Canonical Transformations and Squeezing Formalism in Cosmology
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1 the Basic Set-Up 2 Poisson Brackets
MATHEMATICS 7302 (Analytical Dynamics) YEAR 2016–2017, TERM 2 HANDOUT #12: THE HAMILTONIAN APPROACH TO MECHANICS These notes are intended to be read as a supplement to the handout from Gregory, Classical Mechanics, Chapter 14. 1 The basic set-up I assume that you have already studied Gregory, Sections 14.1–14.4. The following is intended only as a succinct summary. We are considering a system whose equations of motion are written in Hamiltonian form. This means that: 1. The phase space of the system is parametrized by canonical coordinates q =(q1,...,qn) and p =(p1,...,pn). 2. We are given a Hamiltonian function H(q, p, t). 3. The dynamics of the system is given by Hamilton’s equations of motion ∂H q˙i = (1a) ∂pi ∂H p˙i = − (1b) ∂qi for i =1,...,n. In these notes we will consider some deeper aspects of Hamiltonian dynamics. 2 Poisson brackets Let us start by considering an arbitrary function f(q, p, t). Then its time evolution is given by n df ∂f ∂f ∂f = q˙ + p˙ + (2a) dt ∂q i ∂p i ∂t i=1 i i X n ∂f ∂H ∂f ∂H ∂f = − + (2b) ∂q ∂p ∂p ∂q ∂t i=1 i i i i X 1 where the first equality used the definition of total time derivative together with the chain rule, and the second equality used Hamilton’s equations of motion. The formula (2b) suggests that we make a more general definition. Let f(q, p, t) and g(q, p, t) be any two functions; we then define their Poisson bracket {f,g} to be n def ∂f ∂g ∂f ∂g {f,g} = − . -
Time-Dependent Hamiltonian Mechanics on a Locally Conformal
Time-dependent Hamiltonian mechanics on a locally conformal symplectic manifold Orlando Ragnisco†, Cristina Sardón∗, Marcin Zając∗∗ Department of Mathematics and Physics†, Universita degli studi Roma Tre, Largo S. Leonardo Murialdo, 1, 00146 , Rome, Italy. ragnisco@fis.uniroma3.it Department of Applied Mathematics∗, Universidad Polit´ecnica de Madrid. C/ Jos´eGuti´errez Abascal, 2, 28006, Madrid. Spain. [email protected] Department of Mathematical Methods in Physics∗∗, Faculty of Physics. University of Warsaw, ul. Pasteura 5, 02-093 Warsaw, Poland. [email protected] Abstract In this paper we aim at presenting a concise but also comprehensive study of time-dependent (t- dependent) Hamiltonian dynamics on a locally conformal symplectic (lcs) manifold. We present a generalized geometric theory of canonical transformations and formulate a time-dependent geometric Hamilton-Jacobi theory on lcs manifolds. In contrast to previous papers concerning locally conformal symplectic manifolds, here the introduction of the time dependency brings out interesting geometric properties, as it is the introduction of contact geometry in locally symplectic patches. To conclude, we show examples of the applications of our formalism, in particular, we present systems of differential equations with time-dependent parameters, which admit different physical interpretations as we shall point out. arXiv:2104.02636v1 [math-ph] 6 Apr 2021 Contents 1 Introduction 2 2 Fundamentals on time-dependent Hamiltonian systems 5 2.1 Time-dependentsystems. ....... 5 2.2 Canonicaltransformations . ......... 6 2.3 Generating functions of canonical transformations . ................ 8 1 3 Geometry of locally conformal symplectic manifolds 8 3.1 Basics on locally conformal symplectic manifolds . ............... 8 3.2 Locally conformal symplectic structures on cotangent bundles............ -
Perturbation Theory and Exact Solutions
PERTURBATION THEORY AND EXACT SOLUTIONS by J J. LODDER R|nhtdnn Report 76~96 DISSIPATIVE MOTION PERTURBATION THEORY AND EXACT SOLUTIONS J J. LODOER ASSOCIATIE EURATOM-FOM Jun»»76 FOM-INST1TUUT VOOR PLASMAFYSICA RUNHUIZEN - JUTPHAAS - NEDERLAND DISSIPATIVE MOTION PERTURBATION THEORY AND EXACT SOLUTIONS by JJ LODDER R^nhuizen Report 76-95 Thisworkwat performed at part of th«r«Mvchprogmmncof thcHMCiattofiafrccmentof EnratoniOTd th« Stichting voor FundtmenteelOiutereoek der Matctk" (FOM) wtihnnmcWMppoft from the Nederhmdie Organiutic voor Zuiver Wetemchap- pcigk Onderzoek (ZWO) and Evntom It it abo pabHtfMd w a the* of Ac Univenrty of Utrecht CONTENTS page SUMMARY iii I. INTRODUCTION 1 II. GENERALIZED FUNCTIONS DEFINED ON DISCONTINUOUS TEST FUNC TIONS AND THEIR FOURIER, LAPLACE, AND HILBERT TRANSFORMS 1. Introduction 4 2. Discontinuous test functions 5 3. Differentiation 7 4. Powers of x. The partie finie 10 5. Fourier transforms 16 6. Laplace transforms 20 7. Hubert transforms 20 8. Dispersion relations 21 III. PERTURBATION THEORY 1. Introduction 24 2. Arbitrary potential, momentum coupling 24 3. Dissipative equation of motion 31 4. Expectation values 32 5. Matrix elements, transition probabilities 33 6. Harmonic oscillator 36 7. Classical mechanics and quantum corrections 36 8. Discussion of the Pu strength function 38 IV. EXACTLY SOLVABLE MODELS FOR DISSIPATIVE MOTION 1. Introduction 40 2. General quadratic Kami1tonians 41 3. Differential equations 46 4. Classical mechanics and quantum corrections 49 5. Equation of motion for observables 51 V. SPECIAL QUADRATIC HAMILTONIANS 1. Introduction 53 2. Hamiltcnians with coordinate coupling 53 3. Double coordinate coupled Hamiltonians 62 4. Symmetric Hamiltonians 63 i page VI. DISCUSSION 1. Introduction 66 ?. -
Chapter 8 Stability Theory
Chapter 8 Stability theory We discuss properties of solutions of a first order two dimensional system, and stability theory for a special class of linear systems. We denote the independent variable by ‘t’ in place of ‘x’, and x,y denote dependent variables. Let I ⊆ R be an interval, and Ω ⊆ R2 be a domain. Let us consider the system dx = F (t, x, y), dt (8.1) dy = G(t, x, y), dt where the functions are defined on I × Ω, and are locally Lipschitz w.r.t. variable (x, y) ∈ Ω. Definition 8.1 (Autonomous system) A system of ODE having the form (8.1) is called an autonomous system if the functions F (t, x, y) and G(t, x, y) are constant w.r.t. variable t. That is, dx = F (x, y), dt (8.2) dy = G(x, y), dt Definition 8.2 A point (x0, y0) ∈ Ω is said to be a critical point of the autonomous system (8.2) if F (x0, y0) = G(x0, y0) = 0. (8.3) A critical point is also called an equilibrium point, a rest point. Definition 8.3 Let (x(t), y(t)) be a solution of a two-dimensional (planar) autonomous system (8.2). The trace of (x(t), y(t)) as t varies is a curve in the plane. This curve is called trajectory. Remark 8.4 (On solutions of autonomous systems) (i) Two different solutions may represent the same trajectory. For, (1) If (x1(t), y1(t)) defined on an interval J is a solution of the autonomous system (8.2), then the pair of functions (x2(t), y2(t)) defined by (x2(t), y2(t)) := (x1(t − s), y1(t − s)), for t ∈ s + J (8.4) is a solution on interval s + J, for every arbitrary but fixed s ∈ R. -
Canonical Transformations (Lecture 4)
Canonical transformations (Lecture 4) January 26, 2016 61/441 Lecture outline We will introduce and discuss canonical transformations that conserve the Hamiltonian structure of equations of motion. Poisson brackets are used to verify that a given transformation is canonical. A practical way to devise canonical transformation is based on usage of generation functions. The motivation behind this study is to understand the freedom which we have in the choice of various sets of coordinates and momenta. Later we will use this freedom to select a convenient set of coordinates for description of partilcle's motion in an accelerator. 62/441 Introduction Within the Lagrangian approach we can choose the generalized coordinates as we please. We can start with a set of coordinates qi and then introduce generalized momenta pi according to Eqs. @L(qk ; q_k ; t) pi = ; i = 1;:::; n ; @q_i and form the Hamiltonian ! H = pi q_i - L(qk ; q_k ; t) : i X Or, we can chose another set of generalized coordinates Qi = Qi (qk ; t), express the Lagrangian as a function of Qi , and obtain a different set of momenta Pi and a different Hamiltonian 0 H (Qi ; Pi ; t). This type of transformation is called a point transformation. The two representations are physically equivalent and they describe the same dynamics of our physical system. 63/441 Introduction A more general approach to the problem of using various variables in Hamiltonian formulation of equations of motion is the following. Let us assume that we have canonical variables qi , pi and the corresponding Hamiltonian H(qi ; pi ; t) and then make a transformation to new variables Qi = Qi (qk ; pk ; t) ; Pi = Pi (qk ; pk ; t) : i = 1 ::: n: (4.1) 0 Can we find a new Hamiltonian H (Qi ; Pi ; t) such that the system motion in new variables satisfies Hamiltonian equations with H 0? What are requirements on the transformation (4.1) for such a Hamiltonian to exist? These questions lead us to canonical transformations. -
ATTRACTORS: STRANGE and OTHERWISE Attractor - in Mathematics, an Attractor Is a Region of Phase Space That "Attracts" All Nearby Points As Time Passes
ATTRACTORS: STRANGE AND OTHERWISE Attractor - In mathematics, an attractor is a region of phase space that "attracts" all nearby points as time passes. That is, the changing values have a trajectory which moves across the phase space toward the attractor, like a ball rolling down a hilly landscape toward the valley it is attracted to. PHASE SPACE - imagine a standard graph with an x-y axis; it is a phase space. We plot the position of an x-y variable on the graph as a point. That single point summarizes all the information about x and y. If the values of x and/or y change systematically a series of points will plot as a curve or trajectory moving across the phase space. Phase space turns numbers into pictures. There are as many phase space dimensions as there are variables. The strange attractor below has 3 dimensions. LIMIT CYCLE (OR PERIODIC) ATTRACTOR STRANGE (OR COMPLEX) ATTRACTOR A system which repeats itself exactly, continuously, like A strange (or chaotic) attractor is one in which the - + a clock pendulum (left) Position (right) trajectory of the points circle around a region of phase space, but never exactly repeat their path. That is, they do have a predictable overall form, but the form is made up of unpredictable details. Velocity More important, the trajectory of nearby points diverge 0 rapidly reflecting sensitive dependence. Many different strange attractors exist, including the Lorenz, Julian, and Henon, each generated by a Velocity Y different equation. 0 Z The attractors + (right) exhibit fractal X geometry. Velocity 0 ATTRACTORS IN GENERAL We can generalize an attractor as any state toward which a Velocity system naturally evolves. -
Polarization Fields and Phase Space Densities in Storage Rings: Stroboscopic Averaging and the Ergodic Theorem
Physica D 234 (2007) 131–149 www.elsevier.com/locate/physd Polarization fields and phase space densities in storage rings: Stroboscopic averaging and the ergodic theorem✩ James A. Ellison∗, Klaus Heinemann Department of Mathematics and Statistics, University of New Mexico, Albuquerque, NM 87131, United States Received 1 May 2007; received in revised form 6 July 2007; accepted 9 July 2007 Available online 14 July 2007 Communicated by C.K.R.T. Jones Abstract A class of orbital motions with volume preserving flows and with vector fields periodic in the “time” parameter θ is defined. Spin motion coupled to the orbital dynamics is then defined, resulting in a class of spin–orbit motions which are important for storage rings. Phase space densities and polarization fields are introduced. It is important, in the context of storage rings, to understand the behavior of periodic polarization fields and phase space densities. Due to the 2π time periodicity of the spin–orbit equations of motion the polarization field, taken at a sequence of increasing time values θ,θ 2π,θ 4π,... , gives a sequence of polarization fields, called the stroboscopic sequence. We show, by using the + + Birkhoff ergodic theorem, that under very general conditions the Cesaro` averages of that sequence converge almost everywhere on phase space to a polarization field which is 2π-periodic in time. This fulfills the main aim of this paper in that it demonstrates that the tracking algorithm for stroboscopic averaging, encoded in the program SPRINT and used in the study of spin motion in storage rings, is mathematically well-founded. -
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). -
Matrices That Are Similar to Their Inverses
116 THE MATHEMATICAL GAZETTE Matrices that are similar to their inverses GRIGORE CÃLUGÃREANU 1. Introduction In a group G, an element which is conjugate with its inverse is called real, i.e. the element and its inverse belong to the same conjugacy class. An element is called an involution if it is of order 2. With these notions it is easy to formulate the following questions. 1) Which are the (finite) groups all of whose elements are real ? 2) Which are the (finite) groups such that the identity and involutions are the only real elements ? 3) Which are the (finite) groups in which the real elements form a subgroup closed under multiplication? According to specialists, these (general) questions cannot be solved in any reasonable way. For example, there are numerous families of groups all of whose elements are real, like the symmetric groups Sn. There are many solvable groups whose elements are all real, and one can prove that any finite solvable group occurs as a subgroup of a solvable group whose elements are all real. As for question 2, note that in any Abelian group (conjugations are all the identity function), the only real elements are the identity and the involutions, and they form a subgroup. There are non-abelian examples as well, like a Suzuki 2-group. Question 3 is similar to questions 1 and 2. Therefore the abstract study of reality questions in finite groups is unlikely to have a good outcome. This may explain why in the existing bibliography there are only specific studies (see [1, 2, 3, 4]). -
Centro-Invertible Matrices Linear Algebra and Its Applications, 434 (2011) Pp144-151
References • R.S. Wikramaratna, The centro-invertible matrix:a new type of matrix arising in pseudo-random number generation, Centro-invertible Matrices Linear Algebra and its Applications, 434 (2011) pp144-151. [doi:10.1016/j.laa.2010.08.011]. Roy S Wikramaratna, RPS Energy [email protected] • R.S. Wikramaratna, Theoretical and empirical convergence results for additive congruential random number generators, Reading University (Conference in honour of J. Comput. Appl. Math., 233 (2010) 2302-2311. Nancy Nichols' 70th birthday ) [doi: 10.1016/j.cam.2009.10.015]. 2-3 July 2012 Career Background Some definitions … • Worked at Institute of Hydrology, 1977-1984 • I is the k by k identity matrix – Groundwater modelling research and consultancy • J is the k by k matrix with ones on anti-diagonal and zeroes – P/t MSc at Reading 1980-82 (Numerical Solution of PDEs) elsewhere • Worked at Winfrith, Dorset since 1984 – Pre-multiplication by J turns a matrix ‘upside down’, reversing order of terms in each column – UKAEA (1984 – 1995), AEA Technology (1995 – 2002), ECL Technology (2002 – 2005) and RPS Energy (2005 onwards) – Post-multiplication by J reverses order of terms in each row – Oil reservoir engineering, porous medium flow simulation and 0 0 0 1 simulator development 0 0 1 0 – Consultancy to Oil Industry and to Government J = = ()j 0 1 0 0 pq • Personal research interests in development and application of numerical methods to solve engineering 1 0 0 0 j =1 if p + q = k +1 problems, and in mathematical and numerical analysis -
On the Construction of Lightweight Circulant Involutory MDS Matrices⋆
On the Construction of Lightweight Circulant Involutory MDS Matrices? Yongqiang Lia;b, Mingsheng Wanga a. State Key Laboratory of Information Security, Institute of Information Engineering, Chinese Academy of Sciences, Beijing, China b. Science and Technology on Communication Security Laboratory, Chengdu, China [email protected] [email protected] Abstract. In the present paper, we investigate the problem of con- structing MDS matrices with as few bit XOR operations as possible. The key contribution of the present paper is constructing MDS matrices with entries in the set of m × m non-singular matrices over F2 directly, and the linear transformations we used to construct MDS matrices are not assumed pairwise commutative. With this method, it is shown that circulant involutory MDS matrices, which have been proved do not exist over the finite field F2m , can be constructed by using non-commutative entries. Some constructions of 4 × 4 and 5 × 5 circulant involutory MDS matrices are given when m = 4; 8. To the best of our knowledge, it is the first time that circulant involutory MDS matrices have been constructed. Furthermore, some lower bounds on XORs that required to evaluate one row of circulant and Hadamard MDS matrices of order 4 are given when m = 4; 8. Some constructions achieving the bound are also given, which have fewer XORs than previous constructions. Keywords: MDS matrix, circulant involutory matrix, Hadamard ma- trix, lightweight 1 Introduction Linear diffusion layer is an important component of symmetric cryptography which provides internal dependency for symmetric cryptography algorithms. The performance of a diffusion layer is measured by branch number.