Composition Operators Associated with Linear Fractional Transformations in Complex Spaces Fiana Jacobzon, Simeon Reich and David Shoikhet ORT BRAUDE COLLEGE.

Slides:



Advertisements
Similar presentations
Boyce/DiPrima 9th ed, Ch 2.4: Differences Between Linear and Nonlinear Equations Elementary Differential Equations and Boundary Value Problems, 9th edition,
Advertisements

Abbas Edalat Imperial College London Contains joint work with Andre Lieutier (AL) and joint work with Marko Krznaric (MK) Data Types.
Introduction The concept of transform appears often in the literature of image processing and data compression. Indeed a suitable discrete representation.
Boyce/DiPrima 9th ed, Ch 2.8: The Existence and Uniqueness Theorem Elementary Differential Equations and Boundary Value Problems, 9th edition, by William.
Many useful applications, especially in queueing systems, inventory management, and reliability analysis. A connection between discrete time Markov chains.
Ort Braude College of Engineering, 2013 Final Project for the Applied Mathematics Bachelor's Degree (B.Sc) By Ariel Hoffman Advisors: Dr. Fiana Yacobzon,
Lecture 6  Calculating P n – how do we raise a matrix to the n th power?  Ergodicity in Markov Chains.  When does a chain have equilibrium probabilities?
Use of moment generating functions. Definition Let X denote a random variable with probability density function f(x) if continuous (probability mass function.
1.  Detailed Study of groups is a fundamental concept in the study of abstract algebra. To define the notion of groups,we require the concept of binary.
Topics Review of DTMC Classification of states Economic analysis
Hypothesis testing Some general concepts: Null hypothesisH 0 A statement we “wish” to refute Alternative hypotesisH 1 The whole or part of the complement.
In a long history associated with the problem on iterating holomorphic mappings and their fixed points, the work of G. Julia, J. Wolff and C. Caratheodory.
Completeness of the Coulomb eigenfunctions Myles Akin Cyclotron Institute, Texas A&M University, College Station, Texas University of Georgia, Athens,
ON MULTIVARIATE POLYNOMIAL INTERPOLATION
Dirichlet’s Theorem for Polynomial Rings Lior Bary-Soroker, School of Mathematical Sciences, Sackler Faculty of Exact Sciences, Tel-Aviv University 1.
The Monte Carlo Method: an Introduction Detlev Reiter Research Centre Jülich (FZJ) D Jülich
Linear Systems The definition of a linear equation given in Chapter 1 can be extended to more variables; any equation of the form for real numbers.
LINEAR EQUATION IN TWO VARIABLES. System of equations or simultaneous equations – System of equations or simultaneous equations – A pair of linear equations.
1 10. Joint Moments and Joint Characteristic Functions Following section 6, in this section we shall introduce various parameters to compactly represent.
1 1.1 © 2012 Pearson Education, Inc. Linear Equations in Linear Algebra SYSTEMS OF LINEAR EQUATIONS.
6. Markov Chain. State Space The state space is the set of values a random variable X can take. E.g.: integer 1 to 6 in a dice experiment, or the locations.
Mohammed Nasser Acknowledgement: Steve Cunningham
The Poisson Process. A stochastic process { N ( t ), t ≥ 0} is said to be a counting process if N ( t ) represents the total number of “events” that occur.
880.P20 Winter 2006 Richard Kass 1 Confidence Intervals and Upper Limits Confidence intervals (CI) are related to confidence limits (CL). To calculate.
MA Dynamical Systems MODELING CHANGE. Introduction to Dynamical Systems.
Laying the Quantum and Classical Embedding Problems to Rest arXiv: Toby Cubitt 1, Jens Eisert 2, Michael Wolf 3 1 University of Bristol, 2 Potsdam.
MA Dynamical Systems MODELING CHANGE. Modeling Change: Dynamical Systems A dynamical system is a changing system. Definition Dynamic: marked by.
Copyright © Cengage Learning. All rights reserved. CHAPTER 5 SEQUENCES, MATHEMATICAL INDUCTION, AND RECURSION SEQUENCES, MATHEMATICAL INDUCTION, AND RECURSION.
4 4.2 © 2012 Pearson Education, Inc. Vector Spaces NULL SPACES, COLUMN SPACES, AND LINEAR TRANSFORMATIONS.
PROBABILITY AND STATISTICS FOR ENGINEERING Hossein Sameti Department of Computer Engineering Sharif University of Technology Two Functions of Two Random.
4 4.4 © 2012 Pearson Education, Inc. Vector Spaces COORDINATE SYSTEMS.
Functions of Random Variables. Methods for determining the distribution of functions of Random Variables 1.Distribution function method 2.Moment generating.
Finite Element Method.
MA Dynamical Systems MODELING CHANGE. Introduction and Historical Context.
S TOCHASTIC M ODELS L ECTURE 1 P ART II M ARKOV C HAINS Nan Chen MSc Program in Financial Engineering The Chinese University of Hong Kong (ShenZhen) Sept.
Table of Contents Solving Linear Systems of Equations - Dependent Systems The goal in solving a linear system of equations is to find the values of the.
1 2. Independence and Bernoulli Trials Independence: Events A and B are independent if It is easy to show that A, B independent implies are all independent.
MA Dynamical Systems MODELING CHANGE. Modeling Change: Dynamical Systems ‘Powerful paradigm’ future value = present value + change equivalently:
POPULATION DYNAMICS Katja Goldring, Francesca Grogan, Garren Gaut, Advisor: Cymra Haskell.
SECTION 3 ISOMORPHIC BINARY STRUCTURES Definition Let  S,  and  S’,  ’  be binary algebraic structures. An isomorphism of S with S’ is a one-to-one.
Integration of 3-body encounter. Figure taken from
MATH4248 Weeks Topics: review of rigid body motion, Legendre transformations, derivation of Hamilton’s equations of motion, phase space and Liouville’s.
1 Let X represent a Binomial r.v as in (3-42). Then from (2-30) Since the binomial coefficient grows quite rapidly with n, it is difficult to compute (4-1)
1 Two Functions of Two Random Variables In the spirit of the previous lecture, let us look at an immediate generalization: Suppose X and Y are two random.
Fall 2015 COMP 2300 Discrete Structures for Computation Donghyun (David) Kim Department of Mathematics and Physics North Carolina Central University 1.
Expectation. Let X denote a discrete random variable with probability function p(x) (probability density function f(x) if X is continuous) then the expected.
1 Section 5.3 Linear Systems of Equations. 2 THREE EQUATIONS WITH THREE VARIABLES Consider the linear system of three equations below with three unknowns.
Bell Work: Simplify: √500,000,000. Answer: 10,000√5.
Review of Probability. Important Topics 1 Random Variables and Probability Distributions 2 Expected Values, Mean, and Variance 3 Two Random Variables.
Brief Review Probability and Statistics. Probability distributions Continuous distributions.
1 ۞ An eigenvalue λ and an eigenfunction f(x) of an operator Ĥ in a space S satisfy Week 6 2. Properties of self-adjoint operators where f(x) is implied.
Chapter 1 First-Order Differential Equations Shurong Sun University of Jinan Semester 1,
Joint Moments and Joint Characteristic Functions.
Boundary-Value Problems in Rectangular Coordinates
CS433 Modeling and Simulation Lecture 11 Continuous Markov Chains Dr. Anis Koubâa 01 May 2009 Al-Imam Mohammad Ibn Saud University.
Stochastic Processes and Transition Probabilities D Nagesh Kumar, IISc Water Resources Planning and Management: M6L5 Stochastic Optimization.
A function is a rule f that associates with each element in a set A one and only one element in a set B. If f associates the element b with the element.
Topics 1 Specific topics to be covered are: Discrete-time signals Z-transforms Sampling and reconstruction Aliasing and anti-aliasing filters Sampled-data.
ECE-7000: Nonlinear Dynamical Systems 3. Phase Space Methods 3.1 Determinism: Uniqueness in phase space We Assume that the system is linear stochastic.
Ch 6.2: Solution of Initial Value Problems The Laplace transform is named for the French mathematician Laplace, who studied this transform in The.
Introduction to Symmetry Analysis Brian Cantwell Department of Aeronautics and Astronautics Stanford University Chapter 1 - Introduction to Symmetry.
Properties of Groups Proposition 1: Let (G,  ) be a group. i.The inverse element of any element of G is unique. Remark: In view of i., we may use the.
Theory of Computational Complexity Probability and Computing Ryosuke Sasanuma Iwama and Ito lab M1.
Topic Overview and Study Checklist. From Chapter 7 in the white textbook: Modeling with Differential Equations basic models exponential logistic modified.
Initial Conditions & Passive Network Synthesis. Sarvajanik College of Engineering & Technology Made by: Dhruvita Shah Khushbu Shah
Complex Variables. Complex Variables Open Disks or Neighborhoods Definition. The set of all points z which satisfy the inequality |z – z0|
Before: November 28, 2017 Solve each system by graphing. 1. y = 2x – 1
Locality In Distributed Graph Algorithms
Presentation transcript:

Composition Operators Associated with Linear Fractional Transformations in Complex Spaces Fiana Jacobzon, Simeon Reich and David Shoikhet ORT BRAUDE COLLEGE & TECHNION

There was concern amongst the Victorians that aristocratic surnames were becoming extinct. Galton originally posed the question regarding the probability of such an event in the Educational Times of 1873, and the Reverend Henry William Watson replied with a solution. Together, they then wrote in 1874 paper entitled “ On the probability of extinction of families. “ The Galton-Watson model Historical background One of the first applicable models of the complex dynamical systems on the unit disk arose more than a hundred years ago in studies of dynamics of stochastic branching processes.

Let us consider a process starting with a single particle which splits to an unknown number m of new identical particles in the first generation. Then in the next generation each one of m particles splits to an unknown number of new identical particles and so on... III This stochastic process is called the Galton-Watson branching process. The Galton-Watson model

Since p m (1) is distribution of probabilities, then it generates function Let ∆ be the open unit disk in C. Obviously, F : ∆ → ∆ is an analytic function. I So we start at time t = 0 with a single particle (Z(0)=1) The first generation Z(1) is a random variable with distribution of probabilities with The Galton-Watson model

III The question is: What is the distribution of probabilities of a random variable Z(t) in the t- th generation? In other words, what is the probability p (n) k that after t = n generation the number of particles will be k? The Galton-Watson model

III The Galton-Watson model The question is very complicated because we do not know the situation in the previous generations.

So one can define the needed distribution of probabilities as the coefficients of Taylor extension of F (n) It turns out, that if is n -iterate of F, i.e., then It can be shown that there exists the limit the extinction probability of the branching process which is called the extinction probability of the branching process. The Galton-Watson model F 2 (0) 0 F 5 (0) F 4 (0) F 3 (0) F 1 (0) p

One parameter semigroups of analytic mappings Consider the family S ={ F 0, F 1, F 2,...} of iterates of F i.e., F 0 = I, F 1 =F, F 2 = F o F=F 2,... In other words, F 0 (z)=z, F 1 (z)=F(z), F 2 (z)=F(F(z)),… Let ∆ be the open unit disk in the complex plane C, and F : ∆ → ∆ be an analytic function in ∆ with values in ∆. In this case one says that F is a aa a self-mapping of the unit disk ∆. The pair (∆, S ) is called a aa a discrete dynamic system.

For any z Є ∆ we can construct the sequence {F n (z)} n Є N (N={ 0,1,2 …}) of points in ∆. F1(z)F1(z) z F4(z)F4(z) F3(z)F3(z) F2(z)F2(z) F5(z)F5(z) x y 1 ∆ {F (n) (z)} n Є N

The iteration problem Koenigs embedding process This problem can be solved by using the so-called Koenigs embedding process. Consider a family of the functions: S={F 0, F 1, F 2,...} such that F 0 = I, F 1 =F, F 2 = F (2),..., F n = F (n),... F 0 = I, F 1 =F, F 2 = F (2),..., F n = F (n),... Find F (n) explicitly for all n = 1,2,3,… i.F 1 = F ii.F t preserves iteration property for all t ≥ 0 a continuous semigroup A family of functions that satisfies both these properties is called a continuous semigroup. To do this we first should find a continuous function u (t, z) = F t (z) in parameter t, such that

Continuous semigroups of analytic functions A family S={F t } t≥0 is called a one-parameter continuous semigroup (flow) in ∆ if For integer t we get by property ( i ) than F 1 is F, than F 2 is F (2) and The pair (∆, S ) is called a continuous dynamic system. F1(z)F1(z) z F4(z)F4(z) F3(z)F3(z) F2(z)F2(z) F5(z)F5(z) F½(z)F½(z) F 1¾ (z) F 4⅔ (z)

Embedding problem A classical problem of analysis is given an analytic self-mapping F of the open unit disk ∆, to find a continuous semigroup S={F t } t≥0 in ∆ such that F 1 =F. If such a semigroup exists then F is said to be e ee embeddable. In general there are those self-mappings which are not embeddable. So, the problem becomes: describe the class of self-mappings which are embeddable. F 2 (0) 0 F 5 (0) F 4 (0) F 3 (0) F 1 (0)

Continuous semigroups of Linear Fractional mappings Linear Fractional Mappings(LFM) Most of those discrete applications based on semigroups produced from the so-called Linear Fractional Mappings (LFM), i.e., analytic functions in the complex plane of the form: The interest of the Galton-Watson model has increased because of connections with chemical and nuclear chain reactions, the theory of cosmic radiation, the dynamics of disease outbreaks in their generations of spread.

Another important problem is finding conditions on a self-mapping F which ensure that it can be embedded into a continuous semigroup. In particular, for LFM this problem can be reformulated as follows: Find the conditions on the coefficients of an LFM which ensure that it preserves the open disk Δ. Coefficients Problem Find the condition on the coefficients of LFM which ensure the existence of a continuous semigroup S={F t } t≥0 in ∆, such that F 1 =F.

Solution The important key to solve our problem is the asymptotic behavior of the semigroup in both discrete and continuous cases. It described in the well- known Theorem of Denjoy and Wolff. Theorem (Denjoy-Wolff, 1926) Let ∆ be the open unit disk in the complex plane C. If an analytic self- mapping F is not an elliptic automorhpism of ∆, then there is an unique point τ in ∆U ∂∆ such that the iterates {F (n) (z)} n Є N converge to τ uniformly on compact subsets of ∆. Denjoy –Wolff point The point τ is called the Denjoy –Wolff point of the semigroup and it is a common fixed point of {F (n) (z)} n Є N. If, in particular, F is a producing function of a Galton-Watson branching process, then τ is exactly the extinction probability of this process. E. Berkson and H. Porta (1981) established a continuous analog of Denjoy-Wolff theorem for continuous semigroups of analytic self-mapping of ∆. F 2 (z) z F 5 (z) F 4 (z) F 3 (z) F 1 (z) τ

1. Dilation case (rotation + shrinking): - the common fixed point (a) Re c = 0 (group of rotations) Examples (b) Re c ≠ 0

2. Hyperbolic case (shrinking the disk to a point): - the common DW pointExamples

3. Parabolic case : - the common DW pointExamples

with different features and properties, we consider these classes separately. Classification Since the class of analytic self-mappings comprises three subclasses:  dilation τ є Δτ є Δ  hyperbolic τ є ∂∆, 0< F ’ ( τ )<1  parabolic τ є ∂∆, F ’ ( τ )=1

Results Dilation case Proposition 1 (Elin, Reich and Shoikhet, 2001) Let F : Ĉ → Ĉ be an LFM of the form The following assertions hold: i. F analytic self-mapping of Δ if and only if | a |+| c | ≤ 1 ; ii. If i. holds and a ≠ 0 then F is embeddable into continuous semigroup of analytic self mappings of Δ if and only if In particular, if a є R, then F is always embeddable into a continuous semigroup of analytic self-mappings of Δ.

Results Hyperbolic case Proposition 2 Let F : Ĉ → Ĉ be an LFM of the form with F (1) = 1 and 0<F ’ (1) <1. The following assertion are equivalent: i.|c / b| ≤ 1 and c ≠ -b; ii.F analytic self-mapping of Δ of hyperbolic type; iii.F is embeddable into continuous semigroup of analytic self-mappings of Δ.

Results Proposition 3 Let F : Ĉ → Ĉ be an LFM of the form with F (1) = 1 and F ’ (1) =1. The following assertion are equivalent: i.Re(d / c) ≤ -1 and d ≠ -c; ii.F analytic self-mapping of Δ of parabolic type; iii.F is embeddable into continuous semigroup of analytic self-mappings of Δ. Parabolic case

The Method Koenigs function Our method of proof based on the so-called Koenigs function, which is a powerful tool to solve also many other problems as well as computational problems. with F (τ) = τ and 0<|F ’ (τ)| <1. functional equation of Shcroeder It was proven by Koenigs and Valiron that there exist a solution of the following functional equation of Shcroeder: h ( F(z)) = λ h(z), where λ = F ’ (τ) Thus, F can be represented in the form F(z) = h -1 ( λ h(z)), where λ = F ’ (τ) Then for all t ≥ 0 we can write F t as F t (z) = h -1 ( λ t h(z)), where λ = F ’ (τ) Namely, consider for example an LFM of dilation or hyperbolic case, that is

Example Let us consider an LFM of dilation type The Koenigs function associated with F is so Thus, F can be represented in the form F(z) = h -1 ( λ h(z)), where λ = F ’ ( τ) = ½, hence F t (z) = h -1 ( λ t h(z)). Direct calculations show: In particular, substituting here t = n, we get explicitly all iterates F ( n ) = F n

Thank you III