Strongly Connected Components in graphs, formal proof of Tarjan1972 algorithm
|
|
- Alexis Anne Mills
- 5 years ago
- Views:
Transcription
1 Strongly Connected Components in graphs, formal proof of Tarjan972 algorithm Inria Sophia,
2 Plan motivation algorithm pre-/post-conditions imperative programs conclusion.. joint work (in progress) with Ran Chen 2
3 Motivation nice algorithms should have simple formal proofs to be fully published in articles or journals how to publish formal proofs? Coq proofs seem to me unreadable (by normal human) Why3 allows mix of automatic and interactive proofs first-order logic is easy to understand algorithms on graphs = a good testbed 3
4 A one-pass lineartime algorithm
5 The algorithm (/3) depth-first search algorithm with pushing non visited vertices into a working stack and computing oldest vertex reachable by at most a single «back-edge» when that oldest vertex is equal to currently visited vertex, a new strongly connected component is in the working stack on top of current vertex. then pop working stack until currently visited vertex
6 The algorithm (2/3) les valeurs successives de la pile sens croissant du rang
7 The algorithm (2/3) les valeurs successives de la pile sens croissant du rang
8 The algorithm (2/3) les valeurs successives de la pile sens croissant du rang
9 The algorithm (2/3) les valeurs successives de la pile sens croissant du rang 9
10 The algorithm (3/3) print each component on a line
11 Proof in algorithms book (/2) consider the spanning trees (forest) tree structure of strongly connected components 2-3 lemmas about ancestors in spanning trees LOWLINK(x) =min({num[x]} [ {num[y] x =),! y ^ x and y are in same connected component} )
12 Proof in algorithms book (2/2) give the program proof program that part of the proof is very informal 2
13 The algorithm (bis) les valeurs successives de la pile sens croissant du rang LOWLINK(x) =min({num[x]} [ {num[y] x =),! y ^ x and y are in same connected component} ) 3
14 The algorithm (ter) les valeurs successives de la pile sens croissant du rang LOWLINK(x) =min({rank[y] x =),! y ^ x and y are in same connected component} )
15 Our program (/3) a functional version with lists and finite sets the working stack is a list
16 Our program (/3) a functional version with lists and finite sets the working stack is a list s s 2 x s 6
17 Our program (2/3) blacks, grays are sets of vertices; sccs is a set of sets of vertices naming conventions: x, y, z for vertices; b for black sets; s for stacks; cc for connected components; sccs for sets of connected components 7
18 Our program (3/3) 8
19 Pre-/Post-conditions
20 Pre/Post-conditions (/3)
21 Pre/Post-conditions (2/3) stack s m y x x sccs sccs n blacks b m apple rank y stack m apple rank x stack
22 Pre/Post-conditions (3/3)
23 Graphs
24 Paths
25 Invariants (/) blacks \ grays = ; elements s = grays [ blacks (set of sccs) (set of sccs) blacks
26 Invariants (2/) s sccs cc cc2 increasing rank ccn blacks grays
27 Invariants (3/)
28 Invariants (/) s sccs cc cc2 increasing rank ccn blacks grays
29 Assertions
30 Assertions Coq proof: there exists x 0,y 0 and x 0,y 0 y 0 2 s3 =stack x 0 = x x 0 6= x with impossible because m apple rank y s < rank x s impossible because crossedge x 0 2 s2 ^ y 0 62 s2 ^ edge x 0 y 0 are in same strongly connected component as x y 0 2 sccs impossible because sccs disjoint from stack y 0 is white x 0 = x x 0 6= x impossible because successors are black impossible because no black to white
31 Pre/Post-conditions (/3)
32 Full proof full proof is at see the file why3session.html proof: 8 lines (38 lemmas) including the program texts. 82 proof obligations all proved automatically by Alt-Ergo (.30), CVC3 (2..), CVC (.), Eprover (.9), Spass (3.),Yices (.0.) except of manually checked by Coq (8.6) Coq proofs are 20 lines ( )
33 Towards imperative program
34 Assertions
35 Assertions
36 Assertions
37 Missing implementation of graphs vertices as integers in an array successors as lists for every vertex see
38 Conclusion
39 Conclusion readable proofs? simple algorithms should have simple proofs to be shown with a good formal precision compare with other proof systems (without automatic provers?) further algorithms (in next talks?) graphs represented with arrays + lists topological sort, articulation points, scck, ssct Why3 is a beautiful system but not so easy to use! 39
Formal Proofs of Tarjan s Algorithm in Why3, Coq, and Isabelle
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 Formal Proofs of Tarjan s Algorithm in Why3,
More informationFinding Strongly Connected Components
Yufei Tao ITEE University of Queensland We just can t get enough of the beautiful algorithm of DFS! In this lecture, we will use it to solve a problem finding strongly connected components that seems to
More informationSimple proofs of simple programs in Why3
Simple proofs of simple programs in Why3 Jean-Jacques Lévy State Key Laboratory for Computer Science, Institute of Software, Chinese Academy of Sciences & Inria Abstract We want simple proofs for proving
More informationDesign and Analysis of Algorithms
Design and Analysis of Algorithms CS 5311 Lecture 19 Topological Sort Junzhou Huang, Ph.D. Department of Computer Science and ngineering CS5311 Design and Analysis of Algorithms 1 Topological Sort Want
More informationarxiv: v1 [cs.lo] 29 Oct 2018
Formal Proofs of Tarjan s Algorithm in Why3, COQ, and Isabelle arxiv:1810.11979v1 [cs.lo] 29 Oct 2018 Ran Chen 1, Cyril Cohen 2, Jean-Jacques Lévy 3, Stephan Merz 4, and Laurent Théry 2 1 Iscas, Beijing,
More informationStrongly Connected Components
Strongly Connected Components Let G = (V, E) be a directed graph Write if there is a path from to in G Write if and is an equivalence relation: implies and implies s equivalence classes are called the
More informationCISC 320 Introduction to Algorithms Fall Lecture 15 Depth First Search
IS 320 Introduction to lgorithms all 2014 Lecture 15 epth irst Search 1 Traversing raphs Systematic search of every edge and vertex of graph (directed or undirected) fficiency: each edge is visited no
More informationDeductive Program Verification with Why3, Past and Future
Deductive Program Verification with Why3, Past and Future Claude Marché ProofInUse Kick-Off Day February 2nd, 2015 A bit of history 1999: Jean-Christophe Filliâtre s PhD Thesis Proof of imperative programs,
More informationW4231: Analysis of Algorithms
W4231: Analysis of Algorithms 10/21/1999 Definitions for graphs Breadth First Search and Depth First Search Topological Sort. Graphs AgraphG is given by a set of vertices V and a set of edges E. Normally
More informationAnnouncements. HW3 is graded. Average is 81%
CSC263 Week 9 Announcements HW3 is graded. Average is 81% Announcements Problem Set 4 is due this Tuesday! Due Tuesday (Nov 17) Recap The Graph ADT definition and data structures BFS gives us single-source
More informationGraph Algorithms Using Depth First Search
Graph Algorithms Using Depth First Search Analysis of Algorithms Week 8, Lecture 1 Prepared by John Reif, Ph.D. Distinguished Professor of Computer Science Duke University Graph Algorithms Using Depth
More informationElementary Graph Algorithms. Ref: Chapter 22 of the text by Cormen et al. Representing a graph:
Elementary Graph Algorithms Ref: Chapter 22 of the text by Cormen et al. Representing a graph: Graph G(V, E): V set of nodes (vertices); E set of edges. Notation: n = V and m = E. (Vertices are numbered
More informationComputer Science & Engineering 423/823 Design and Analysis of Algorithms
Computer Science & Engineering 423/823 Design and Analysis of Algorithms Lecture 04 Elementary Graph Algorithms (Chapter 22) Stephen Scott (Adapted from Vinodchandran N. Variyam) sscott@cse.unl.edu Introduction
More informationGraph Algorithms. Chapter 22. CPTR 430 Algorithms Graph Algorithms 1
Graph Algorithms Chapter 22 CPTR 430 Algorithms Graph Algorithms Why Study Graph Algorithms? Mathematical graphs seem to be relatively specialized and abstract Why spend so much time and effort on algorithms
More informationGraph representation
Graph Algorithms 1 Graph representation Given graph G = (V, E). May be either directed or undirected. Two common ways to represent for algorithms: 1. Adjacency lists. 2. Adjacency matrix. When expressing
More informationBasic Graph Algorithms
Basic Graph Algorithms 1 Representations of Graphs There are two standard ways to represent a graph G(V, E) where V is the set of vertices and E is the set of edges. adjacency list representation adjacency
More informationReadable semi-automatic formal proofs of Depth-First Search in graphs using Why3
Readable semi-automatic formal proofs of Depth-First Search in graphs using Why3 Ran Chen, Jean-Jacques Levy To cite this version: Ran Chen, Jean-Jacques Levy. Readable semi-automatic formal proofs of
More informationGraph Representations and Traversal
COMPSCI 330: Design and Analysis of Algorithms 02/08/2017-02/20/2017 Graph Representations and Traversal Lecturer: Debmalya Panigrahi Scribe: Tianqi Song, Fred Zhang, Tianyu Wang 1 Overview This lecture
More informationFundamental Graph Algorithms Part Four
Fundamental Graph Algorithms Part Four Announcements Problem Set One due right now. Due Friday at 2:15PM using one late period. Problem Set Two out, due next Friday, July 12 at 2:15PM. Play around with
More informationGraph Representation
Graph Representation Adjacency list representation of G = (V, E) An array of V lists, one for each vertex in V Each list Adj[u] contains all the vertices v such that there is an edge between u and v Adj[u]
More informationFormally-Proven Kosaraju s algorithm
Formally-Proven Kosaraju s algorithm Laurent Théry Laurent.Thery@sophia.inria.fr Abstract This notes explains how the Kosaraju s algorithm that computes the strong-connected components of a directed graph
More informationCS2 Algorithms and Data Structures Note 10. Depth-First Search and Topological Sorting
CS2 Algorithms and Data Structures Note 10 Depth-First Search and Topological Sorting In this lecture, we will analyse the running time of DFS and discuss a few applications. 10.1 A recursive implementation
More informationComputer Science & Engineering 423/823 Design and Analysis of Algorithms
s of s Computer Science & Engineering 423/823 Design and Analysis of Lecture 03 (Chapter 22) Stephen Scott (Adapted from Vinodchandran N. Variyam) 1 / 29 s of s s are abstract data types that are applicable
More informationCSI 604 Elementary Graph Algorithms
CSI 604 Elementary Graph Algorithms Ref: Chapter 22 of the text by Cormen et al. (Second edition) 1 / 25 Graphs: Basic Definitions Undirected Graph G(V, E): V is set of nodes (or vertices) and E is the
More informationHomework No. 4 Answers
Homework No. 4 Answers CSCI 4470/6470 Algorithms, CS@UGA, Spring 2018 Due Thursday March 29, 2018 There are 100 points in total. 1. (20 points) Consider algorithm DFS (or algorithm To-Start-DFS in Lecture
More informationGraph Algorithms. Definition
Graph Algorithms Many problems in CS can be modeled as graph problems. Algorithms for solving graph problems are fundamental to the field of algorithm design. Definition A graph G = (V, E) consists of
More informationAlgorithm Design and Analysis
Algorithm Design and Analysis LECTURE 5 Graphs Applications of DFS Topological sort Strongly connected components Sofya Raskhodnikova S. Raskhodnikova; based on slides by E. Demaine, C. Leiserson, A. Smith,
More informationPart VI Graph algorithms. Chapter 22 Elementary Graph Algorithms Chapter 23 Minimum Spanning Trees Chapter 24 Single-source Shortest Paths
Part VI Graph algorithms Chapter 22 Elementary Graph Algorithms Chapter 23 Minimum Spanning Trees Chapter 24 Single-source Shortest Paths 1 Chapter 22 Elementary Graph Algorithms Representations of graphs
More informationGraph Algorithms: Chapters Part 1: Introductory graph concepts
UMass Lowell Computer Science 91.503 Algorithms Dr. Haim Levkowitz Fall, 2007 Graph Algorithms: Chapters 22-25 Part 1: Introductory graph concepts 1 91.404 Graph Review Elementary Graph Algorithms Minimum
More informationGraph. Vertex. edge. Directed Graph. Undirected Graph
Module : Graphs Dr. Natarajan Meghanathan Professor of Computer Science Jackson State University Jackson, MS E-mail: natarajan.meghanathan@jsums.edu Graph Graph is a data structure that is a collection
More informationWhy3 where programs meet provers
Why3 where programs meet provers Jean-Christophe Filliâtre CNRS KeY Symposium 2017 Rastatt, Germany October 5, 2017 history started in 2001, as an intermediate language in the process of verifying C and
More informationGraph: representation and traversal
Graph: representation and traversal CISC4080, Computer Algorithms CIS, Fordham Univ. Instructor: X. Zhang! Acknowledgement The set of slides have use materials from the following resources Slides for textbook
More informationChapter 22. Elementary Graph Algorithms
Graph Algorithms - Spring 2011 Set 7. Lecturer: Huilan Chang Reference: (1) Cormen, Leiserson, Rivest, and Stein, Introduction to Algorithms, 2nd Edition, The MIT Press. (2) Lecture notes from C. Y. Chen
More informationLecture 10: Strongly Connected Components, Biconnected Graphs
15-750: Graduate Algorithms February 8, 2016 Lecture 10: Strongly Connected Components, Biconnected Graphs Lecturer: David Witmer Scribe: Zhong Zhou 1 DFS Continued We have introduced Depth-First Search
More informationCSCE 750, Fall 2002 Notes 6 Page Graph Problems ffl explore all nodes (breadth first and depth first) ffl find the shortest path from a given s
CSCE 750, Fall 2002 Notes 6 Page 1 10 Graph Algorithms (These notes follow the development in Cormen, Leiserson, and Rivest.) 10.1 Definitions ffl graph, directed graph (digraph), nodes, edges, subgraph
More informationGraph Search. Adnan Aziz
Graph Search Adnan Aziz Based on CLRS, Ch 22. Recall encountered graphs several weeks ago (CLRS B.4) restricted our attention to definitions, terminology, properties Now we ll see how to perform basic
More informationWhy. an intermediate language for deductive program verification
Why an intermediate language for deductive program verification Jean-Christophe Filliâtre CNRS Orsay, France AFM workshop Grenoble, June 27, 2009 Jean-Christophe Filliâtre Why tutorial AFM 09 1 / 56 Motivations
More informationCSE 101. Algorithm Design and Analysis Miles Jones and Russell Impagliazzo Miles Office 4208 CSE Building
CSE 101 Algorithm Design and Analysis Miles Jones and Russell Impagliazzo mej016@eng.ucsd.edu russell@cs.ucsd.edu Miles Office 4208 CSE Building Russell s Office: 4248 CSE Building procedure DFS(G) cc
More information22.3 Depth First Search
22.3 Depth First Search Depth-First Search(DFS) always visits a neighbour of the most recently visited vertex with an unvisited neighbour. This means the search moves forward when possible, and only backtracks
More informationGraphs. Part I: Basic algorithms. Laura Toma Algorithms (csci2200), Bowdoin College
Laura Toma Algorithms (csci2200), Bowdoin College Undirected graphs Concepts: connectivity, connected components paths (undirected) cycles Basic problems, given undirected graph G: is G connected how many
More informationINF2220: algorithms and data structures Series 7
Universitetet i Oslo nstitutt for nformatikk. Yu,. arabeg uiologo NF2220: algorithms and data structures Series 7 Topic ore on graphs: FS, iconnectivity, and strongly connected components (Exercises with
More informationProducing All Ideals of a Forest, Formally (Verification Pearl)
Producing All Ideals of a Forest, Formally (Verification Pearl) Jean-Christophe Filliâtre, Mário Pereira To cite this version: Jean-Christophe Filliâtre, Mário Pereira. Producing All Ideals of a Forest,
More informationCut vertices, Cut Edges and Biconnected components. MTL776 Graph algorithms
Cut vertices, Cut Edges and Biconnected components MTL776 Graph algorithms Articulation points, Bridges, Biconnected Components Let G = (V;E) be a connected, undirected graph. An articulation point of
More informationTrees Algorhyme by Radia Perlman
Algorhyme by Radia Perlman I think that I shall never see A graph more lovely than a tree. A tree whose crucial property Is loop-free connectivity. A tree which must be sure to span. So packets can reach
More informationProof: if not f[u] < d[v], then u still grey while v is being visited. DFS visit(v) will then terminate before DFS visit(u).
Parenthesis property of DFS discovery and finishing times (Thm 23.6 of CLR): For any two nodes u,v of a directed graph, if d[u] < d[v] (i.e. u discovered before v), either f[v] < f[u] (i.e. visit time
More informationHomework Assignment #3 Graph
CISC 4080 Computer Algorithms Spring, 2019 Homework Assignment #3 Graph Some of the problems are adapted from problems in the book Introduction to Algorithms by Cormen, Leiserson and Rivest, and some are
More informationLecture 6 Basic Graph Algorithms
CS 491 CAP Intro to Competitive Algorithmic Programming Lecture 6 Basic Graph Algorithms Uttam Thakore University of Illinois at Urbana-Champaign September 30, 2015 Updates ICPC Regionals teams will be
More information15-451/651: Design & Analysis of Algorithms October 5, 2017 Lecture #11: Depth First Search and Strong Components last changed: October 17, 2017
15-451/651: Design & Analysis of Algorithms October 5, 2017 Lecture #11: Depth First Search and Strong Components last changed: October 17, 2017 1 Introduction Depth first search is a very useful technique
More informationLecture 2 - Graph Theory Fundamentals - Reachability and Exploration 1
CME 305: Discrete Mathematics and Algorithms Instructor: Professor Aaron Sidford (sidford@stanford.edu) January 11, 2018 Lecture 2 - Graph Theory Fundamentals - Reachability and Exploration 1 In this lecture
More informationDepth First Search (DFS)
Algorithms & Models of Computation CS/ECE 374, Fall 2017 Depth First Search (DFS) Lecture 16 Thursday, October 26, 2017 Sariel Har-Peled (UIUC) CS374 1 Fall 2017 1 / 60 Today Two topics: Structure of directed
More informationFormal Verification of Floating-Point programs
Formal Verification of Floating-Point programs Sylvie Boldo and Jean-Christophe Filliâtre Montpellier June, 26th 2007 INRIA Futurs CNRS, LRI Motivations Goal: reliability in numerical software Motivations
More informationGraph Algorithms. Andreas Klappenecker. [based on slides by Prof. Welch]
Graph Algorithms Andreas Klappenecker [based on slides by Prof. Welch] 1 Directed Graphs Let V be a finite set and E a binary relation on V, that is, E VxV. Then the pair G=(V,E) is called a directed graph.
More informationDFS & STRONGLY CONNECTED COMPONENTS
DFS & STRONGLY CONNECTED COMPONENTS CS 4407 Search Tree Breadth-First Search (BFS) Depth-First Search (DFS) Depth-First Search (DFS) u d[u]: when u is discovered f[u]: when searching adj of u is finished
More informationTutorial. Question There are no forward edges. 4. For each back edge u, v, we have 0 d[v] d[u].
Tutorial Question 1 A depth-first forest classifies the edges of a graph into tree, back, forward, and cross edges. A breadth-first tree can also be used to classify the edges reachable from the source
More informationTrees and Graphs Shabsi Walfish NYU - Fundamental Algorithms Summer 2006
Trees and Graphs Basic Definitions Tree: Any connected, acyclic graph G = (V,E) E = V -1 n-ary Tree: Tree s/t all vertices of degree n+1 A root has degree n Binary Search Tree: A binary tree such that
More informationPaths partition with prescribed beginnings in digraphs: a Chvátal-Erdős condition approach.
Paths partition with prescribed beginnings in digraphs: a Chvátal-Erdős condition approach. S. Bessy, Projet Mascotte, CNRS/INRIA/UNSA, INRIA Sophia-Antipolis, 2004 route des Lucioles BP 93, 06902 Sophia-Antipolis
More informationCS521 \ Notes for the Final Exam
CS521 \ Notes for final exam 1 Ariel Stolerman Asymptotic Notations: CS521 \ Notes for the Final Exam Notation Definition Limit Big-O ( ) Small-o ( ) Big- ( ) Small- ( ) Big- ( ) Notes: ( ) ( ) ( ) ( )
More informationFundamentals of Data Structure
Fundamentals of Data Structure Set-1 1. Which if the following is/are the levels of implementation of data structure A) Abstract level B) Application level C) Implementation level D) All of the above 2.
More informationFundamental Graph Algorithms Part Three
Fundamental Graph Algorithms Part Three Outline for Today Topological Sorting, Part II How can we quickly compute a topological ordering on a DAG? Connected Components How do we find the pieces of an undirected
More informationBasic Graph Algorithms (CLRS B.4-B.5, )
Basic Graph Algorithms (CLRS B.-B.,.-.) Basic Graph Definitions A graph G = (V,E) consists of a finite set of vertices V and a finite set of edges E. Directed graphs: E is a set of ordered pairs of vertices
More information( ) D. Θ ( ) ( ) Ο f ( n) ( ) Ω. C. T n C. Θ. B. n logn Ο
CSE 0 Name Test Fall 0 Multiple Choice. Write your answer to the LEFT of each problem. points each. The expected time for insertion sort for n keys is in which set? (All n! input permutations are equally
More informationPart IV. Common Reduction Targets
Part IV Common Reduction Targets 413 Chapter 13 Depth-First Search As we have seen in earlier chapters, many problems can be characterized as graph problems. Graph problems often require the searching
More informationGraphs: Connectivity. Jordi Cortadella and Jordi Petit Department of Computer Science
Graphs: Connectivity Jordi Cortadella and Jordi Petit Department of Computer Science A graph Source: Wikipedia The network graph formed by Wikipedia editors (edges) contributing to different Wikipedia
More informationCS Elementary Graph Algorithms
CS43-09 Elementary Graph Algorithms Outline Representation of Graphs Instructor: Fei Li Room 443 ST II Office hours: Tue. & Thur. 1:30pm - 2:30pm or by appointments lifei@cs.gmu.edu with subject: CS43
More informationCS Elementary Graph Algorithms
CS483-09 Elementary Graph Algorithms Instructor: Fei Li Room 443 ST II Office hours: Tue. & Thur. 1:30pm - 2:30pm or by appointments lifei@cs.gmu.edu with subject: CS483 http://www.cs.gmu.edu/ lifei/teaching/cs483_fall07/
More informationChapter 23. Minimum Spanning Trees
Chapter 23. Minimum Spanning Trees We are given a connected, weighted, undirected graph G = (V,E;w), where each edge (u,v) E has a non-negative weight (often called length) w(u,v). The Minimum Spanning
More informationDeductive Verification in Frama-C and SPARK2014: Past, Present and Future
Deductive Verification in Frama-C and SPARK2014: Past, Present and Future Claude Marché (Inria & Université Paris-Saclay) OSIS, Frama-C & SPARK day, May 30th, 2017 1 / 31 Outline Why this joint Frama-C
More information(Re)Introduction to Graphs and Some Algorithms
(Re)Introduction to Graphs and Some Algorithms Graph Terminology (I) A graph is defined by a set of vertices V and a set of edges E. The edge set must work over the defined vertices in the vertex set.
More informationCombining Coq and Gappa for Certifying FP Programs
Introduction Example Gappa Tactic Conclusion Combining Coq and Gappa for Certifying Floating-Point Programs Sylvie Boldo Jean-Christophe Filliâtre Guillaume Melquiond Proval, Laboratoire de Recherche en
More informationAlgorithm Design and Analysis
Algorithm Design and Analysis LECTURE 7 Greedy Graph Algorithms Topological sort Shortest paths Adam Smith The (Algorithm) Design Process 1. Work out the answer for some examples. Look for a general principle
More informationCS 341: Algorithms. Douglas R. Stinson. David R. Cheriton School of Computer Science University of Waterloo. February 26, 2019
CS 341: Algorithms Douglas R. Stinson David R. Cheriton School of Computer Science University of Waterloo February 26, 2019 D.R. Stinson (SCS) CS 341 February 26, 2019 1 / 296 1 Course Information 2 Introduction
More information( ) n 3. n 2 ( ) D. Ο
CSE 0 Name Test Summer 0 Last Digits of Mav ID # Multiple Choice. Write your answer to the LEFT of each problem. points each. The time to multiply two n n matrices is: A. Θ( n) B. Θ( max( m,n, p) ) C.
More informationTaking Stock. IE170: Algorithms in Systems Engineering: Lecture 17. Depth-First Search. DFS (Initialize and Go) Last Time Depth-First Search
Taking Stock IE170: Algorithms in Systems Engineering: Lecture 17 Jeff Linderoth Department of Industrial and Systems Engineering Lehigh University March 2, 2007 Last Time Depth-First Search This Time:
More informationConnectivity. Use DFS or BFS. 03/11/04 Lecture 18 1
Connectivity A (simple) undirected graph is connected if there exists a path between every pair of vertices. If a graph is not connected, then G (V,E ) is a connected component of the graph G(V,E) if V
More informationDepth First Search (DFS)
Algorithms & Models of Computation CS/ECE 374, Fall 2017 epth First Search (FS) Lecture 1 Thursday, October 2, 2017 Today Two topics: Structure of directed graphs FS and its properties One application
More informationCS 473: Algorithms. Chandra Chekuri 3228 Siebel Center. Fall University of Illinois, Urbana-Champaign
CS 473: Algorithms Chandra chekuri@cs.illinois.edu 3228 Siebel Center University of Illinois, Urbana-Champaign Fall 2010 Depth First Search Directed Graphs Searching ted Graphs Strong Connected Components
More informationAn Improved Algorithm for Finding the Strongly Connected Components of a Directed Graph
An Improved Algorithm for Finding the Strongly Connected Components of a Directed Graph David J. Pearce Computer Science Group Victoria University, NZ david.pearce@mcs.vuw.ac.nz Keywords: Graph Algorithms,
More informationLecture 3: Graphs and flows
Chapter 3 Lecture 3: Graphs and flows Graphs: a useful combinatorial structure. Definitions: graph, directed and undirected graph, edge as ordered pair, path, cycle, connected graph, strongly connected
More informationAlgorithms and Data Structures
Algorithms and Data Structures Graphs 3: Finding Connected Components Marius Kloft Content of this Lecture Finding Connected Components in Undirected Graphs Finding Strongly Connected Components in Directed
More informationSemi-Persistent Data Structures
Semi-Persistent Data Structures Sylvain Conchon and Jean-Christophe Filliâtre Université Paris Sud CNRS ESOP 2008 S. Conchon & J.-C. Filliâtre Semi-Persistent Data Structures ESOP 2008 1 Persistence persistent
More informationlogn D. Θ C. Θ n 2 ( ) ( ) f n B. nlogn Ο n2 n 2 D. Ο & % ( C. Θ # ( D. Θ n ( ) Ω f ( n)
CSE 0 Test Your name as it appears on your UTA ID Card Fall 0 Multiple Choice:. Write the letter of your answer on the line ) to the LEFT of each problem.. CIRCLED ANSWERS DO NOT COUNT.. points each. The
More informationData Structures. Elementary Graph Algorithms BFS, DFS & Topological Sort
Data Structures Elementary Graph Algorithms BFS, DFS & Topological Sort 1 Graphs A graph, G = (V, E), consists of two sets: V is a finite non-empty set of vertices. E is a set of pairs of vertices, called
More informationGraphs and trees come up everywhere. We can view the internet as a graph (in many ways) Web search views web pages as a graph
Graphs and Trees Graphs and trees come up everywhere. We can view the internet as a graph (in many ways) who is connected to whom Web search views web pages as a graph Who points to whom Niche graphs (Ecology):
More informationAlgorithms and Data Structures
Algorithms and Data Structures Strongly Connected Components Ulf Leser Content of this Lecture Graph Traversals Strongly Connected Components Ulf Leser: Algorithms and Data Structures, Summer Semester
More informationDeductive Program Verification with Why3
Deductive Program Verification with Why3 Jean-Christophe Filliâtre CNRS Mathematical Structures of Computation Formal Proof, Symbolic Computation and Computer Arithmetic Lyon, February 2014 definition
More informationDetecting negative cycles with Tarjan s breadth-first scanning algorithm
Detecting negative cycles with Tarjan s breadth-first scanning algorithm Tibor Ásványi asvanyi@inf.elte.hu ELTE Eötvös Loránd University Faculty of Informatics Budapest, Hungary Abstract The Bellman-Ford
More informationUnweighted Graphs & Algorithms
Unweighted Graphs & Algorithms Zachary Friggstad Programming Club Meeting References Chapter 4: Graph (Section 4.2) Chapter 22: Elementary Graph Algorithms Graphs Features: vertices/nodes/dots and edges/links/lines
More information1 Start with each vertex being its own component. 2 Merge two components into one by choosing the light edge
Taking Stock IE170: in Systems Engineering: Lecture 19 Jeff Linderoth Department of Industrial and Systems Engineering Lehigh University March 16, 2007 Last Time Minimum This Time More Strongly Connected
More informationRepresentations of Graphs
ELEMENTARY GRAPH ALGORITHMS -- CS-5321 Presentation -- I am Nishit Kapadia Representations of Graphs There are two standard ways: A collection of adjacency lists - they provide a compact way to represent
More informationInf 2B: Graphs II - Applications of DFS
Inf 2B: Graphs II - Applications of DFS Kyriakos Kalorkoti School of Informatics University of Edinburgh Reminder: Recursive DFS Algorithm dfs(g) 1. Initialise Boolean array visited by setting all entries
More informationAn O (mlogn)-time Algorithm for the Maximal Planar Subgraph Problem
An O (mlogn)-time Algorithm for the Maximal Planar Subgraph Problem Jiazhen Cai 1 Courant Institute, NYU New York, NY 10012 Xiaofeng Han Department of Computer Science Princeton University Princeton, NJ
More informationTopic 12: Elementary Graph Algorithms
Topic 12: Elementary Graph Algorithms Pierre Flener Lars-enrik Eriksson (version of 2013-02-10) Different kinds of graphs List Tree A B C A B C Directed Acyclic Graph (DAG) D A B C D E Directed Graph (Digraph)
More informationDirected Graphs (II) Hwansoo Han
Directed Graphs (II) Hwansoo Han Traversals of Directed Graphs To solve many problems dealing with digraphs, we need to visit vertexes and arcs in a systematic way Depth-first search (DFS) A generalization
More information11.9 Connectivity Connected Components. mcs 2015/5/18 1:43 page 419 #427
mcs 2015/5/18 1:43 page 419 #427 11.9 Connectivity Definition 11.9.1. Two vertices are connected in a graph when there is a path that begins at one and ends at the other. By convention, every vertex is
More informationChapter 22 Elementary Graph Algorithms
Chapter 22 Elementary Graph Algorithms Graph Representations Graph G = (V,E) Directed Undirected Adjacency Lists Adjacency Matrix Graph Representations Adjacency List: Undirected Memory: Adjacency: Graph
More informationKruskal s MST Algorithm
Kruskal s MST Algorithm CLRS Chapter 23, DPV Chapter 5 Version of November 5, 2014 Main Topics of This Lecture Kruskal s algorithm Another, but different, greedy MST algorithm Introduction to UNION-FIND
More informationApplications of BDF and DFS
January 14, 2016 1 Deciding whether a graph is bipartite using BFS. 2 Finding connected components of a graph (both BFS and DFS) works. 3 Deciding whether a digraph is strongly connected. 4 Finding cut
More informationCSC313 High Integrity Systems/CSCM13 Critical Systems. CSC313/CSCM13 Chapter 2 1/ 221
CSC313 High Integrity Systems/CSCM13 Critical Systems CSC313/CSCM13 Chapter 2 1/ 221 CSC313 High Integrity Systems/ CSCM13 Critical Systems Course Notes Chapter 2: SPARK Ada Sect. 2 (f) Anton Setzer Dept.
More informationGraphs: basic concepts and algorithms
: basic concepts and algorithms Topics covered by this lecture: - Reminder Trees Trees (in-order,post-order,pre-order) s (BFS, DFS) Denitions: Reminder Directed graph (digraph): G = (V, E), V - vertex
More informationDepth-first search in undirected graphs What parts of the graph are reachable from a given vertex?
Why graphs? Chapter 3. Decompositions of graphs Awiderangeofproblemscanbeexpressedwithclarityandprecisionin the concise pictorial language of graphs. Graph coloring. Graph connectivity and reachability.
More information