A Combinatorial Tool for Computing the Effective Homotopy of Iterated Loop
Total Page:16
File Type:pdf, Size:1020Kb
Load more
Recommended publications
-
Persistent Homology of Unweighted Complex Networks Via Discrete
www.nature.com/scientificreports OPEN Persistent homology of unweighted complex networks via discrete Morse theory Received: 17 January 2019 Harish Kannan1, Emil Saucan2,3, Indrava Roy1 & Areejit Samal 1,4 Accepted: 6 September 2019 Topological data analysis can reveal higher-order structure beyond pairwise connections between Published: xx xx xxxx vertices in complex networks. We present a new method based on discrete Morse theory to study topological properties of unweighted and undirected networks using persistent homology. Leveraging on the features of discrete Morse theory, our method not only captures the topology of the clique complex of such graphs via the concept of critical simplices, but also achieves close to the theoretical minimum number of critical simplices in several analyzed model and real networks. This leads to a reduced fltration scheme based on the subsequence of the corresponding critical weights, thereby leading to a signifcant increase in computational efciency. We have employed our fltration scheme to explore the persistent homology of several model and real-world networks. In particular, we show that our method can detect diferences in the higher-order structure of networks, and the corresponding persistence diagrams can be used to distinguish between diferent model networks. In summary, our method based on discrete Morse theory further increases the applicability of persistent homology to investigate the global topology of complex networks. In recent years, the feld of topological data analysis (TDA) has rapidly grown to provide a set of powerful tools to analyze various important features of data1. In this context, persistent homology has played a key role in bring- ing TDA to the fore of modern data analysis. -
Combinatorial Topology
Chapter 6 Basics of Combinatorial Topology 6.1 Simplicial and Polyhedral Complexes In order to study and manipulate complex shapes it is convenient to discretize these shapes and to view them as the union of simple building blocks glued together in a “clean fashion”. The building blocks should be simple geometric objects, for example, points, lines segments, triangles, tehrahedra and more generally simplices, or even convex polytopes. We will begin by using simplices as building blocks. The material presented in this chapter consists of the most basic notions of combinatorial topology, going back roughly to the 1900-1930 period and it is covered in nearly every algebraic topology book (certainly the “classics”). A classic text (slightly old fashion especially for the notation and terminology) is Alexandrov [1], Volume 1 and another more “modern” source is Munkres [30]. An excellent treatment from the point of view of computational geometry can be found is Boissonnat and Yvinec [8], especially Chapters 7 and 10. Another fascinating book covering a lot of the basics but devoted mostly to three-dimensional topology and geometry is Thurston [41]. Recall that a simplex is just the convex hull of a finite number of affinely independent points. We also need to define faces, the boundary, and the interior of a simplex. Definition 6.1 Let be any normed affine space, say = Em with its usual Euclidean norm. Given any n+1E affinely independentpoints a ,...,aE in , the n-simplex (or simplex) 0 n E σ defined by a0,...,an is the convex hull of the points a0,...,an,thatis,thesetofallconvex combinations λ a + + λ a ,whereλ + + λ =1andλ 0foralli,0 i n. -
WEIGHT FILTRATIONS in ALGEBRAIC K-THEORY Daniel R
November 13, 1992 WEIGHT FILTRATIONS IN ALGEBRAIC K-THEORY Daniel R. Grayson University of Illinois at Urbana-Champaign Abstract. We survey briefly some of the K-theoretic background related to the theory of mixed motives and motivic cohomology. 1. Introduction. The recent search for a motivic cohomology theory for varieties, described elsewhere in this volume, has been largely guided by certain aspects of the higher algebraic K-theory developed by Quillen in 1972. It is the purpose of this article to explain the sense in which the previous statement is true, and to explain how it is thought that the motivic cohomology groups with rational coefficients arise from K-theory through the intervention of the Adams operations. We give a basic description of algebraic K-theory and explain how Quillen’s idea [42] that the Atiyah-Hirzebruch spectral sequence of topology may have an algebraic analogue guides the search for motivic cohomology. There are other useful survey articles about algebraic K-theory: [50], [46], [23], [56], and [40]. I thank W. Dwyer, E. Friedlander, S. Lichtenbaum, R. McCarthy, S. Mitchell, C. Soul´e and R. Thomason for frequent and useful consultations. 2. Constructing topological spaces. In this section we explain the considerations from combinatorial topology which give rise to the higher algebraic K-groups. The first principle is simple enough to state, but hard to implement: when given an interesting group (such as the Grothendieck group of a ring) arising from some algebraic situation, try to realize it as a low-dimensional homotopy group (especially π0 or π1) of a space X constructed in some combinatorial way from the same algebraic inputs, and study the homotopy type of the resulting space. -
Combinatorial Topology and Applications to Quantum Field Theory
Combinatorial Topology and Applications to Quantum Field Theory by Ryan George Thorngren A dissertation submitted in partial satisfaction of the requirements for the degree of Doctor of Philosophy in Mathematics in the Graduate Division of the University of California, Berkeley Committee in charge: Professor Vivek Shende, Chair Professor Ian Agol Professor Constantin Teleman Professor Joel Moore Fall 2018 Abstract Combinatorial Topology and Applications to Quantum Field Theory by Ryan George Thorngren Doctor of Philosophy in Mathematics University of California, Berkeley Professor Vivek Shende, Chair Topology has become increasingly important in the study of many-body quantum mechanics, in both high energy and condensed matter applications. While the importance of smooth topology has long been appreciated in this context, especially with the rise of index theory, torsion phenomena and dis- crete group symmetries are relatively new directions. In this thesis, I collect some mathematical results and conjectures that I have encountered in the exploration of these new topics. I also give an introduction to some quantum field theory topics I hope will be accessible to topologists. 1 To my loving parents, kind friends, and patient teachers. i Contents I Discrete Topology Toolbox1 1 Basics4 1.1 Discrete Spaces..........................4 1.1.1 Cellular Maps and Cellular Approximation.......6 1.1.2 Triangulations and Barycentric Subdivision......6 1.1.3 PL-Manifolds and Combinatorial Duality........8 1.1.4 Discrete Morse Flows...................9 1.2 Chains, Cycles, Cochains, Cocycles............... 13 1.2.1 Chains, Cycles, and Homology.............. 13 1.2.2 Pushforward of Chains.................. 15 1.2.3 Cochains, Cocycles, and Cohomology......... -
Homology Groups of Homeomorphic Topological Spaces
An Introduction to Homology Prerna Nadathur August 16, 2007 Abstract This paper explores the basic ideas of simplicial structures that lead to simplicial homology theory, and introduces singular homology in order to demonstrate the equivalence of homology groups of homeomorphic topological spaces. It concludes with a proof of the equivalence of simplicial and singular homology groups. Contents 1 Simplices and Simplicial Complexes 1 2 Homology Groups 2 3 Singular Homology 8 4 Chain Complexes, Exact Sequences, and Relative Homology Groups 9 ∆ 5 The Equivalence of H n and Hn 13 1 Simplices and Simplicial Complexes Definition 1.1. The n-simplex, ∆n, is the simplest geometric figure determined by a collection of n n + 1 points in Euclidean space R . Geometrically, it can be thought of as the complete graph on (n + 1) vertices, which is solid in n dimensions. Figure 1: Some simplices Extrapolating from Figure 1, we see that the 3-simplex is a tetrahedron. Note: The n-simplex is topologically equivalent to Dn, the n-ball. Definition 1.2. An n-face of a simplex is a subset of the set of vertices of the simplex with order n + 1. The faces of an n-simplex with dimension less than n are called its proper faces. 1 Two simplices are said to be properly situated if their intersection is either empty or a face of both simplices (i.e., a simplex itself). By \gluing" (identifying) simplices along entire faces, we get what are known as simplicial complexes. More formally: Definition 1.3. A simplicial complex K is a finite set of simplices satisfying the following condi- tions: 1 For all simplices A 2 K with α a face of A, we have α 2 K. -
Graph Reconstruction by Discrete Morse Theory Arxiv:1803.05093V2 [Cs.CG] 21 Mar 2018
Graph Reconstruction by Discrete Morse Theory Tamal K. Dey,∗ Jiayuan Wang,∗ Yusu Wang∗ Abstract Recovering hidden graph-like structures from potentially noisy data is a fundamental task in modern data analysis. Recently, a persistence-guided discrete Morse-based framework to extract a geometric graph from low-dimensional data has become popular. However, to date, there is very limited theoretical understanding of this framework in terms of graph reconstruction. This paper makes a first step towards closing this gap. Specifically, first, leveraging existing theoretical understanding of persistence-guided discrete Morse cancellation, we provide a simplified version of the existing discrete Morse-based graph reconstruction algorithm. We then introduce a simple and natural noise model and show that the aforementioned framework can correctly reconstruct a graph under this noise model, in the sense that it has the same loop structure as the hidden ground-truth graph, and is also geometrically close. We also provide some experimental results for our simplified graph-reconstruction algorithm. 1 Introduction Recovering hidden structures from potentially noisy data is a fundamental task in modern data analysis. A particular type of structure often of interest is the geometric graph-like structure. For example, given a collection of GPS trajectories, recovering the hidden road network can be modeled as reconstructing a geometric graph embedded in the plane. Given the simulated density field of dark matters in universe, finding the hidden filamentary structures is essentially a problem of geometric graph reconstruction. Different approaches have been developed for reconstructing a curve or a metric graph from input data. For example, in computer graphics, much work have been done in extracting arXiv:1803.05093v2 [cs.CG] 21 Mar 2018 1D skeleton of geometric models using the medial axis or Reeb graphs [10, 29, 20, 16, 22, 7]. -
Arxiv:2012.08669V1 [Math.CT] 15 Dec 2020 2 Preface
Sheaf Theory Through Examples (Abridged Version) Daniel Rosiak December 12, 2020 arXiv:2012.08669v1 [math.CT] 15 Dec 2020 2 Preface After circulating an earlier version of this work among colleagues back in 2018, with the initial aim of providing a gentle and example-heavy introduction to sheaves aimed at a less specialized audience than is typical, I was encouraged by the feedback of readers, many of whom found the manuscript (or portions thereof) helpful; this encouragement led me to continue to make various additions and modifications over the years. The project is now under contract with the MIT Press, which would publish it as an open access book in 2021 or early 2022. In the meantime, a number of readers have encouraged me to make available at least a portion of the book through arXiv. The present version represents a little more than two-thirds of what the professionally edited and published book would contain: the fifth chapter and a concluding chapter are missing from this version. The fifth chapter is dedicated to toposes, a number of more involved applications of sheaves (including to the \n- queens problem" in chess, Schreier graphs for self-similar groups, cellular automata, and more), and discussion of constructions and examples from cohesive toposes. Feedback or comments on the present work can be directed to the author's personal email, and would of course be appreciated. 3 4 Contents Introduction 7 0.1 An Invitation . .7 0.2 A First Pass at the Idea of a Sheaf . 11 0.3 Outline of Contents . 20 1 Categorical Fundamentals for Sheaves 23 1.1 Categorical Preliminaries . -
RGB Image-Based Data Analysis Via Discrete Morse Theory and Persistent Homology
RGB IMAGE-BASED DATA ANALYSIS 1 RGB image-based data analysis via discrete Morse theory and persistent homology Chuan Du*, Christopher Szul,* Adarsh Manawa, Nima Rasekh, Rosemary Guzman, and Ruth Davidson** Abstract—Understanding and comparing images for the pur- uses a combination of DMT for the partitioning and skeletoniz- poses of data analysis is currently a very computationally ing (in other words finding the underlying topological features demanding task. A group at Australian National University of) images using differences in grayscale values as the distance (ANU) recently developed open-source code that can detect function for DMT and PH calculations. fundamental topological features of a grayscale image in a The data analysis methods we have developed for analyzing computationally feasible manner. This is made possible by the spatial properties of the images could lead to image-based fact that computers store grayscale images as cubical cellular complexes. These complexes can be studied using the techniques predictive data analysis that bypass the computational require- of discrete Morse theory. We expand the functionality of the ments of machine learning, as well as lead to novel targets ANU code by introducing methods and software for analyzing for which key statistical properties of images should be under images encoded in red, green, and blue (RGB), because this study to boost traditional methods of predictive data analysis. image encoding is very popular for publicly available data. Our The methods in [10], [11] and the code released with them methods allow the extraction of key topological information from were developed to handle grayscale images. But publicly avail- RGB images via informative persistence diagrams by introducing able image-based data is usually presented in heat-map data novel methods for transforming RGB-to-grayscale. -
Persistence in Discrete Morse Theory
Persistence in discrete Morse theory Dissertation zur Erlangung des mathematisch-naturwissenschaftlichen Doktorgrades Doctor rerum naturalium der Georg-August-Universität Göttingen vorgelegt von Ulrich Bauer aus München Göttingen 2011 D7 Referent: Prof. Dr. Max Wardetzky Koreferent: Prof. Dr. Robert Schaback Weiterer Referent: Prof. Dr. Herbert Edelsbrunner Tag der mündlichen Prüfung: 12.5.2011 Contents 1 Introduction1 1.1 Overview................................. 1 1.2 Related work ............................... 7 1.3 Acknowledgements ........................... 8 2 Discrete Morse theory 11 2.1 CW complexes .............................. 11 2.2 Discrete vector fields .......................... 12 2.3 The Morse complex ........................... 15 2.4 Morse and pseudo-Morse functions . 17 2.5 Symbolic perturbation ......................... 20 2.6 Level and order subcomplexes .................... 23 2.7 Straight-line homotopies of discrete Morse functions . 28 2.8 PL functions and discrete Morse functions . 29 2.9 Morse theory for general CW complexes . 34 3 Persistent homology of discrete Morse functions 41 3.1 Birth, death, and persistence pairs . 42 3.2 Duality and persistence ........................ 44 3.3 Stability of persistence diagrams ................... 45 4 Optimal topological simplification of functions on surfaces 51 4.1 Topological denoising by simplification . 51 4.2 The persistence hierarchy ....................... 53 4.3 The plateau function .......................... 59 4.4 Checking the constraint ........................ 62 5 Efficient computation of topological simplifications 67 5.1 Defining a consistent total order .................... 68 iii Contents 5.2 Computing persistence pairs ..................... 68 5.3 Extracting the gradient vector field . 70 5.4 Constructing the simplified function . 70 5.5 Correctness of the algorithm ...................... 71 6 Discussion 75 6.1 Computational results ......................... 75 6.2 Relation to simplification of persistence diagrams . -
On the Topology of Discrete Strategies∗
The final version of this paper was published in The International Journal of Robotics Research, 29(7), June 2010, pp. 855–896, by SAGE Publications Ltd, http://online.sagepub.com. c Michael Erdmann On the Topology of Discrete Strategies∗ Michael Erdmann School of Computer Science Carnegie Mellon University [email protected] December 2009 Abstract This paper explores a topological perspective of planning in the presence of uncertainty, focusing on tasks specified by goal states in discrete spaces. The paper introduces strategy complexes. A strategy complex is the collection of all plans for attaining all goals in a given space. Plans are like jigsaw pieces. Understanding how the pieces fit together in a strategy complex reveals structure. That structure characterizes the inherent capabilities of an uncertain system. By adjusting the jigsaw pieces in a design loop, one can build systems with desired competencies. The paper draws on representations from combinatorial topology, Markov chains, and polyhedral cones. Triangulating between these three perspectives produces a topological language for describing concisely the capabilities of uncertain systems, analogous to concepts of reachability and controllability in other disciplines. The major nouns in this language are topological spaces. Three key theorems (numbered 1, 11, 20 in the paper) illustrate the sentences in this language: (a) Goal Attainability: There exists a strategy for attaining a particular goal from anywhere in a system if and only if the strategy complex of a slightly modified system is homotopic to a sphere. (b) Full Controllability: A system can move between any two states despite control uncertainty precisely when its strategy complex is homotopic to a sphere of dimension two less than the number of states. -
Introduction to Topological Data Analysis Julien Tierny
Introduction to Topological Data Analysis Julien Tierny To cite this version: Julien Tierny. Introduction to Topological Data Analysis. Doctoral. France. 2017. cel-01581941 HAL Id: cel-01581941 https://hal.archives-ouvertes.fr/cel-01581941 Submitted on 5 Sep 2017 HAL is a multi-disciplinary open access L’archive ouverte pluridisciplinaire HAL, est archive for the deposit and dissemination of sci- destinée au dépôt et à la diffusion de documents entific research documents, whether they are pub- scientifiques de niveau recherche, publiés ou non, lished or not. The documents may come from émanant des établissements d’enseignement et de teaching and research institutions in France or recherche français ou étrangers, des laboratoires abroad, or from public or private research centers. publics ou privés. Sorbonne Universités, UPMC Univ Paris 06 Laboratoire d’Informatique de Paris 6 Julien Tierny Introduction to Topological Data Analysis Sorbonne Universités, UPMC Univ Paris 06 Laboratoire d’Informatique de Paris 6 UMR UPMC/CNRS 7606 – Tour 26 4 Place Jussieu – 75252 Paris Cedex 05 – France Notations X Topological space ¶X Boundary of a topological space M Manifold Rd Euclidean space of dimension d s, t d-simplex, face of a d-simplex v, e, t, T Vertex, edge, triangle and tetrahedron Lk(s), St(s) Link and star of a simplex Lkd(s), Std(s) d-simplices of the link and the star of a simplex K Simplicial complex T Triangulation M Piecewise linear manifold bi i-th Betti number c Euler characteristic th ai i barycentric coordinates of a point p relatively -
Motivic Homotopy Theory
Voevodsky’s Nordfjordeid Lectures: Motivic Homotopy Theory Vladimir Voevodsky1, Oliver R¨ondigs2, and Paul Arne Østvær3 1 School of Mathematics, Institute for Advanced Study, Princeton, USA [email protected] 2 Fakult¨at f¨ur Mathematik, Universit¨atBielefeld, Bielefeld, Germany [email protected] 3 Department of Mathematics, University of Oslo, Oslo, Norway [email protected] 148 V. Voevodsky et al. 1 Introduction Motivic homotopy theory is a new and in vogue blend of algebra and topology. Its primary object is to study algebraic varieties from a homotopy theoretic viewpoint. Many of the basic ideas and techniques in this subject originate in algebraic topology. This text is a report from Voevodsky’s summer school lectures on motivic homotopy in Nordfjordeid. Its first part consists of a leisurely introduction to motivic stable homotopy theory, cohomology theories for algebraic varieties, and some examples of current research problems. As background material, we recommend the lectures of Dundas [Dun] and Levine [Lev] in this volume. An introductory reference to motivic homotopy theory is Voevodsky’s ICM address [Voe98]. The appendix includes more in depth background material required in the main body of the text. Our discussion of model structures for motivic spectra follows Jardine’s paper [Jar00]. In the first part, we introduce the motivic stable homotopy category. The examples of motivic cohomology, algebraic K-theory, and algebraic cobordism illustrate the general theory of motivic spectra. In March 2000, Voevodsky [Voe02b] posted a list of open problems concerning motivic homotopy theory. There has been so much work done in the interim that our update of the status of these conjectures may be useful to practitioners of motivic homotopy theory.