CONVERGENCE RATES of MARKOV CHAINS 1. Orientation 1
Total Page:16
File Type:pdf, Size:1020Kb
Load more
Recommended publications
-
Numerical Solution of Ordinary Differential Equations
NUMERICAL SOLUTION OF ORDINARY DIFFERENTIAL EQUATIONS Kendall Atkinson, Weimin Han, David Stewart University of Iowa Iowa City, Iowa A JOHN WILEY & SONS, INC., PUBLICATION Copyright c 2009 by John Wiley & Sons, Inc. All rights reserved. Published by John Wiley & Sons, Inc., Hoboken, New Jersey. Published simultaneously in Canada. No part of this publication may be reproduced, stored in a retrieval system, or transmitted in any form or by any means, electronic, mechanical, photocopying, recording, scanning, or otherwise, except as permitted under Section 107 or 108 of the 1976 United States Copyright Act, without either the prior written permission of the Publisher, or authorization through payment of the appropriate per-copy fee to the Copyright Clearance Center, Inc., 222 Rosewood Drive, Danvers, MA 01923, (978) 750-8400, fax (978) 646-8600, or on the web at www.copyright.com. Requests to the Publisher for permission should be addressed to the Permissions Department, John Wiley & Sons, Inc., 111 River Street, Hoboken, NJ 07030, (201) 748-6011, fax (201) 748-6008. Limit of Liability/Disclaimer of Warranty: While the publisher and author have used their best efforts in preparing this book, they make no representations or warranties with respect to the accuracy or completeness of the contents of this book and specifically disclaim any implied warranties of merchantability or fitness for a particular purpose. No warranty may be created ore extended by sales representatives or written sales materials. The advice and strategies contained herin may not be suitable for your situation. You should consult with a professional where appropriate. Neither the publisher nor author shall be liable for any loss of profit or any other commercial damages, including but not limited to special, incidental, consequential, or other damages. -
Randomized Algorithms
Chapter 9 Randomized Algorithms randomized The theme of this chapter is madendrizo algorithms. These are algorithms that make use of randomness in their computation. You might know of quicksort, which is efficient on average when it uses a random pivot, but can be bad for any pivot that is selected without randomness. Analyzing randomized algorithms can be difficult, so you might wonder why randomiza- tion in algorithms is so important and worth the extra effort. Well it turns out that for certain problems randomized algorithms are simpler or faster than algorithms that do not use random- ness. The problem of primality testing (PT), which is to determine if an integer is prime, is a good example. In the late 70s Miller and Rabin developed a famous and simple random- ized algorithm for the problem that only requires polynomial work. For over 20 years it was not known whether the problem could be solved in polynomial work without randomization. Eventually a polynomial time algorithm was developed, but it is much more complicated and computationally more costly than the randomized version. Hence in practice everyone still uses the randomized version. There are many other problems in which a randomized solution is simpler or cheaper than the best non-randomized solution. In this chapter, after covering the prerequisite background, we will consider some such problems. The first we will consider is the following simple prob- lem: Question: How many comparisons do we need to find the top two largest numbers in a sequence of n distinct numbers? Without the help of randomization, there is a trivial algorithm for finding the top two largest numbers in a sequence that requires about 2n − 3 comparisons. -
MA3K0 - High-Dimensional Probability
MA3K0 - High-Dimensional Probability Lecture Notes Stefan Adams i 2020, update 06.05.2020 Notes are in final version - proofreading not completed yet! Typos and errors will be updated on a regular basis Contents 1 Prelimaries on Probability Theory 1 1.1 Random variables . .1 1.2 Classical Inequalities . .4 1.3 Limit Theorems . .6 2 Concentration inequalities for independent random variables 8 2.1 Why concentration inequalities . .8 2.2 Hoeffding’s Inequality . 10 2.3 Chernoff’s Inequality . 13 2.4 Sub-Gaussian random variables . 15 2.5 Sub-Exponential random variables . 22 3 Random vectors in High Dimensions 27 3.1 Concentration of the Euclidean norm . 27 3.2 Covariance matrices and Principal Component Analysis (PCA) . 30 3.3 Examples of High-Dimensional distributions . 32 3.4 Sub-Gaussian random variables in higher dimensions . 34 3.5 Application: Grothendieck’s inequality . 36 4 Random Matrices 36 4.1 Geometrics concepts . 36 4.2 Concentration of the operator norm of random matrices . 38 4.3 Application: Community Detection in Networks . 41 4.4 Application: Covariance Estimation and Clustering . 41 5 Concentration of measure - general case 43 5.1 Concentration by entropic techniques . 43 5.2 Concentration via Isoperimetric Inequalities . 50 5.3 Some matrix calculus and covariance estimation . 54 6 Basic tools in high-dimensional probability 57 6.1 Decoupling . 57 6.2 Concentration for Anisotropic random vectors . 62 6.3 Symmetrisation . 63 7 Random Processes 64 ii Preface Introduction We discuss an elegant argument that showcases the -
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. -
Approximations in Numerical Analysis, Cont'd
Jim Lambers MAT 460/560 Fall Semester 2009-10 Lecture 7 Notes These notes correspond to Section 1.3 in the text. Approximations in Numerical Analysis, cont'd Convergence Many algorithms in numerical analysis are iterative methods that produce a sequence fαng of ap- proximate solutions which, ideally, converges to a limit α that is the exact solution as n approaches 1. Because we can only perform a finite number of iterations, we cannot obtain the exact solution, and we have introduced computational error. If our iterative method is properly designed, then this computational error will approach zero as n approaches 1. However, it is important that we obtain a sufficiently accurate approximate solution using as few computations as possible. Therefore, it is not practical to simply perform enough iterations so that the computational error is determined to be sufficiently small, because it is possible that another method may yield comparable accuracy with less computational effort. The total computational effort of an iterative method depends on both the effort per iteration and the number of iterations performed. Therefore, in order to determine the amount of compu- tation that is needed to attain a given accuracy, we must be able to measure the error in αn as a function of n. The more rapidly this function approaches zero as n approaches 1, the more rapidly the sequence of approximations fαng converges to the exact solution α, and as a result, fewer iterations are needed to achieve a desired accuracy. We now introduce some terminology that will aid in the discussion of the convergence behavior of iterative methods. -
HIGHER MOMENTS of BANACH SPACE VALUED RANDOM VARIABLES 1. Introduction Let X Be a Random Variable with Values in a Banach Space
HIGHER MOMENTS OF BANACH SPACE VALUED RANDOM VARIABLES SVANTE JANSON AND STEN KAIJSER Abstract. We define the k:th moment of a Banach space valued ran- dom variable as the expectation of its k:th tensor power; thus the mo- ment (if it exists) is an element of a tensor power of the original Banach space. We study both the projective and injective tensor products, and their relation. Moreover, in order to be general and flexible, we study three different types of expectations: Bochner integrals, Pettis integrals and Dunford integrals. One of the problems studied is whether two random variables with the same injective moments (of a given order) necessarily have the same projective moments; this is of interest in applications. We show that this holds if the Banach space has the approximation property, but not in general. Several sections are devoted to results in special Banach spaces, in- cluding Hilbert spaces, CpKq and Dr0; 1s. The latter space is non- separable, which complicates the arguments, and we prove various pre- liminary results on e.g. measurability in Dr0; 1s that we need. One of the main motivations of this paper is the application to Zolotarev metrics and their use in the contraction method. This is sketched in an appendix. 1. Introduction Let X be a random variable with values in a Banach space B. To avoid measurability problems, we assume for most of this section for simplicity that B is separable and X Borel measurable; see Section 3 for measurability in the general case. Moreover, for definiteness, we consider real Banach spaces only; the complex case is similar. -
On the Three Methods for Bounding the Rate of Convergence for Some Continuous-Time Markov Chains
On the Three Methods for Bounding the Rate of Convergence for some Continuous-time Markov Chains Alexander Zeifman∗ Yacov Satiny Anastasia Kryukovaz Rostislav Razumchik§ Ksenia Kiseleva{ Galina Shilovak Abstract. Consideration is given to the three different analytical methods for the computation of upper bounds for the rate of convergence to the limiting regime of one specific class of (in)homogeneous continuous- time Markov chains. This class is particularly suited to describe evolutions of the total number of customers in (in)homogeneous M=M=S queueing systems with possibly state-dependent arrival and service intensities, batch arrivals and services. One of the methods is based on the logarithmic norm of a linear operator function; the other two rely on Lyapunov functions and differential inequalities, respectively. Less restrictive conditions (compared to those known from the literature) under which the methods are applicable, are being formulated. Two numerical examples are given. It is also shown that for homogeneous birth-death Markov processes defined on a finite state space with all transition rates being positive, all methods yield the same sharp upper bound. ∗Vologda State University, Vologda, Russia; Institute of Informatics Problems, Federal Research Center “Computer Science and Control”, Russian Academy of Sciences, Moscow, arXiv:1911.04086v2 [math.PR] 24 Feb 2020 Russia; Vologda Research Center of the Russian Academy of Sciences, Vologda, Russia. E-mail: [email protected] yVologda State University, Vologda, Russia zVologda State -
Control Policy with Autocorrelated Noise in Reinforcement Learning for Robotics
International Journal of Machine Learning and Computing, Vol. 5, No. 2, April 2015 Control Policy with Autocorrelated Noise in Reinforcement Learning for Robotics Paweł Wawrzyński primitives by means of RL algorithms based on MDP Abstract—Direct application of reinforcement learning in framework, but it does not alleviate the problem of robot robotics rises the issue of discontinuity of control signal. jerking. The current paper is intended to fill this gap. Consecutive actions are selected independently on random, In this paper, control policy is introduced that may undergo which often makes them excessively far from one another. Such reinforcement learning and have the following properties: control is hardly ever appropriate in robots, it may even lead to their destruction. This paper considers a control policy in which It is applicable to robot control optimization. consecutive actions are modified by autocorrelated noise. That It does not lead to robot jerking. policy generally solves the aforementioned problems and it is readily applicable in robots. In the experimental study it is It can be optimized by any RL algorithm that is designed applied to three robotic learning control tasks: Cart-Pole to optimize a classical stochastic control policy. SwingUp, Half-Cheetah, and a walking humanoid. The policy introduced here is based on a deterministic transformation of state combined with a random element in Index Terms—Machine learning, reinforcement learning, actorcritics, robotics. the form of a specific stochastic process, namely the moving average. The paper is organized as follows. In Section II the I. INTRODUCTION problem of our interest is defined. Section III presents the main contribution of this paper i.e., a stochastic control policy Reinforcement learning (RL) addresses the problem of an that prevents robot jerking while learning. -
Multivariate Scale-Mixed Stable Distributions and Related Limit
Multivariate scale-mixed stable distributions and related limit theorems∗ Victor Korolev,† Alexander Zeifman‡ Abstract In the paper, multivariate probability distributions are considered that are representable as scale mixtures of multivariate elliptically contoured stable distributions. It is demonstrated that these distributions form a special subclass of scale mixtures of multivariate elliptically contoured normal distributions. Some properties of these distributions are discussed. Main attention is paid to the representations of the corresponding random vectors as products of independent random variables. In these products, relations are traced of the distributions of the involved terms with popular probability distributions. As examples of distributions of the class of scale mixtures of multivariate elliptically contoured stable distributions, multivariate generalized Linnik distributions are considered in detail. Their relations with multivariate ‘ordinary’ Linnik distributions, multivariate normal, stable and Laplace laws as well as with univariate Mittag-Leffler and generalized Mittag-Leffler distributions are discussed. Limit theorems are proved presenting necessary and sufficient conditions for the convergence of the distributions of random sequences with independent random indices (including sums of a random number of random vectors and multivariate statistics constructed from samples with random sizes) to scale mixtures of multivariate elliptically contoured stable distributions. The property of scale- mixed multivariate stable -
Random Fields, Fall 2014 1
Random fields, Fall 2014 1 fMRI brain scan Ocean waves Nobel prize 2996 to The oceans cover 72% of John C. Mather the earth’s surface. George F. Smoot Essential for life on earth, “for discovery of the and huge economic blackbody form and importance through anisotropy of the cosmic fishing, transportation, microwave background oil and gas extraction radiation" PET brain scan TexPoint fonts used in EMF. Read the TexPoint manual before you delete this box.: A The course • Kolmogorov existence theorem, separable processes, measurable processes • Stationarity and isotropy • Orthogonal and spectral representations • Geometry • Exceedance sets • Rice formula • Slepian models Literature An unfinished manuscript “Applications of RANDOM FIELDS AND GEOMETRY: Foundations and Case Studies” by Robert Adler, Jonathan Taylor, and Keith Worsley. Complementary literature: “Level sets and extrema of random processes and fields” by Jean- Marc Azais and Mario Wschebor, Wiley, 2009 “Asymptotic Methods in the Theory of Gaussian Processes” by Vladimir Piterbarg, American Mathematical Society, ser. Translations of Mathematical Monographs, Vol. 148, 1995 “Random fields and Geometry” by Robert Adler and Jonathan Taylor, Springer 2007 Slides for ATW ch 2, p 24-39 Exercises: 2.8.1, 2.8.2, 2.8.3, 2.8.4, 2.8.5, 2.8.6 + excercises in slides Stochastic convergence (assumed known) 푎.푠. Almost sure convergence: 푋푛 X 퐿2 Mean square convergence: 푋푛 X 푃 Convergence in probability: 푋푛 X 푑 푑 푑 Convergence in distribution: 푋푛 X, 퐹푛 F, 푃푛 P, … 푎.푠. 푃 • 푋푛 X ⇒ 푋푛 푋 퐿2 푃 • 푋푛 X ⇒ 푋푛 푋 푃 퐿2 • 푋푛 푋 plus uniform integrability ⇒ 푋푛 X 푃 푎.푠. -
Convergence Rates for the Quantum Central Limit Theorem
Commun. Math. Phys. 383, 223–279 (2021) Communications in Digital Object Identifier (DOI) https://doi.org/10.1007/s00220-021-03988-1 Mathematical Physics Convergence Rates for the Quantum Central Limit Theorem Simon Becker1 , Nilanjana Datta1, Ludovico Lami2,3, Cambyse Rouzé4 1 Department of Applied Mathematics and Theoretical Physics, Centre for Mathematical Sciences University of Cambridge, Cambridge CB3 0WA,UK. E-mail: [email protected]; [email protected] 2 School of Mathematical Sciences and Centre for the Mathematics and Theoretical Physics of Quantum Non-Equilibrium Systems, University of Nottingham, University Park, Nottingham NG7 2RD, UK. 3 Institute of Theoretical Physics and IQST, Universität Ulm, Albert-Einstein-Allee, 89069 Ulm, Germany. E-mail: [email protected] 4 Zentrum Mathematik, Technische Universität München, 85748 Garching, Germany. E-mail: [email protected] Received: 20 February 2020 / Accepted: 15 January 2021 Published online: 15 February 2021 – © The Author(s) 2021 Contents 1. Introduction ................................. 224 2. Notation and Definitions ........................... 228 2.1 Mathematical notation ......................... 228 2.2 Definitions ................................ 229 2.2.1 Quantum information with continuous variables .......... 229 2.2.2 Phase space formalism ....................... 231 2.3 Moments ................................ 233 2.4 Quantum convolution .......................... 235 3. Cushen and Hudson’s Quantum Central Limit Theorem .......... 236 4. Main Results ................................. 237 4.1 Quantitative bounds in the QCLT .................... 237 4.2 Optimality of convergence rates and necessity of finite second moments in the QCLT ............................... 239 4.3 Applications to capacity of cascades of beam splitters with non-Gaussian environment ............................... 240 4.4 New results on quantum characteristic functions ............ 242 5. New Results on Quantum Characteristic Functions: Proofs ........ -
High Order Regularization of Dirac-Delta Sources in Two Space Dimensions
INTERNSHIP REPORT High order regularization of Dirac-delta sources in two space dimensions. Author: Supervisors: Wouter DE VRIES Prof. Guustaaf JACOBS s1010239 Jean-Piero SUAREZ HOST INSTITUTION: San Diego State University, San Diego (CA), USA Department of Aerospace Engineering and Engineering Mechanics Computational Fluid Dynamics laboratory Prof. G.B. JACOBS HOME INSTITUTION University of Twente, Enschede, Netherlands Faculty of Engineering Technology Group of Engineering Fluid Dynamics Prof. H.W.M. HOEIJMAKERS March 4, 2015 Abstract In this report the regularization of singular sources appearing in hyperbolic conservation laws is studied. Dirac-delta sources represent small particles which appear as source-terms in for example Particle-laden flow. The Dirac-delta sources are regularized with a high order accurate polynomial based regularization technique. Instead of regularizing a single isolated particle, the aim is to employ the regularization technique for a cloud of particles distributed on a non- uniform grid. By assuming that the resulting force exerted by a cloud of particles can be represented by a continuous function, the influence of the source term can be approximated by the convolution of the continuous function and the regularized Dirac-delta function. It is shown that in one dimension the distribution of the singular sources, represented by the convolution of the contin- uous function and Dirac-delta function, can be approximated with high accuracy using the regularization technique. The method is extended to two dimensions and high order approximations of the source-term are obtained as well. The resulting approximation of the singular sources are interpolated using a polynomial interpolation in order to regain a continuous distribution representing the force exerted by the cloud of particles.