What is topology? - Michael Robinsondrmichaelrobinson.net/sheaftutorial/20150825_tutorial_2.pdf ·...
Transcript of What is topology? - Michael Robinsondrmichaelrobinson.net/sheaftutorial/20150825_tutorial_2.pdf ·...
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Michael Robinson
What is topology?
SIMPLEX Program
© 2015 Michael Robinson This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
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Michael Robinson2
Mathematical dependency tree
Sheaves
Cellular sheaves
Linear algebra
Set theory
Calculus
Topology
Homology
SimplicialComplexes
CW complexes
Sheaf cohomology
AbstractSimplicialComplexes
de Rham cohomology(Stokes' theorem)
Manifolds
Lecture 2
Lectures 3, 4
Lectures 5, 6
Lectures 7, 8
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Michael Robinson3
Session objectives● What is a topological space?
– What kinds of spaces are there?
● How to represent topological spaces effectively for data fusion problems– Simplicial complex models, and how they interface
with topology
● Encoding data as a topological space
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Michael Robinson4
What is topology?
=
Topology is the study of spaces under continuous deformations
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Michael Robinson5
What is topology?
Not this!This is TopoGRAPHY!
(Thanks USGS!)
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Michael Robinson6
Topological imaging● Cellphone used to record signal level of 802.11 access
points near several apartment buildings● Signal level (dBm) and station MAC address recorded
periodically– Uniquely identified 52 WAPs
● Random projection to 2d
(image courtesy of Google)
Randomprojection
Software credit: Daniel Muellner and Mikael Vejdemo-Johansson
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Michael Robinson7
Topological imaging● Goal: measure environment and targets with minimal
sensing and opportunistic sources● Key theoretical guarantees proven● First generation algorithms
– Simulated extensively– Validated experimentally
(image courtesy of Google)
Randomprojection
Software credit: Daniel Muellner and Mikael Vejdemo-Johansson
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Michael Robinson8
What kind of space?General topological space● More general
(obviously!)● Very traditional● But admits
pathological cases
Manifolds● Very restricted, but
this aids estimation● Calculus!
Cellular space● Less general (and less
pathological)● Good for computation● Less general (and less
expressive)● More constraints
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Michael Robinson9
Some (mostly) topological spaces
Preorder
Undirectedhypergraph
Abstractsimplicialcomplex
CW complex
Simplicialcomplex
Cell complex
Stratified manifold
Manifold
Topological space
Δ-complexRegular CWcomplex
Flagcomplex
Undirectedgraph
“is a”“can be made into a”
Morse theory
Alexandrovtopology
Geometric
realization
Topo
logy
Com
b inator ics
Note to category theorists: This diagram does NOT commute!
Inclusion structure
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Michael Robinson10
Spaces of interest for this tutorial
Preorder
Undirectedhypergraph
Abstractsimplicialcomplex
CW complex
Simplicialcomplex
Cell complex
Stratified manifold
Manifold
Topological space
Δ-complexRegular CWcomplex
Flagcomplex
Undirectedgraph
“is a”“can be made into a”
Morse theory
Alexandrovtopology
Geometric
realization
Topo
logy
Com
b inator ics
Note to category theorists: This diagram still does NOT commute!
Inclusion structure
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Michael Robinson11
High level intuition first...… then the precise details
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Michael Robinson12
Simplicial complexes● A simplicial complex is a collection of vertices and ...
v1
v3
v2
v4
A vertex represents an individual measurement taken by a sensor
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Michael Robinson13
Simplicial complexes● … edges (pairs of vertices) and ...
v1
v3
v2
v4
{v1,v
2}
{v1,v
3}
{v1,v
2}
{v2,v
3}
{v3,v
4}
An edge represents the fact that two measurements should be tested for consistency
Usually people call this a graph; I will too
NB: I got lazy… v1 should be {v
1}
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Michael Robinson14
Simplicial complexes● ... higher dimensional simplices (tuples of vertices)● Whenever you have a simplex, you have all subsets,
called faces, too.
v1
v3
v2
v4
{v1,v
2,v
3}
{v1,v
3}
{v1,v
2}
{v2,v
3}
{v3,v
4}
A simplex represents that several measurements should be tested for consistency
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Michael Robinson15
Undirected hypergraphs● ... might have simplices that are missing some faces● … and we call simplices hyperedges as a reminder
v1
v3
v2
v4
{v1,v
2,v
3}
{v1,v
3}
{v1,v
2}
{v2,v
3}
{v3,v
4}
A hyperedge still represents that several measurements should be tested for consistency
X
X
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Michael Robinson16
Flag complexes● All cliques – an edge between every pair of vertices
– are simplices, too
v1
v3
v2
v4
{v1,v
2,v
3}
{v1,v
3}
{v1,v
2}
{v2,v
3}
{v3,v
4}
v1
v3
v2
v4
{v1,v
2}
{v1,v
3}
{v1,v
2}
{v2,v
3}
{v3,v
4}
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Michael Robinson17
Helpful taxonomy
Undirectedhypergraph
Abstractsimplicialcomplex
Flagcomplex
Undirectedgraph
Simplices are defined by their vertices, simplices always have all their faces
Vertices and edges only
Hyperedges defined by vertices, but other than that, anything goes...
All cliques are simplices
Easier to compute invariantsHarder to compute invariants
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Michael Robinson
Now the details!
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Michael Robinson19
Abstract Simplicial ComplexGiven a set V, which we'll call vertices, an abstract simplicial complex is a collection X of subsets of V satisfying the property:
If a ∈ X and b ⊆ a then b ∈ X.
(If you ignore this property, then X is a hypergraph.)
Elements of X are called simplices
● A simplex a = {a0, a
1, … a
n} is said to have dimension n
● A maximal simplex is called a facet or a toplex● Dimension 0 = vertex, dimension 1 = edge
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Michael Robinson20
Flag complexes and graphs● If X only has vertices and edges, it's called a graph● A clique in a graph is a subgraph in which every pair of
vertices has an edge between them● If all cliques are also simplices, then X is a flag complex
● Flag complexes are often better choices than graphs because they capture higher dimensional (multi-way) relationships
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Michael Robinson21
Topology● A way to describe neighborhoods of simplices● This is really good for describing where a piece of
data is valid – near the sensor, usually● There's a natural way to do this completely generally,
and it's very handy mathematically– It leads to computational trickiness, though– We'll use the Alexandrov topology when needed (usually
for hypergraphs) and avoid the general definition● Fortunately, there's usually a way to go between
topology and simplicial complexes
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Topology, preciselyIf X is a set, then a topology T on X is a collection of subsets of X satisfying four axioms:
1. The empty set is in T
2. X is in T
3. If U is in T and V is in T then U ⋂ V is in T
4. All unions of elements of T are in T
The elements of T are called the open sets for the topology
The pair (X,T) is called a topological space
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Michael Robinson23
Stars over simplices● The star over a simplex is that simplex along with all
higher dimensional ones containing it
{v1,v
2,v
3}
{v1,v
3}
{v1,v
2}
{v2,v
3}
{v3,v
4}
Star over v1:
{v1}
{v3}
{v4}
{v2}
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Michael Robinson24
Stars over simplices● The star over a simplex is that simplex along with all
higher dimensional ones containing it
{v1}
{v3}
{v1,v
2,v
3}
{v1,v
3}
{v1,v
2}
{v2,v
3}
{v3,v
4}
Star over {v1,v
2}:
{v3}
{v4}
{v2}
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Michael Robinson25
Stars over simplices● The star over a simplex is that simplex along with all
higher dimensional ones containing it● Proposition: The collection of all possible stars and all
possible unions of stars forms a topology for the set of simplices
● It's called the Alexandrov topology, and it's usually the correct one for data integration problems
● This works for hypergraphs, not just abstract simplicial complexes
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Beyond hypergraphs● Sometimes you need to duplicate simplices…
– This isn't a problem for the topology, but it violates our combinatorial construction thus far
– This usually leads to multigraphs and multihypergraphs● The Alexandrov topology still works, though, and this
causes no computational problems in the end
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Michael Robinson27
Beyond hypergraphs● What's important is the set of attachments between
simplices– These form a partial order for the simplices, via set
inclusion– Vertices are subsets of edges, etc.
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Starting with a topology● There's usually a physical region associated to a
sensor where it's gathering information– It's usually good practice to assert that this is an open
set in some appropriate topology, often the usual topology for Euclidean space
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Michael Robinson29
Recall: Multi-INT tracking● Each sensor has a region associated to it
– Should be an open set in Euclidean space for these sensors; a good guess for physical sensors
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Topologizing the sensor space● We now know this is a simplicial complex!● But how did we get it?
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Michael Robinson31
Nerve of an open coverLet (X,T) be a topological space and � ={U
1,U
2,…} be
a collection of open sets whose union is X (an open cover)
The nerve N(� ) is the abstract simplicial complex with● The set of vertices given by �, and
● Whenever Ua ⋂ U
b ⋂ … is nonempty, {U
a, U
b, …} is
a simplex in N(� )
A crucial theorem (Leray theorem) links N(� ) to the topological structure of (X,T)
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Michael Robinson32
Next up...● Lunch!● Next lecture: Define sheaves!
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Michael Robinson33
Further reading...● Allen Hatcher, Algebraic Topology, Cambridge University Press, 2002
– http://www.math.cornell.edu/~hatcher/AT/AT.pdf● James Munkres, Topology, Pearson, 2000
● Gurjeet Singh, Facundo Mémoli and Gunnar Carlsson, “Topological Methods for the Analysis of High Dimensional Data Sets and 3D Object Recognition,” Eurographics Symposium on Point Based Graphics, European Association for Computer Graphics, 2007, pp. 91–100
● Gunnar Carlsson, “Topology and Data,” Bull. Amer. Math. Soc. 46 (2009), 255–308
● Dai Tamaki, “Cellular stratified spaces”
– http://arxiv.org/abs/1106.3772– http://arxiv.org/abs/1111.4774
● Michael Robinson and Robert Ghrist “Topological localization via signals of opportunity,” IEEE Trans. Sig. Proc, Vol. 60, No. 5, May 2012.