The Computational Complexity of Knot Genus and Spanning Area
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Unknot Recognition Through Quantifier Elimination
UNKNOT RECOGNITION THROUGH QUANTIFIER ELIMINATION SYED M. MEESUM AND T. V. H. PRATHAMESH Abstract. Unknot recognition is one of the fundamental questions in low dimensional topology. In this work, we show that this problem can be encoded as a validity problem in the existential fragment of the first-order theory of real closed fields. This encoding is derived using a well-known result on SU(2) representations of knot groups by Kronheimer-Mrowka. We further show that applying existential quantifier elimination to the encoding enables an UnKnot Recogntion algorithm with a complexity of the order 2O(n), where n is the number of crossings in the given knot diagram. Our algorithm is simple to describe and has the same runtime as the currently best known unknot recognition algorithms. 1. Introduction In mathematics, a knot refers to an entangled loop. The fundamental problem in the study of knots is the question of knot recognition: can two given knots be transformed to each other without involving any cutting and pasting? This problem was shown to be decidable by Haken in 1962 [6] using the theory of normal surfaces. We study the special case in which we ask if it is possible to untangle a given knot to an unknot. The UnKnot Recogntion recognition algorithm takes a knot presentation as an input and answers Yes if and only if the given knot can be untangled to an unknot. The best known complexity of UnKnot Recogntion recognition is 2O(n), where n is the number of crossings in a knot diagram [2, 6]. More recent developments show that the UnKnot Recogntion recogni- tion is in NP \ co-NP. -
Computational Topology and the Unique Games Conjecture
Computational Topology and the Unique Games Conjecture Joshua A. Grochow1 Department of Computer Science & Department of Mathematics University of Colorado, Boulder, CO, USA [email protected] https://orcid.org/0000-0002-6466-0476 Jamie Tucker-Foltz2 Amherst College, Amherst, MA, USA [email protected] Abstract Covering spaces of graphs have long been useful for studying expanders (as “graph lifts”) and unique games (as the “label-extended graph”). In this paper we advocate for the thesis that there is a much deeper relationship between computational topology and the Unique Games Conjecture. Our starting point is Linial’s 2005 observation that the only known problems whose inapproximability is equivalent to the Unique Games Conjecture – Unique Games and Max-2Lin – are instances of Maximum Section of a Covering Space on graphs. We then observe that the reduction between these two problems (Khot–Kindler–Mossel–O’Donnell, FOCS ’04; SICOMP ’07) gives a well-defined map of covering spaces. We further prove that inapproximability for Maximum Section of a Covering Space on (cell decompositions of) closed 2-manifolds is also equivalent to the Unique Games Conjecture. This gives the first new “Unique Games-complete” problem in over a decade. Our results partially settle an open question of Chen and Freedman (SODA, 2010; Disc. Comput. Geom., 2011) from computational topology, by showing that their question is almost equivalent to the Unique Games Conjecture. (The main difference is that they ask for inapproxim- ability over Z2, and we show Unique Games-completeness over Zk for large k.) This equivalence comes from the fact that when the structure group G of the covering space is Abelian – or more generally for principal G-bundles – Maximum Section of a G-Covering Space is the same as the well-studied problem of 1-Homology Localization. -
On the Number of Unknot Diagrams Carolina Medina, Jorge Luis Ramírez Alfonsín, Gelasio Salazar
On the number of unknot diagrams Carolina Medina, Jorge Luis Ramírez Alfonsín, Gelasio Salazar To cite this version: Carolina Medina, Jorge Luis Ramírez Alfonsín, Gelasio Salazar. On the number of unknot diagrams. SIAM Journal on Discrete Mathematics, Society for Industrial and Applied Mathematics, 2019, 33 (1), pp.306-326. 10.1137/17M115462X. hal-02049077 HAL Id: hal-02049077 https://hal.archives-ouvertes.fr/hal-02049077 Submitted on 26 Feb 2019 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. On the number of unknot diagrams Carolina Medina1, Jorge L. Ramírez-Alfonsín2,3, and Gelasio Salazar1,3 1Instituto de Física, UASLP. San Luis Potosí, Mexico, 78000. 2Institut Montpelliérain Alexander Grothendieck, Université de Montpellier. Place Eugèene Bataillon, 34095 Montpellier, France. 3Unité Mixte Internationale CNRS-CONACYT-UNAM “Laboratoire Solomon Lefschetz”. Cuernavaca, Mexico. October 17, 2017 Abstract Let D be a knot diagram, and let D denote the set of diagrams that can be obtained from D by crossing exchanges. If D has n crossings, then D consists of 2n diagrams. A folklore argument shows that at least one of these 2n diagrams is unknot, from which it follows that every diagram has finite unknotting number. -
Algebraic Topology
Algebraic Topology Vanessa Robins Department of Applied Mathematics Research School of Physics and Engineering The Australian National University Canberra ACT 0200, Australia. email: [email protected] September 11, 2013 Abstract This manuscript will be published as Chapter 5 in Wiley's textbook Mathe- matical Tools for Physicists, 2nd edition, edited by Michael Grinfeld from the University of Strathclyde. The chapter provides an introduction to the basic concepts of Algebraic Topology with an emphasis on motivation from applications in the physical sciences. It finishes with a brief review of computational work in algebraic topology, including persistent homology. arXiv:1304.7846v2 [math-ph] 10 Sep 2013 1 Contents 1 Introduction 3 2 Homotopy Theory 4 2.1 Homotopy of paths . 4 2.2 The fundamental group . 5 2.3 Homotopy of spaces . 7 2.4 Examples . 7 2.5 Covering spaces . 9 2.6 Extensions and applications . 9 3 Homology 11 3.1 Simplicial complexes . 12 3.2 Simplicial homology groups . 12 3.3 Basic properties of homology groups . 14 3.4 Homological algebra . 16 3.5 Other homology theories . 18 4 Cohomology 18 4.1 De Rham cohomology . 20 5 Morse theory 21 5.1 Basic results . 21 5.2 Extensions and applications . 23 5.3 Forman's discrete Morse theory . 24 6 Computational topology 25 6.1 The fundamental group of a simplicial complex . 26 6.2 Smith normal form for homology . 27 6.3 Persistent homology . 28 6.4 Cell complexes from data . 29 2 1 Introduction Topology is the study of those aspects of shape and structure that do not de- pend on precise knowledge of an object's geometry. -
On Computation of HOMFLY-PT Polynomials of 2–Bridge Diagrams
. On computation of HOMFLY-PT polynomials of 2{bridge diagrams . .. Masahiko Murakami . Joint work with Fumio Takeshita and Seiichi Tani Nihon University . December 20th, 2010 1 Masahiko Murakami (Nihon University) On computation of HOMFLY-PT polynomials December 20th, 2010 1 / 28 Contents Motivation and Results Preliminaries Computation Conclusion 1 Masahiko Murakami (Nihon University) On computation of HOMFLY-PT polynomials December 20th, 2010 2 / 28 Contents Motivation and Results Preliminaries Computation Conclusion 1 Masahiko Murakami (Nihon University) On computation of HOMFLY-PT polynomials December 20th, 2010 3 / 28 There exist polynomial time algorithms for computing Jones polynomials and HOMFLY-PT polynomials under reasonable restrictions. Computational Complexities of Knot Polynomials Alexander polynomial [Alexander](1928) Generally, polynomial time Jones polynomial [Jones](1985) Generally, #P{hard [Jaeger, Vertigan and Welsh](1993) HOMFLY-PT polynomial [Freyd, Yetter, Hoste, Lickorish, Millett, Ocneanu](1985) [Przytycki, Traczyk](1987) Generally, #P{hard [Jaeger, Vertigan and Welsh](1993) 1 Masahiko Murakami (Nihon University) On computation of HOMFLY-PT polynomials December 20th, 2010 4 / 28 Computational Complexities of Knot Polynomials Alexander polynomial [Alexander](1928) Generally, polynomial time Jones polynomial [Jones](1985) Generally, #P{hard [Jaeger, Vertigan and Welsh](1993) HOMFLY-PT polynomial [Freyd, Yetter, Hoste, Lickorish, Millett, Ocneanu](1985) [Przytycki, Traczyk](1987) Generally, #P{hard [Jaeger, Vertigan -
Mathematics and Computation
Mathematics and Computation Mathematics and Computation Ideas Revolutionizing Technology and Science Avi Wigderson Princeton University Press Princeton and Oxford Copyright c 2019 by Avi Wigderson Requests for permission to reproduce material from this work should be sent to [email protected] Published by Princeton University Press, 41 William Street, Princeton, New Jersey 08540 In the United Kingdom: Princeton University Press, 6 Oxford Street, Woodstock, Oxfordshire OX20 1TR press.princeton.edu All Rights Reserved Library of Congress Control Number: 2018965993 ISBN: 978-0-691-18913-0 British Library Cataloging-in-Publication Data is available Editorial: Vickie Kearn, Lauren Bucca, and Susannah Shoemaker Production Editorial: Nathan Carr Jacket/Cover Credit: THIS INFORMATION NEEDS TO BE ADDED WHEN IT IS AVAILABLE. WE DO NOT HAVE THIS INFORMATION NOW. Production: Jacquie Poirier Publicity: Alyssa Sanford and Kathryn Stevens Copyeditor: Cyd Westmoreland This book has been composed in LATEX The publisher would like to acknowledge the author of this volume for providing the camera-ready copy from which this book was printed. Printed on acid-free paper 1 Printed in the United States of America 10 9 8 7 6 5 4 3 2 1 Dedicated to the memory of my father, Pinchas Wigderson (1921{1988), who loved people, loved puzzles, and inspired me. Ashgabat, Turkmenistan, 1943 Contents Acknowledgments 1 1 Introduction 3 1.1 On the interactions of math and computation..........................3 1.2 Computational complexity theory.................................6 1.3 The nature, purpose, and style of this book............................7 1.4 Who is this book for?........................................7 1.5 Organization of the book......................................8 1.6 Notation and conventions..................................... -
Combinatorics for Knots
Basic notions Matroid Knot coloring and the unknotting problem Oriented matroids Spatial graphs Ropes and thickness Combinatorics for Knots J. Ram´ırezAlfons´ın Universit´eMontpellier 2 J. Ram´ırezAlfons´ın Combinatorics for Knots Basic notions Matroid Knot coloring and the unknotting problem Oriented matroids Spatial graphs Ropes and thickness 1 Basic notions 2 Matroid 3 Knot coloring and the unknotting problem 4 Oriented matroids 5 Spatial graphs 6 Ropes and thickness J. Ram´ırezAlfons´ın Combinatorics for Knots Basic notions Matroid Knot coloring and the unknotting problem Oriented matroids Spatial graphs Ropes and thickness J. Ram´ırez Alfons´ın Combinatorics for Knots Basic notions Matroid Knot coloring and the unknotting problem Oriented matroids Spatial graphs Ropes and thickness Reidemeister moves I !1 I II !1 II III !1 III J. Ram´ırez Alfons´ın Combinatorics for Knots Basic notions Matroid Knot coloring and the unknotting problem Oriented matroids Spatial graphs Ropes and thickness III II II I II II J. Ram´ırezAlfons´ın Combinatorics for Knots Basic notions Matroid Knot coloring and the unknotting problem Oriented matroids Spatial graphs Ropes and thickness III I II I J. Ram´ırez Alfons´ın Combinatorics for Knots Basic notions Matroid Knot coloring and the unknotting problem Oriented matroids Spatial graphs Ropes and thickness III I I II II I I J. Ram´ırez Alfons´ın Combinatorics for Knots Basic notions Matroid Knot coloring and the unknotting problem Oriented matroids Spatial graphs Ropes and thickness Bracket polynomial For any link diagram D define a Laurent polynomial < D > in one variable A which obeys the following three rules where U denotes the unknot : J. -
Using Reidemeister Moves for UNKNOTTING
Algorithms in Topology Today’s Plan: Review the history and approaches to two fundamental problems: Unknotting Manifold Recognition and Classification What are knots? Knots are closed loops in R3, up to isotopy Embedding Projection What are knots? We can study smooth or polygonal knots. These give equivalent theories, but polygonal knots are more natural for computation. What are knots? Knots are closed loops in space, up to isotopy We can study smooth or polygonal knots. These give equivalent theories, but polygonal knots are more natural for computation. For algorithmic purposes, we can explicitly describe a knot as a polygon in Z3. K = {(0,0,0), (1,2,0), (2,3,8), ... , (0,0,0)} We can also use several equivalent descriptions. Some Basic Questions • Can we classify knots? • Can we recognize a particular knot, such as the unknot? • How hard is it to recognize a knot? • Does topology say something new about complexity classes? • Do undecidable problems arise in the study of knots and 3- manifolds. • Does the study of topological and geometric algorithms lead to new insight into classical problems? (Isoperimetric inequalities, P=NP? NP=coNP?) Basic Questions about Manifolds • Can we classify manifolds? • Can we recognize a particular manifold, such as the sphere? • How hard is it to recognize a manifold? (What is the complexity of an algorithm) • What undecidable problems arise in the study of knots and 3- manifolds? • Does the study of topological and geometric algorithms lead to new insight into classical problems? (Isoperimetric inequalities, P=NP? NP=coNP?) Describing Surfaces and 3-Manifolds What type of surfaces and manifolds do we consider? There are three main categories to choose from: Smooth Piecewise Linear Continuous Describing Surfaces and 3-Manifolds The continuous theory allows for more pathological examples. -
Topics in Low Dimensional Computational Topology
THÈSE DE DOCTORAT présentée et soutenue publiquement le 7 juillet 2014 en vue de l’obtention du grade de Docteur de l’École normale supérieure Spécialité : Informatique par ARNAUD DE MESMAY Topics in Low-Dimensional Computational Topology Membres du jury : M. Frédéric CHAZAL (INRIA Saclay – Île de France ) rapporteur M. Éric COLIN DE VERDIÈRE (ENS Paris et CNRS) directeur de thèse M. Jeff ERICKSON (University of Illinois at Urbana-Champaign) rapporteur M. Cyril GAVOILLE (Université de Bordeaux) examinateur M. Pierre PANSU (Université Paris-Sud) examinateur M. Jorge RAMÍREZ-ALFONSÍN (Université Montpellier 2) examinateur Mme Monique TEILLAUD (INRIA Sophia-Antipolis – Méditerranée) examinatrice Autre rapporteur : M. Eric SEDGWICK (DePaul University) Unité mixte de recherche 8548 : Département d’Informatique de l’École normale supérieure École doctorale 386 : Sciences mathématiques de Paris Centre Numéro identifiant de la thèse : 70791 À Monsieur Lagarde, qui m’a donné l’envie d’apprendre. Résumé La topologie, c’est-à-dire l’étude qualitative des formes et des espaces, constitue un domaine classique des mathématiques depuis plus d’un siècle, mais il n’est apparu que récemment que pour de nombreuses applications, il est important de pouvoir calculer in- formatiquement les propriétés topologiques d’un objet. Ce point de vue est la base de la topologie algorithmique, un domaine très actif à l’interface des mathématiques et de l’in- formatique auquel ce travail se rattache. Les trois contributions de cette thèse concernent le développement et l’étude d’algorithmes topologiques pour calculer des décompositions et des déformations d’objets de basse dimension, comme des graphes, des surfaces ou des 3-variétés. -
25 HIGH-DIMENSIONAL TOPOLOGICAL DATA ANALYSIS Fr´Ed´Ericchazal
25 HIGH-DIMENSIONAL TOPOLOGICAL DATA ANALYSIS Fr´ed´ericChazal INTRODUCTION Modern data often come as point clouds embedded in high-dimensional Euclidean spaces, or possibly more general metric spaces. They are usually not distributed uniformly, but lie around some highly nonlinear geometric structures with nontriv- ial topology. Topological data analysis (TDA) is an emerging field whose goal is to provide mathematical and algorithmic tools to understand the topological and geometric structure of data. This chapter provides a short introduction to this new field through a few selected topics. The focus is deliberately put on the mathe- matical foundations rather than specific applications, with a particular attention to stability results asserting the relevance of the topological information inferred from data. The chapter is organized in four sections. Section 25.1 is dedicated to distance- based approaches that establish the link between TDA and curve and surface re- construction in computational geometry. Section 25.2 considers homology inference problems and introduces the idea of interleaving of spaces and filtrations, a funda- mental notion in TDA. Section 25.3 is dedicated to the use of persistent homology and its stability properties to design robust topological estimators in TDA. Sec- tion 25.4 briefly presents a few other settings and applications of TDA, including dimensionality reduction, visualization and simplification of data. 25.1 GEOMETRIC INFERENCE AND RECONSTRUCTION Topologically correct reconstruction of geometric shapes from point clouds is a classical problem in computational geometry. The case of smooth curve and surface reconstruction in R3 has been widely studied over the last two decades and has given rise to a wide range of efficient tools and results that are specific to dimensions 2 and 3; see Chapter 35. -
On the Treewidth of Triangulated 3-Manifolds
On the Treewidth of Triangulated 3-Manifolds Kristóf Huszár Institute of Science and Technology Austria (IST Austria) Am Campus 1, 3400 Klosterneuburg, Austria [email protected] https://orcid.org/0000-0002-5445-5057 Jonathan Spreer1 Institut für Mathematik, Freie Universität Berlin Arnimallee 2, 14195 Berlin, Germany [email protected] https://orcid.org/0000-0001-6865-9483 Uli Wagner Institute of Science and Technology Austria (IST Austria) Am Campus 1, 3400 Klosterneuburg, Austria [email protected] https://orcid.org/0000-0002-1494-0568 Abstract In graph theory, as well as in 3-manifold topology, there exist several width-type parameters to describe how “simple” or “thin” a given graph or 3-manifold is. These parameters, such as pathwidth or treewidth for graphs, or the concept of thin position for 3-manifolds, play an important role when studying algorithmic problems; in particular, there is a variety of problems in computational 3-manifold topology – some of them known to be computationally hard in general – that become solvable in polynomial time as soon as the dual graph of the input triangulation has bounded treewidth. In view of these algorithmic results, it is natural to ask whether every 3-manifold admits a triangulation of bounded treewidth. We show that this is not the case, i.e., that there exists an infinite family of closed 3-manifolds not admitting triangulations of bounded pathwidth or treewidth (the latter implies the former, but we present two separate proofs). We derive these results from work of Agol and of Scharlemann and Thompson, by exhibiting explicit connections between the topology of a 3-manifold M on the one hand and width-type parameters of the dual graphs of triangulations of M on the other hand, answering a question that had been raised repeatedly by researchers in computational 3-manifold topology. -
CMSC 754: Lecture 18 Introduction to Computational Topology
CMSC 754 Ahmed Abdelkader (Guest) CMSC 754: Lecture 18 Introduction to Computational Topology The introduction presented here is mainly based on Edelsbrunner and Harer's Computational Topology, while drawing doses of inspiration from Ghrist's Elementary Applied Topology; it is mostly self-contained, at the expense of brevity and less rigor here and there to fit the short span of one or two lectures. An excellent textbook to consult is Hatcher's Algebraic Topology, which is freely available online: https://pi.math.cornell.edu/~hatcher/AT/AT.pdf. 2 What is Topology? We are all familiar with Euclidean spaces, especially the plane R where we 3 draw our figures and maps, and the physical space R where we actually live and move about. Our direct experiences with these spaces immediately suggest a natural metric structure which we can use to make useful measurements such as distances, areas, and volumes. Intuitively, a metric recognizes which pairs of locations are close or far. In more physical terms, a metric relates to the amount of energy it takes to move a particle of mass from one location to another. If we are able to move particles between a pair of locations, we say the locations are connected, and if the locations are close, we say they are neighbors. In every day life, we frequently rely more upon the abstract notions of neighborhoods and connectedness if we are not immediately concerned with exact measurements. For instance, it is usually not a big deal if we miss the elevator and opt to take the stairs, or miss an exit on the highway and take the next one; these pairs of paths are equivalent if we are not too worried about running late to an important appointment.