by y˙ = cx + dy where a, b, c, d are parameters. Equivalently, in vector notation x˙ = Ax (1) where a b x A = and x = (2) c d ! y ! The Linear property means that if x1 and x2 are solutions, then so is c1x1 + c2x2 for any c1 and c2. The solutions ofx ˙ = Ax can be visualized as trajectories moving on the (x,y) plane, or phase plane. 1 Example 4.1.1 mx¨ + kx = 0 i.e. the simple harmonic oscillator Fig. 4.1.1 The state of the system is characterized by x and v =x ˙ x˙ = v k v˙ = x −m i.e. for each (x,v) we obtain a vector (˙x, v˙) ⇒ vector field on the phase plane. 2 As for a 1-dimensional system, we imagine a fluid flowing steadily on the phase plane with a local velocity given by (˙x, v˙) = (v, ω2x). − Fig. 4.1.2 Trajectory is found by placing an imag- • inary particle or phase point at (x0,v0) and watching how it moves. (x,v) = (0, 0) is a fixed point: • static equilibrium! Trajectories form closed orbits around (0, 0): • oscillations! 3 The phase portrait looks like... Fig. 4.1.3 NB ω2x2 + v2 is constant on each ellipse. -
Math 217: True False Practice Professor Karen Smith 1. a Square Matrix Is Invertible If and Only If Zero Is Not an Eigenvalue. Solution Note: True
(c)2015 UM Math Dept licensed under a Creative Commons By-NC-SA 4.0 International License. Math 217: True False Practice Professor Karen Smith 1. A square matrix is invertible if and only if zero is not an eigenvalue. Solution note: True. Zero is an eigenvalue means that there is a non-zero element in the kernel. For a square matrix, being invertible is the same as having kernel zero. 2. If A and B are 2 × 2 matrices, both with eigenvalue 5, then AB also has eigenvalue 5. Solution note: False. This is silly. Let A = B = 5I2. Then the eigenvalues of AB are 25. 3. If A and B are 2 × 2 matrices, both with eigenvalue 5, then A + B also has eigenvalue 5. Solution note: False. This is silly. Let A = B = 5I2. Then the eigenvalues of A + B are 10. 4. A square matrix has determinant zero if and only if zero is an eigenvalue. Solution note: True. Both conditions are the same as the kernel being non-zero. 5. If B is the B-matrix of some linear transformation V !T V . Then for all ~v 2 V , we have B[~v]B = [T (~v)]B. Solution note: True. This is the definition of B-matrix. 21 2 33 T 6. Suppose 40 2 05 is the matrix of a transformation V ! V with respect to some basis 0 0 1 B = (f1; f2; f3). Then f1 is an eigenvector. Solution note: True. It has eigenvalue 1. The first column of the B-matrix is telling us that T (f1) = f1. -
Homogeneous Systems (1.5) Linear Independence and Dependence (1.7)
Math 20F, 2015SS1 / TA: Jor-el Briones / Sec: A01 / Handout Page 1 of3 Homogeneous systems (1.5) Homogenous systems are linear systems in the form Ax = 0, where 0 is the 0 vector. Given a system Ax = b, suppose x = α + t1α1 + t2α2 + ::: + tkαk is a solution (in parametric form) to the system, for any values of t1; t2; :::; tk. Then α is a solution to the system Ax = b (seen by seeting t1 = ::: = tk = 0), and α1; α2; :::; αk are solutions to the homoegeneous system Ax = 0. Terms you should know The zero solution (trivial solution): The zero solution is the 0 vector (a vector with all entries being 0), which is always a solution to the homogeneous system Particular solution: Given a system Ax = b, suppose x = α + t1α1 + t2α2 + ::: + tkαk is a solution (in parametric form) to the system, α is the particular solution to the system. The other terms constitute the solution to the associated homogeneous system Ax = 0. Properties of a Homegeneous System 1. A homogeneous system is ALWAYS consistent, since the zero solution, aka the trivial solution, is always a solution to that system. 2. A homogeneous system with at least one free variable has infinitely many solutions. 3. A homogeneous system with more unknowns than equations has infinitely many solu- tions 4. If α1; α2; :::; αk are solutions to a homogeneous system, then ANY linear combination of α1; α2; :::; αk is also a solution to the homogeneous system Important theorems to know Theorem. (Chapter 1, Theorem 6) Given a consistent system Ax = b, suppose α is a solution to the system. -
Me Me Ft-Uiowa-Math2550 Assignment Hw6fall14 Due 10/09/2014 at 11:59Pm CDT
me me ft-uiowa-math2550 Assignment HW6fall14 due 10/09/2014 at 11:59pm CDT The columns of A could be either linearly dependent or lin- 1. (1 pt) Library/WHFreeman/Holt linear algebra/Chaps 1-4- early independent depending on the case. /2.3.40.pg Correct Answers: • B n Let S be a set of m vectors in R with m > n. Select the best statement. 3. (1 pt) Library/WHFreeman/Holt linear algebra/Chaps 1-4- /2.3.42.pg • A. The set S is linearly independent, as long as it does Let A be a matrix with more columns than rows. not include the zero vector. Select the best statement. • B. The set S is linearly dependent. • C. The set S is linearly independent, as long as no vec- • A. The columns of A are linearly independent, as long tor in S is a scalar multiple of another vector in the set. as they does not include the zero vector. • D. The set S is linearly independent. • B. The columns of A could be either linearly dependent • E. The set S could be either linearly dependent or lin- or linearly independent depending on the case. early independent, depending on the case. • C. The columns of A must be linearly dependent. • F. none of the above • D. The columns of A are linearly independent, as long Solution: (Instructor solution preview: show the student so- as no column is a scalar multiple of another column in lution after due date. ) A • E. none of the above SOLUTION Solution: (Instructor solution preview: show the student so- By theorem 2.13, a linearly independent set in n can contain R lution after due date.