1 1.Write down the permutations that are the automorphisms of this graph. 2.Write the cycle structure notation for each of the automorphisms. 3.How many.

Slides:



Advertisements
Similar presentations
Lecture 15. Graph Algorithms
Advertisements

CS1022 Computer Programming & Principles
In a previous lecture, this dictionary resulted after the first phase 1 pivot: X0 = X1 - 1 X2 + 1 X3 X4 = X1 + 3 X2 + 1 X
1 Code generation Our book's target machine (appendix A): opcode source1, source2, destination add r1, r2, r3 addI r1, c, r2 loadI c, r2 load r1, r2 loadAI.
1 CS 201 Compiler Construction Machine Code Generation.
1 If you still are having problems getting your program to work, send me for help. You will need to use it later in the term to start Programming.
22C:19 Discrete Math Graphs Fall 2014 Sukumar Ghosh.
Approximation, Chance and Networks Lecture Notes BISS 2005, Bertinoro March Alessandro Panconesi University La Sapienza of Rome.
Graph Isomorphism Algorithms and networks. Graph Isomorphism 2 Today Graph isomorphism: definition Complexity: isomorphism completeness The refinement.
CompSci 102 Discrete Math for Computer Science April 19, 2012 Prof. Rodger Lecture adapted from Bruce Maggs/Lecture developed at Carnegie Mellon, primarily.
Algorithms and Data Structures Lecture 4. Agenda: Trees – fundamental notions, variations Binary search tree.
Discrete Structures Lecture 13: Trees Ji Yanyan United International College Thanks to Professor Michael Hvidsten.
CS774. Markov Random Field : Theory and Application Lecture 17 Kyomin Jung KAIST Nov
Great Theoretical Ideas in Computer Science.
Computability and Complexity 23-1 Computability and Complexity Andrei Bulatov Search and Optimization.
Graph Algorithms: Minimum Spanning Tree We are given a weighted, undirected graph G = (V, E), with weight function w:
MATH 310, FALL 2003 (Combinatorial Problem Solving) Lecture 15, Friday, October 3.
2-Layer Crossing Minimisation Johan van Rooij. Overview Problem definitions NP-Hardness proof Heuristics & Performance Practical Computation One layer:
Job Scheduling Lecture 19: March 19. Job Scheduling: Unrelated Multiple Machines There are n jobs, each job has: a processing time p(i,j) (the time to.
Chapter 4: Straight Line Drawing Ronald Kieft. Contents Introduction Algorithm 1: Shift Method Algorithm 2: Realizer Method Other parts of chapter 4 Questions?
B + -Trees (Part 2) Lecture 21 COMP171 Fall 2006.
MATH 310, FALL 2003 (Combinatorial Problem Solving) Lecture 11, Wednesday, September 24.
1 Separator Theorems for Planar Graphs Presented by Shira Zucker.
Design and Analysis of Algorithms Minimum Spanning trees
03/01/2005Tucker, Sec Applied Combinatorics, 4th Ed. Alan Tucker Section 3.1 Properties of Trees Prepared by Joshua Schoenly and Kathleen McNamara.
MATH 310, FALL 2003 (Combinatorial Problem Solving) Lecture 10, Monday, September 22.
Minimum Spanning Trees. Subgraph A graph G is a subgraph of graph H if –The vertices of G are a subset of the vertices of H, and –The edges of G are a.
FAST FREQUENT FREE TREE MINING IN GRAPH DATABASES Marko Lazić 3335/2011 Department of Computer Engineering and Computer Science,
Data Structures and Algorithms Graphs Minimum Spanning Tree PLSD210.
May 5, 2015Applied Discrete Mathematics Week 13: Boolean Algebra 1 Dijkstra’s Algorithm procedure Dijkstra(G: weighted connected simple graph with vertices.
“On an Algorithm of Zemlyachenko for Subtree Isomorphism” Yefim Dinitz, Alon Itai, Michael Rodeh (1998) Presented by: Masha Igra, Merav Bukra.
May 1, 2002Applied Discrete Mathematics Week 13: Graphs and Trees 1News CSEMS Scholarships for CS and Math students (US citizens only) $3,125 per year.
Multiway Trees. Trees with possibly more than two branches at each node are know as Multiway trees. 1. Orchards, Trees, and Binary Trees 2. Lexicographic.
Foundations of Discrete Mathematics
Straight line drawings of planar graphs – part II Roeland Luitwieler.
CSCI 115 Chapter 7 Trees. CSCI 115 §7.1 Trees §7.1 – Trees TREE –Let T be a relation on a set A. T is a tree if there exists a vertex v 0 in A s.t. there.
Lecture 8 Tree.
Succinct Data Structures Ian Munro University of Waterloo Joint work with David Benoit, Andrej Brodnik, D, Clark, F. Fich, M. He, J. Horton, A. López-Ortiz,
1 Bob and Sue solved this by hand: Maximize x x 2 subject to 1 x x 2 ≤ x x 2 ≤ 4 x 1, x 2 ≥ 0 and their last dictionary was: X1.
Graph Colouring L09: Oct 10. This Lecture Graph coloring is another important problem in graph theory. It also has many applications, including the famous.
Agenda Review: –Planar Graphs Lecture Content:  Concepts of Trees  Spanning Trees  Binary Trees Exercise.
Speeding Up Enumeration Algorithms with Amortized Analysis Takeaki Uno (National Institute of Informatics, JAPAN)
2 Office hours: MWR 4:20-5:30 inside or just outside Elliott 162- tell me in class that you would like to attend. For those of you who cannot stay: MWR:
LIMITATIONS OF ALGORITHM POWER
Chapter 10: Trees A tree is a connected simple undirected graph with no simple circuits. Properties: There is a unique simple path between any 2 of its.
Trees Thm 2.1. (Cayley 1889) There are nn-2 different labeled trees
CSCU elections voting is available over the next 3 days during the following times: 10:00am - 3:30pm Monday, Oct. 1 9:30am - 3:30pm Tuesday, Oct. 2 9:30am.
Great Theoretical Ideas in Computer Science for Some.
Algorithms for hard problems Parameterized complexity Bounded tree width approaches Juris Viksna, 2015.
CIS 540 Principles of Embedded Computation Spring Instructor: Rajeev Alur
Planning and Scheduling.  A job can be made up of a number of smaller tasks that can be completed by a number of different “processors.”  The processors.
Discrete Mathematics Chapter 10 Trees.
Senior Project Board Implementation of the Solution to the Conjugacy Problem in Thompson’s Group F by Nabil Hossain Advisers: James Belk & Robert McGrail.
Applied Discrete Mathematics Week 15: Trees
CS1022 Computer Programming & Principles
Redraw these graphs so that none of the line intersect except at the vertices B C D E F G H.
Source Code for Data Structures and Algorithm Analysis in C (Second Edition) – by Weiss
Parameterized complexity Bounded tree width approaches
12. Graphs and Trees 2 Summary
Character-Based Phylogeny Reconstruction
Algorithms and networks
Algorithms and networks
Kruskal’s Algorithm for finding a minimum spanning tree
And the Final Subject is…
Σ 2i = 2 k i=0 CSC 225: Proof of the Day
Output Sensitive Enumeration
Give the parent, queue, BFI (breadth first index), and level arrays when BFS is applied to this graph starting at vertex 0. Process the neighbours of each.
Switching Lemmas and Proof Complexity
Complexity Theory: Foundations
Presentation transcript:

1 1.Write down the permutations that are the automorphisms of this graph. 2.Write the cycle structure notation for each of the automorphisms. 3.How many independent sets of order 2 does it have?

2 Two independent sets S and T are equivalent if there is an automorphism of G mapping the vertices of S to those in T. Given a graph G with automorphism group order g and an independent set S such that G has k automorphisms mapping the independent set S to itself, there will be g/k different independent sets of the graph that correspond to S. Usually the minimum of these is chosen to be the canonical representative of its equivalence class.

3 1.Which automorphisms map this independent set to itself? 2.Which other independent sets are equivalent to it?

4 4 * 3 = 12

5 Sorting these lexicographically: {0, 3} < {1,4} < {2,5}

6 1.Which automorphisms map this independent set to itself? 2.Which other independent sets are equivalent to it?

7 2 * 6 = 12

8 Sorting these lexicographically: {0, 2} < {0, 4} < {1, 3} < {1, 5} < {2,4} < {3,5}

9 The independent sets of order two fall into two equivalence classes. We could choose the minimum one in each equivalence class as the representative for its class:

10 For each of these two embeddings, apply clockwise BFS starting at the red vertex with first child the green vertex and direction clockwise.

11 0 (2): (3): (3): (3): (3): (2): (2): (2): (3): (3): (3): (2): 3 4 Lexicographically smaller.

12 0 (2): (3): (3): (3): (3): (2): 3 4 Lexicographically smaller. 0 (3): (3): (2): (3): (3): (2): 1 4

13 0 (2): (3): (3): (3): (3): (2): 3 4 Lexicographically smaller 0 (2): (3): (3): (3): (3): (2): 3 4

14 0 (2): (3): (3): (3): (3): (2): (2): (3): (3): (3): (3): (2): 3 4 Face walking: Face walking:

15 0 (2): (3): (3): (3): (3): (2): (2): (3): (3): (3): (3): (2): 3 4 Automorphism since the same.

16 To get the canonical form of an embedding: For each of the n choices of the root r do For each of the degree(r) choices of the first child f do For each choice d of direction do Do a clockwise BFS with root r first child f and direction d Choose the lex. smallest as the canonical form.

17 To get automorphisms of an embedding: For each of the n choices of the root r do For each of the degree(r) choices of the first child f do For each choice d of direction do Do a clockwise BFS with root r first child f and direction d If the rotation system matches the canonical form the BFI labelling is an automorphism.

Alan Turing Celebration Lecture Biological Evolution as a Form of Learning Leslie Valiant, CC and Applied Math., Harvard Wed. Oct 10, 3:30pm, ECS 124 Living organisms function according to protein circuits. Darwin's theory of evolution suggests that these circuits have evolved through variation guided by natural selection. However, the question of which circuits can so evolve in realistic population sizes and within realistic numbers of generations has remained essentially unaddressed.

We suggest that computational learning theory offers the framework for investigating this question, of how circuits can come into being adaptively from experience, without a designer. We formulate evolution as a form of learning from examples. The targets of the learning process are the functions of highest fitness. The examples are the experiences. The learning process is constrained so that the feedback from the experiences is Darwinian. We formulate a notion of evolvability that distinguishes function classes that are evolvable with polynomially bounded resources from those that are not. The dilemma is that if the function class, say for the expression levels of proteins in terms of each other, is too restrictive, then it will not support biology, while if it is too expressive then no evolution algorithm will exist to navigate it.

CSC 482/582: Project proposal slides deadline extended to Oct. 11. You should be working on a literature review and planning your methodology. Assignment #1 has been posted: due Thurs. Oct. 18 at the beginning of class. Assignments may be handed in up to 5 business days after the official due date with a 2% penalty for each business day that the assignment is overdue.

Graph Isomorphism The graph isomorphism problem has no known polynomial time algorithm which works for an arbitrary graph. Canonical form: If two graphs are isomorphic, their canonical forms must be the same, otherwise, they must be different. For trees and planar graphs, a canonical form can be computed in polynomial time. 21

The ( ) Canonical Form for a Tree 22

Label each vertex with the string ( ). While more than two vertices remain do Locate all the leaves. Remove each leaf placing its label within the ( and ) of the parent so that within the ( ) of the parent, the strings representing the children are sorted. End while If there is one vertex left, the canonical form of the tree is the label of this vertex. If there are two vertices u and v left, the canonical form of T is label(u) concatenated with label(v) where label(u) ≤ label(v). 23

( ) Label each vertex with ( ). 24

( ) Identify the leaves. 25

(( )) (( )( )( )) ( ) (( )( )( )) (( )( )) (( )) Put labels of leaves inside ( ) of their neighbours making sure strings are in sorted order. 26

(( )) (( )( )( )) ( ) (( )( )( )) (( )( )) (( )) 27

(( )) (( )( )( )) ( ) (( )( )( )) (( )( )) (( )) Identify the leaves. 28

( (( )) ( ) ( ) ( )) ( (( )( )) (( )( )) ( ) ) ((( )( )( ))) Put labels of leaves inside ( ) of their neighbours making sure strings are in sorted order. 29

( (( )) ( ) ( ) ( )) ( (( )( )) (( )( )) ( ) ) ((( )( )( ))) 30

( (( )) ( ) ( ) ( )) ( (( )( )) (( )( )) ( ) ) ((( )( )( ))) 31

( ( (( )( )) (( )( )) ( ) ) ( (( )) ( ) ( ) ( )) (( )( )( )) ) CANONICAL FORM FOR THIS TREE: ( ( (( )( )) (( )( )) ( ) ) ( (( )) ( ) ( ) ( ))(( )( )( )) ) 32

( ( (( )( )) (( )( )) ( ) ) ( (( )) ( ) ( ) ( )) (( )( )( )) ) To reconstruct the tree: Factor what is in each ( ) into minimal well- parenthesized strings. 33

( (( )( )) (( )( )) ( ) ) To reconstruct the tree: Add a leaf for each well-parenthesized string w with label w. ( (( )) ( ) ( ) ( )) (( )( )( )) 34

( (( )( )) (( )( )) ( ) ) ( (( )) ( ) ( ) ( ) ) ( ( ) ( ) ( ) ) Factor what is in each ( ) into minimal well- parenthesized strings. 35

( (( )( )) (( )( )) ( ) ) ( (( )) ( ) ( ) ( ) ) ( ( ) ( ) ( ) ) Add a leaf for each one. 36

With the labels on them. ( ( ) ) ( ) (( )( )) ( ) 37

( ( ) ) ( ) (( )( )) ( ) 38

( ) 39

40