Tyler White MATH 493 Dr. Wanner

Slides:



Advertisements
Similar presentations
Stationary Probability Vector of a Higher-order Markov Chain By Zhang Shixiao Supervisors: Prof. Chi-Kwong Li and Dr. Jor-Ting Chan.
Advertisements

Alpha Shapes. Used for Shape Modelling Creates shapes out of point sets Gives a hierarchy of shapes. Has been used for detecting pockets in proteins.
B ETTI NUMBERS OF RANDOM SIMPLICIAL COMPLEXES MATTHEW KAHLE & ELIZABETH MECKE Presented by Ariel Szapiro.
Example: Given a matrix defining a linear mapping Find a basis for the null space and a basis for the range Pamela Leutwyler.
Discrete Structures Chapter 6: Set Theory
TEL-AVIV UNIVERSITY FACULTY OF EXACT SCIENCES SCHOOL OF MATHEMATICAL SCIENCES An Algorithm for the Computation of the Metric Average of Two Simple Polygons.
More Set Definitions and Proofs 1.6, 1.7. Ordered n-tuple The ordered n-tuple (a1,a2,…an) is the ordered collection that has a1 as its first element,
Proximity graphs: reconstruction of curves and surfaces
Lecture 5: Triangulations & simplicial complexes (and cell complexes). in a series of preparatory lectures for the Fall 2013 online course MATH:7450 (22M:305)
1 Constructing Convex 3-Polytopes From Two Triangulations of a Polygon Benjamin Marlin Dept. of Mathematics & Statistics McGill University Godfried Toussaint.
CHAPTER 5: CONVEX POLYTOPES Anastasiya Yeremenko 1.
08/30/00 Dinesh Manocha, COMP258 Hermite Curves A mathematical representation as a link between the algebraic & geometric form Defined by specifying the.
2. Voronoi Diagram 2.1 Definiton Given a finite set S of points in the plane , each point X of  defines a subset S X of S consisting of the points of.
Computational Geometry The art of finding algorithms for solving geometrical problems Literature: –M. De Berg et al: Computational Geometry, Springer,
Signal , Weight Vector Spaces and Linear Transformations
Signal , Weight Vector Spaces and Linear Transformations
Computational Geometry The art of finding algorithms for solving geometrical problems Literature: –M. De Berg et al: Computational Geometry, Springer,
Linear System of Equations MGT 4850 Spring 2008 University of Lethbridge.
An Algorithm for Polytope Decomposition and Exact Computation of Multiple Integrals.
Simplicial Sets, and Their Application to Computing Homology Patrick Perry November 27, 2002.
Computational Geometry The art of finding algorithms for solving geometrical problems Literature: –M. De Berg et al: Computational Geometry, Springer,
Coordinate Systems (11/4/05) It turns out that every vector space V which has a finite basis can be “realized” as one of the spaces R n as soon as we pick.
Introductory Notes on Geometric Aspects of Topology PART I: Experiments in Topology 1964 Stephen Barr (with some additional material from Elementary Topology.
MA5209 Algebraic Topology Wayne Lawton Department of Mathematics National University of Singapore S ,
Section 4.1 Vectors in ℝ n. ℝ n Vectors Vector addition Scalar multiplication.
General (point-set) topology Jundong Liu Ohio Univ.
TEL-AVIV UNIVERSITY RAYMOND AND BEVERLY SACKLER FACULTY OF EXACT SCIENCES SCHOOL OF MATHEMATICAL SCIENCES An Algorithm for the Computation of the Metric.
OR Backgrounds-Convexity  Def: line segment joining two points is the collection of points.
An Introduction to Computational Geometry: Polyhedra Joseph S. B. Mitchell Stony Brook University Chapter 6: Devadoss-O’Rourke.
Euler’s characteristic and the sphere
Raeda Naamnieh 1. Outline Subdivision of Bezier Curves Restricted proof for Bezier Subdivision Convergence of Refinement Strategies 2.
I.4 Polyhedral Theory 1. Integer Programming  Objective of Study: want to know how to describe the convex hull of the solution set to the IP problem.
Introductory Notes on Geometric Aspects of Topology PART I: Experiments in Topology 1964 Stephen Barr (with some additional material from Elementary Topology.
MATH:7450 (22M:305) Topics in Topology: Scientific and Engineering Applications of Algebraic Topology Sept 9, 2013: Create your own homology. Fall 2013.
Proving that a Valid Inequality is Facet-defining  Ref: W, p  X  Z + n. For simplicity, assume conv(X) bounded and full-dimensional. Consider.
4.5: The Dimension of a Vector Space. Theorem 9 If a vector space V has a basis, then any set in V containing more than n vectors must be linearly dependent.
What is a matroid? A matroid M is a finite set E, with a set I of subsets of E satisfying: 1.The empty set is in I 2.If X is in I, then every subset of.
Summary of the Last Lecture This is our second lecture. In our first lecture, we discussed The vector spaces briefly and proved some basic inequalities.
Linear Programming Chap 2. The Geometry of LP  In the text, polyhedron is defined as P = { x  R n : Ax  b }. So some of our earlier results should.
Topology Preserving Edge Contraction Paper By Dr. Tamal Dey et al Presented by Ramakrishnan Kazhiyur-Mannar.
1 ALGEBRAIC TOPOLOGY SIMPLICAL COMPLEX ALGEBRAIC TOPOLOGY SIMPLICAL COMPLEX Tsau Young (‘T. Y.’) Lin Institute of Data Science and Computing and Computer.
Math 3121 Abstract Algebra I Lecture 6 Midterm back over+Section 7.
1 Chapter 4 Geometry of Linear Programming  There are strong relationships between the geometrical and algebraic features of LP problems  Convenient.
Creating a cell complex = CW complex Building block: n-cells = { x in R n : || x || ≤ 1 } 2-cell = open disk = { x in R 2 : ||x || < 1 } Examples: 0-cell.
Sept 25, 2013: Applicable Triangulations.
Creating a cell complex = CW complex
Proving that a Valid Inequality is Facet-defining
Elements of Combinatorial Topology
RECORD. RECORD Subspaces of Vector Spaces: Check to see if there are any properties inherited from V:
Additive Combinatorics and its Applications in Theoretical CS
Polyhedron Here, we derive a representation of polyhedron and see the properties of the generators. We also see how to identify the generators. The results.
1.4 The Extreme Point Theorem : Geometry of a linear programming problem The set of feasible solutions to a general linear programming problem is a convex.
Polyhedron Here, we derive a representation of polyhedron and see the properties of the generators. We also see how to identify the generators. The results.
Chapter 1. Formulations (BW)
Algebraic Topology Simplical Complex
Theorems about LINEAR MAPPINGS.
e2 e1 e5 e4 e3 v1 v2 v3 v4 f The dimension of f = 2
Unstructured grid: an introduction
Affine Spaces Def: Suppose
I.4 Polyhedral Theory (NW)
Quantum Foundations Lecture 3
Quantum Foundations Lecture 2
I.4 Polyhedral Theory.
Proving that a Valid Inequality is Facet-defining
Combinatorial Topology and Distributed Computing
Preliminaries on normed vector space
1.2 Guidelines for strong formulations
e2 e1 e5 e4 e3 v1 v2 v3 v4 f The dimension of f =
Chapter 3: Simplicial Homology Instructor: Yusu Wang
1.2 Guidelines for strong formulations
Presentation transcript:

Tyler White MATH 493 Dr. Wanner Simplicial Homology Tyler White MATH 493 Dr. Wanner

Overview Introduction to Simplicial Homology Prerequisite Definitions Definition of a Simplex Boundary Maps on Simplicies

Introduction to Simplicial Homology Homology theory was originally developed with simplicies Every cubical set can be represented in terms of simplicies, but there are sets that can be represented in terms of simplicies but are not cubical sets. Cubical Homology works well for a wide range of computational problems, however it is easier to work with simplicies when working in a more abstract setting.

Prerequisite Definitions Def. A subset K of Rd is called convex if, given any two points x,y in K, the line segment [x,y] := {λx + (1 – λ)y | 0 ≤ λ ≤ 1 } joining x to y is contained in K. Def. The convex hull conv A of a subset A of Rd is the intersection of all closed and convex sets containing A. Theorem: Let V = {v0, v1, …, vn} є Rd be a finite set. Then conv(V) is the set of those x є Rd that can be written as n n x = Σ λivi, 0 ≤ λi ≤ 1, Σ λi = 1 i=0 i=0

Def A finite set V = {v0, v1, …, vn } in Rd is geometrically independent if, for any x є conv(V), the coefficients λi are unique. Proposition: Let V = {v0, v1, …, vn } є Rd. Then V is geometrically independent if and only if the set of vectors {v1 – v0, v2 – v0, …, vn – v0} is linearly independent.

Definition of a Simplex Def: Let V = {v0, v1, …, vn} be geometrically independent. The set S = conv(V) is called a simplex or, more specifically, an n-simplex spanned by the vertices v0, v1, …, vn. The number n is called the dimension of S. If V’ is a subset of V of k ≤ n vertices, the set S’ = conv(V’) is called a k-face of S Theorem: Any two n-simplices are homeomorphic. Definition: A simplicial complex S is a finite collection of simplices such that 1. every face of a simplex in S is in S, 2. the intersection of any two simplices in S is a face of each of them.

Def: Given a simplicial complex S in Rd, the union of all simplices of S is called the polytope of S and is denoted by |S|. A subset P of Rd is a polyhedron if P is the polytope of some simplicial complex S. In this case S is called a triangulation of P

Boundary Maps Restricting ourselves to Z2 we can define the boundary maps as: n δn(S) = Σ conv(V/{vi}) i=0 Proposition: δn-1δn = 0 for all n The simplicial boundary operator with integer coefficients is: n δk[v0, v1, …, vn] = Σ(-1)i[v0, v1, …, vi-1, vi+1, …, vn]