PPT-Ergodic transition: a toy random matrix model

Author : giovanna-bartolotta | Published Date : 2016-04-07

VEKravtsov ICTP Trieste and Landau Institute Collaboration Ivan Khaymovich Aaalto Emilio Cuevas Murcia Manybody localization Anderson localization model on random

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Ergodic transition: a toy random matrix model: Transcript

VEKravtsov ICTP Trieste and Landau Institute Collaboration Ivan Khaymovich Aaalto Emilio Cuevas Murcia Manybody localization Anderson localization model on random regular graph RRG. Note first half of talk consists of blackboard. see video. : . http. ://www.fields.utoronto.ca/video-archive/2013/07/215-. 1962. then I did a . matlab. demo. t=1000000; . i. =. sqrt. (-1);figure(1);hold . Lecture. 8. Ergodicty. 1. Random process. 2. 3. Agenda (. Lec. . . 8. ). Ergodicity. Central equations. Biomedical engineering example:. Analysis of heart sound murmurs. 4. Ergodicity. A random process . The Brown Bag. Hassan Bukhari. BS . Physics . 2012. 墫鱡뱿轺. . Stat . Mech. Project. . “. It is not less important to understand the foundation of such a complex issue than to calculate useful quantities”. I. sometry . P. roperty for General . Norms. . Zeyuan Allen-Zhu . (MIT / Princeton). Rati Gelashvili . (MIT). Ilya Razenshteyn . (MIT, now IBM . Almaden. ). Linear dimension reduction. Is there a . Part 4. The Story so far …. Def:. Markov Chain: collection of states together with a matrix of probabilities called transition matrix (. p. ij. ) where . p. ij. indicates the probability of switching from state S. Ergodic phases in strongly disordered random regular graphs. V.E.Kravtsov. ICTP, Trieste. . Collaboration: . Boris . Altshuler. , Columbia U.. Lev . Ioffe. , Paris and Rutgers. Ivan . Khaymovich. , Aalto. (part 1). 1. Haim Kaplan and Uri Zwick. Algorithms in Action. Tel Aviv University. Last updated: April . 15 . 2016. (Finite, Discrete time) Markov chain. 2. A sequence . of random variables. . Each . Reid . Calamita. Motivation: Why Dynamics?. Modeling motion through time. Analytical Solutions. Numerical Approximations. Qualitative Results. Fixed Points. Robustness. General Behavior. Bounds. Limit Cycles. regular Or Ergodic?. Absorbing state: A state in a . Markov . chain . that . you . cannot . leave, . i.e. . p. ii. = 1. . Absorbing . Markov chain. : . if it has at least one absorbing state and it is possible to reach that absorbing state from any other state. . Richard Peng. Georgia Tech. OUtline. (Structured) Linear Systems. Iterative and Direct Methods. (. Graph) . Sparsification. Sparsified. Squaring. Speeding up Gaussian Elimination. Graph Laplacians. 1. Jen-Hao Yeh, . Sameer Hemmady, Xing . Zheng. , James . Hart, . Edward Ott, Thomas Antonsen, Steven M. Anlage. Research funded by AFOSR and the . ONR/UMD AppEl, ONR-MURI . and DURIP programs. It makes no sense to talk about. Richard Peng. Georgia Tech. OUtline. (Structured) Linear Systems. Iterative and Direct Methods. (. Graph) . Sparsification. Sparsified. Squaring. Speeding up Gaussian Elimination. Graph Laplacians. 1. Extraction with Dynamic Transition Matrix. Bingfeng. Luo. , . Yansong. . Feng,. . Zheng. . Wang,. . Zhanxing. . Zhu,. . Songfang. . Huang. , . Rui. Yan. . and. . Dongyan. . Zhao. 2017/04/22. Authors: . Kexiang. Wang, . Zhifang. Sui, et al.. Organization: Peking University. Speaker: . Kexiang. Wang. E-mail: wkx@pku.edu.cn. Outline. Overview of Our Paper. Aim. We propose the adjustable affinity-preserving random walk method for generic and query-focused multi-document summarization to enforce the .

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