B AB 5 C AC 3 D ABGED 9 E ABGE 7 F ABGEF 8 G ABG 6 A BEDA 3 C BC 1 D BCD 2 E BE 1 F BEF 2 G BG 1

Size: px
Start display at page:

Download "B AB 5 C AC 3 D ABGED 9 E ABGE 7 F ABGEF 8 G ABG 6 A BEDA 3 C BC 1 D BCD 2 E BE 1 F BEF 2 G BG 1"

Transcription

1 p a. Find the shortest path from A to all other vertices for the graph in Figure 9.8. b. Find the shortest unweighted path from B to all other vertices for the graph in Figure 9.8. A 5 B C D G F E a. source destination path cost A B AB 5 C AC D ABGED 9 E ABGE 7 F ABGEF 8 G ABG 6 b. Source Destination path cost B A BEDA C BC D BCD E BE F BEF G BG p. 9. (ZOJ 56) a. Explain how to modify Dijkstra's algorithm to produce a count of the number of different minimum paths from v to w. b. Explain how to modify Dijkstra's algorithm so that if there is more than one minimum path from v to w, a path with the fewest number of edges is chosen.

2 Answer a: void Dijkstra( Table T ) /* T[ ].Count is initialized to be. T[start].Count = */ vertex v, w; for ( ; ; ) v = smallest unknown distance vertex; if ( v == NotAVertex ) break; T[v].Known = True; for ( each w adjacent to v ) if(!t[w].known ) if( T[v].Dist + Cvw < T[w].Dist ) Decrease( T[w].Dist to T[v]+Cvw ) T[w].Path = v; T[w].Count = T[v].Count; /* NOT T[w].Count = */ else if( T[v].Dist + Cvw == T[w].Dist ) T[w].Count += T[v].Count; /* NOT T[w].Count += */ Answer b: void Dijkstra( Table T ) /* T[ ].Count is initialized to be */ vertex v, w; for ( ; ; ) v = smallest unknown distance vertex; if ( v == NotAVertex ) break; T[v].Known = True; for ( each w adjacent to v ) if(!t[w].known ) if( T[v].Dist + Cvw < T[w].Dist ) Decrease( T[w].Dist to T[v]+Cvw ) T[w].Path = v; T[w].Count = T[v].Count + ; else if( ( T[v].Dist + Cvw == T[w].Dist ) && ( T[v].Count + < T[w].Count ) ) T[w].Count = T[v].Count + ; T[w].Path = v; /* DO NOT forget this */

3 p. 9. Find the maximum flow in the network of Figure Answer: A B C s D E F 6 G H I t p. 9.5 a. Find a minimum spanning tree for the graph in Figure 9.8 using both Prim's and Kruskal's algorithms. b. Is this minimum spanning tree unique? Why? Answer a: They are the same A B C D E F G H I 7 J Answer b: This minimum spanning tree is not unique. For example, another minimum spanning tree obtained from Prim s algorithm is: A B C D E F G H I 7 J

4 p Write a program to find the strongly connected components in a digraph. #define MaxVertices /* maximum number of vertices */ typedef int Vertex; /* vertices are numbered from to MaxVertices */ typedef enum FALSE, TRUE boolean; /* declarations for a graph with adjacency list representation */ #ifndef _Graph_h struct VNode; typedef struct VNode *PtrToVNode; struct GNode; typedef struct GNode *PtrToGNode; typedef PtrToGNode Graph; /* create a graph with NumOfVertices vertices and no edge */ Graph CreateGraph( int NumOfVertices ); /* insert edge V->W to G */ boolean InsertEdge( Vertex V, Vertex W, Graph G ); /* reverse all the edges in G and return the resulting graph Gr */ Graph ReverseGraph( Graph G ); /* free spaces taken by G */ void DeleteGraph( Graph G ); /* implementations of the above functions are omitted */ #endif /*_Graph_h */ struct VNode Vertex Vert; PtrToVNode Next; ; struct GNode int NumOfVertices; int NumOfEdges; PtrToVNode *Array; ; boolean Visited[MaxVertices]; /* global mark for visited vertices */ Vertex DfsOrder[MaxVertices]; /* store vertices in dfs order */ int DfsNum; /* dfs number for vertices */

5 void PostOrder( Vertex V ) /* store vertices during postorder dfs */ DfsOrder[DfsNum++] = V; void PrintV( Vertex V ) /* print V during postorder dfs */ printf("%d ", V); void PostorderDfs( Vertex V, Graph G, void (*f)(vertex V) ) /* postorder dfs template with visiting function f */ PtrToVNode W; Visited[V] = TRUE; for ( W=G->Array[V]->Next; W; W=W->Next ) if (!Visited[W->Vert] ) PostorderDfs( W->Vert, G, (*f) ); (*f)(v); void StronglyConnectedComponents( Graph G ) /* print the strongly connected components in G */ /* output format: V, V,... V, V, */ Graph Gr; Vertex V; /* Step : mark the postorder dfs number for each vertex in G */ InitializeVisited( G->NumOfVertices ); /* Visited[ ] is initialized to be FALSE */ DfsNum = ; for ( V=; V<G->NumOfVertices; V++ ) if (!Visited[V] ) PostorderDfs( V, G, PostOrder ); /* end for */ /* Step : reverse edges in G and save the resulting graph in Gr */ Gr = ReverseGraph( G ); if (!Gr )

6 printf("program failed: cannot reverse graph.\n"); else /* Step : print components by postorder dfs on Gr */ InitializeVisited( Gr->NumOfVertices ); while ( DfsNum ) /* always start at the vertex with the largest dfs number */ V = DfsOrder[DfsNum]; if (!Visited[V] ) /* print this component in a line*/ printf( " " ); PostorderDfs( V, Gr, PrintV ); printf( "\n" ); /* end - if */ /* end - while */ DeleteGraph( Gr ); /* free space */ /* end - else */ Sketch of the proof of correctness:. V, W Comp(G) Path(V->W) and Path(W->V) in both G and Gr;. V, W Comp(G) V, W DfsT(G) and DfsT(Gr);. For V DfsT(Gr) with X as the root, Path(X->V) in Gr, and hence Path(V->X) in G.. Step V, X DfsT(G); 5. DfsNum(X) > DfsNum(V) V must be numbered before X in DfsT(G); 6. Step 5 V is a descendant of X in DfsT(G) since it was a postorder visit; 7. Step 6 Path(X->V) in G. G/G r DfsT(G) DfsT(G r )

CSE 326, Data Structures. Sample Final Exam. Problem Max Points Score 1 14 (2x7) 2 18 (3x6) 3 4 4 7 5 9 6 16 7 8 8 4 9 8 10 4 Total 92.

CSE 326, Data Structures. Sample Final Exam. Problem Max Points Score 1 14 (2x7) 2 18 (3x6) 3 4 4 7 5 9 6 16 7 8 8 4 9 8 10 4 Total 92. Name: Email ID: CSE 326, Data Structures Section: Sample Final Exam Instructions: The exam is closed book, closed notes. Unless otherwise stated, N denotes the number of elements in the data structure

More information

Data Structures and Algorithms Written Examination

Data Structures and Algorithms Written Examination Data Structures and Algorithms Written Examination 22 February 2013 FIRST NAME STUDENT NUMBER LAST NAME SIGNATURE Instructions for students: Write First Name, Last Name, Student Number and Signature where

More information

Home Page. Data Structures. Title Page. Page 1 of 24. Go Back. Full Screen. Close. Quit

Home Page. Data Structures. Title Page. Page 1 of 24. Go Back. Full Screen. Close. Quit Data Structures Page 1 of 24 A.1. Arrays (Vectors) n-element vector start address + ielementsize 0 +1 +2 +3 +4... +n-1 start address continuous memory block static, if size is known at compile time dynamic,

More information

Cpt S 223. School of EECS, WSU

Cpt S 223. School of EECS, WSU The Shortest Path Problem 1 Shortest-Path Algorithms Find the shortest path from point A to point B Shortest in time, distance, cost, Numerous applications Map navigation Flight itineraries Circuit wiring

More information

SEMITOTAL AND TOTAL BLOCK-CUTVERTEX GRAPH

SEMITOTAL AND TOTAL BLOCK-CUTVERTEX GRAPH CHAPTER 3 SEMITOTAL AND TOTAL BLOCK-CUTVERTEX GRAPH ABSTRACT This chapter begins with the notion of block distances in graphs. Using block distance we defined the central tendencies of a block, like B-radius

More information

Social Media Mining. Graph Essentials

Social Media Mining. Graph Essentials Graph Essentials Graph Basics Measures Graph and Essentials Metrics 2 2 Nodes and Edges A network is a graph nodes, actors, or vertices (plural of vertex) Connections, edges or ties Edge Node Measures

More information

2. (a) Explain the strassen s matrix multiplication. (b) Write deletion algorithm, of Binary search tree. [8+8]

2. (a) Explain the strassen s matrix multiplication. (b) Write deletion algorithm, of Binary search tree. [8+8] Code No: R05220502 Set No. 1 1. (a) Describe the performance analysis in detail. (b) Show that f 1 (n)+f 2 (n) = 0(max(g 1 (n), g 2 (n)) where f 1 (n) = 0(g 1 (n)) and f 2 (n) = 0(g 2 (n)). [8+8] 2. (a)

More information

Output: 12 18 30 72 90 87. struct treenode{ int data; struct treenode *left, *right; } struct treenode *tree_ptr;

Output: 12 18 30 72 90 87. struct treenode{ int data; struct treenode *left, *right; } struct treenode *tree_ptr; 50 20 70 10 30 69 90 14 35 68 85 98 16 22 60 34 (c) Execute the algorithm shown below using the tree shown above. Show the exact output produced by the algorithm. Assume that the initial call is: prob3(root)

More information

MATHEMATICS Unit Decision 1

MATHEMATICS Unit Decision 1 General Certificate of Education January 2008 Advanced Subsidiary Examination MATHEMATICS Unit Decision 1 MD01 Tuesday 15 January 2008 9.00 am to 10.30 am For this paper you must have: an 8-page answer

More information

Analysis of Algorithms, I

Analysis of Algorithms, I Analysis of Algorithms, I CSOR W4231.002 Eleni Drinea Computer Science Department Columbia University Thursday, February 26, 2015 Outline 1 Recap 2 Representing graphs 3 Breadth-first search (BFS) 4 Applications

More information

IE 680 Special Topics in Production Systems: Networks, Routing and Logistics*

IE 680 Special Topics in Production Systems: Networks, Routing and Logistics* IE 680 Special Topics in Production Systems: Networks, Routing and Logistics* Rakesh Nagi Department of Industrial Engineering University at Buffalo (SUNY) *Lecture notes from Network Flows by Ahuja, Magnanti

More information

Minimum Spanning Trees

Minimum Spanning Trees Minimum Spanning Trees weighted graph API cycles and cuts Kruskal s algorithm Prim s algorithm advanced topics References: Algorithms in Java, Chapter 20 http://www.cs.princeton.edu/introalgsds/54mst 1

More information

The following themes form the major topics of this chapter: The terms and concepts related to trees (Section 5.2).

The following themes form the major topics of this chapter: The terms and concepts related to trees (Section 5.2). CHAPTER 5 The Tree Data Model There are many situations in which information has a hierarchical or nested structure like that found in family trees or organization charts. The abstraction that models hierarchical

More information

Any two nodes which are connected by an edge in a graph are called adjacent node.

Any two nodes which are connected by an edge in a graph are called adjacent node. . iscuss following. Graph graph G consist of a non empty set V called the set of nodes (points, vertices) of the graph, a set which is the set of edges and a mapping from the set of edges to a set of pairs

More information

Data Structure with C

Data Structure with C Subject: Data Structure with C Topic : Tree Tree A tree is a set of nodes that either:is empty or has a designated node, called the root, from which hierarchically descend zero or more subtrees, which

More information

GRAPH THEORY LECTURE 4: TREES

GRAPH THEORY LECTURE 4: TREES GRAPH THEORY LECTURE 4: TREES Abstract. 3.1 presents some standard characterizations and properties of trees. 3.2 presents several different types of trees. 3.7 develops a counting method based on a bijection

More information

Load balancing Static Load Balancing

Load balancing Static Load Balancing Chapter 7 Load Balancing and Termination Detection Load balancing used to distribute computations fairly across processors in order to obtain the highest possible execution speed. Termination detection

More information

Load Balancing and Termination Detection

Load Balancing and Termination Detection Chapter 7 Load Balancing and Termination Detection 1 Load balancing used to distribute computations fairly across processors in order to obtain the highest possible execution speed. Termination detection

More information

Part 2: Community Detection

Part 2: Community Detection Chapter 8: Graph Data Part 2: Community Detection Based on Leskovec, Rajaraman, Ullman 2014: Mining of Massive Datasets Big Data Management and Analytics Outline Community Detection - Social networks -

More information

The number of marks is given in brackets [ ] at the end of each question or part question. The total number of marks for this paper is 72.

The number of marks is given in brackets [ ] at the end of each question or part question. The total number of marks for this paper is 72. ADVANCED SUBSIDIARY GCE UNIT 4736/01 MATHEMATICS Decision Mathematics 1 THURSDAY 14 JUNE 2007 Afternoon Additional Materials: Answer Booklet (8 pages) List of Formulae (MF1) Time: 1 hour 30 minutes INSTRUCTIONS

More information

Network (Tree) Topology Inference Based on Prüfer Sequence

Network (Tree) Topology Inference Based on Prüfer Sequence Network (Tree) Topology Inference Based on Prüfer Sequence C. Vanniarajan and Kamala Krithivasan Department of Computer Science and Engineering Indian Institute of Technology Madras Chennai 600036 vanniarajanc@hcl.in,

More information

Data Structure [Question Bank]

Data Structure [Question Bank] Unit I (Analysis of Algorithms) 1. What are algorithms and how they are useful? 2. Describe the factor on best algorithms depends on? 3. Differentiate: Correct & Incorrect Algorithms? 4. Write short note:

More information

Outline. NP-completeness. When is a problem easy? When is a problem hard? Today. Euler Circuits

Outline. NP-completeness. When is a problem easy? When is a problem hard? Today. Euler Circuits Outline NP-completeness Examples of Easy vs. Hard problems Euler circuit vs. Hamiltonian circuit Shortest Path vs. Longest Path 2-pairs sum vs. general Subset Sum Reducing one problem to another Clique

More information

Exam study sheet for CS2711. List of topics

Exam study sheet for CS2711. List of topics Exam study sheet for CS2711 Here is the list of topics you need to know for the final exam. For each data structure listed below, make sure you can do the following: 1. Give an example of this data structure

More information

Why? A central concept in Computer Science. Algorithms are ubiquitous.

Why? A central concept in Computer Science. Algorithms are ubiquitous. Analysis of Algorithms: A Brief Introduction Why? A central concept in Computer Science. Algorithms are ubiquitous. Using the Internet (sending email, transferring files, use of search engines, online

More information

Examination paper for MA0301 Elementær diskret matematikk

Examination paper for MA0301 Elementær diskret matematikk Department of Mathematical Sciences Examination paper for MA0301 Elementær diskret matematikk Academic contact during examination: Iris Marjan Smit a, Sverre Olaf Smalø b Phone: a 9285 0781, b 7359 1750

More information

Load Balancing and Termination Detection

Load Balancing and Termination Detection Chapter 7 slides7-1 Load Balancing and Termination Detection slides7-2 Load balancing used to distribute computations fairly across processors in order to obtain the highest possible execution speed. Termination

More information

Ordered Lists and Binary Trees

Ordered Lists and Binary Trees Data Structures and Algorithms Ordered Lists and Binary Trees Chris Brooks Department of Computer Science University of San Francisco Department of Computer Science University of San Francisco p.1/62 6-0:

More information

Handout #Ch7 San Skulrattanakulchai Gustavus Adolphus College Dec 6, 2010. Chapter 7: Digraphs

Handout #Ch7 San Skulrattanakulchai Gustavus Adolphus College Dec 6, 2010. Chapter 7: Digraphs MCS-236: Graph Theory Handout #Ch7 San Skulrattanakulchai Gustavus Adolphus College Dec 6, 2010 Chapter 7: Digraphs Strong Digraphs Definitions. A digraph is an ordered pair (V, E), where V is the set

More information

2.3 WINDOW-TO-VIEWPORT COORDINATE TRANSFORMATION

2.3 WINDOW-TO-VIEWPORT COORDINATE TRANSFORMATION 2.3 WINDOW-TO-VIEWPORT COORDINATE TRANSFORMATION A world-coordinate area selected for display is called a window. An area on a display device to which a window is mapped is called a viewport. The window

More information

A Review And Evaluations Of Shortest Path Algorithms

A Review And Evaluations Of Shortest Path Algorithms A Review And Evaluations Of Shortest Path Algorithms Kairanbay Magzhan, Hajar Mat Jani Abstract: Nowadays, in computer networks, the routing is based on the shortest path problem. This will help in minimizing

More information

1) The postfix expression for the infix expression A+B*(C+D)/F+D*E is ABCD+*F/DE*++

1) The postfix expression for the infix expression A+B*(C+D)/F+D*E is ABCD+*F/DE*++ Answer the following 1) The postfix expression for the infix expression A+B*(C+D)/F+D*E is ABCD+*F/DE*++ 2) Which data structure is needed to convert infix notations to postfix notations? Stack 3) The

More information

Chapter 7 Load Balancing and Termination Detection

Chapter 7 Load Balancing and Termination Detection Chapter 7 Load Balancing and Termination Detection Load balancing used to distribute computations fairly across processors in order to obtain the highest possible execution speed. Termination detection

More information

Load Balancing and Termination Detection

Load Balancing and Termination Detection Chapter 7 Slide 1 Slide 2 Load Balancing and Termination Detection Load balancing used to distribute computations fairly across processors in order to obtain the highest possible execution speed. Termination

More information

Solutions to Homework 6

Solutions to Homework 6 Solutions to Homework 6 Debasish Das EECS Department, Northwestern University ddas@northwestern.edu 1 Problem 5.24 We want to find light spanning trees with certain special properties. Given is one example

More information

Data Structures and Algorithms

Data Structures and Algorithms Data Structures and Algorithms CS245-2016S-06 Binary Search Trees David Galles Department of Computer Science University of San Francisco 06-0: Ordered List ADT Operations: Insert an element in the list

More information

Computer Algorithms. NP-Complete Problems. CISC 4080 Yanjun Li

Computer Algorithms. NP-Complete Problems. CISC 4080 Yanjun Li Computer Algorithms NP-Complete Problems NP-completeness The quest for efficient algorithms is about finding clever ways to bypass the process of exhaustive search, using clues from the input in order

More information

BSc (Hons) Business Information Systems, BSc (Hons) Computer Science with Network Security. & BSc. (Hons.) Software Engineering

BSc (Hons) Business Information Systems, BSc (Hons) Computer Science with Network Security. & BSc. (Hons.) Software Engineering BSc (Hons) Business Information Systems, BSc (Hons) Computer Science with Network Security & BSc. (Hons.) Software Engineering Cohort: BIS/05/FT BCNS/05/FT BSE/05/FT Examinations for 2005-2006 / Semester

More information

Sociology and CS. Small World. Sociology Problems. Degree of Separation. Milgram s Experiment. How close are people connected? (Problem Understanding)

Sociology and CS. Small World. Sociology Problems. Degree of Separation. Milgram s Experiment. How close are people connected? (Problem Understanding) Sociology Problems Sociology and CS Problem 1 How close are people connected? Small World Philip Chan Problem 2 Connector How close are people connected? (Problem Understanding) Small World Are people

More information

CompSci-61B, Data Structures Final Exam

CompSci-61B, Data Structures Final Exam Your Name: CompSci-61B, Data Structures Final Exam Your 8-digit Student ID: Your CS61B Class Account Login: This is a final test for mastery of the material covered in our labs, lectures, and readings.

More information

WAN Wide Area Networks. Packet Switch Operation. Packet Switches. COMP476 Networked Computer Systems. WANs are made of store and forward switches.

WAN Wide Area Networks. Packet Switch Operation. Packet Switches. COMP476 Networked Computer Systems. WANs are made of store and forward switches. Routing WAN Wide Area Networks WANs are made of store and forward switches. To there and back again COMP476 Networked Computer Systems A packet switch with two types of I/O connectors: one type is used

More information

Bicolored Shortest Paths in Graphs with Applications to Network Overlay Design

Bicolored Shortest Paths in Graphs with Applications to Network Overlay Design Bicolored Shortest Paths in Graphs with Applications to Network Overlay Design Hongsik Choi and Hyeong-Ah Choi Department of Electrical Engineering and Computer Science George Washington University Washington,

More information

An Introduction to APGL

An Introduction to APGL An Introduction to APGL Charanpal Dhanjal February 2012 Abstract Another Python Graph Library (APGL) is a graph library written using pure Python, NumPy and SciPy. Users new to the library can gain an

More information

Chapter 6: Graph Theory

Chapter 6: Graph Theory Chapter 6: Graph Theory Graph theory deals with routing and network problems and if it is possible to find a best route, whether that means the least expensive, least amount of time or the least distance.

More information

Reductions & NP-completeness as part of Foundations of Computer Science undergraduate course

Reductions & NP-completeness as part of Foundations of Computer Science undergraduate course Reductions & NP-completeness as part of Foundations of Computer Science undergraduate course Alex Angelopoulos, NTUA January 22, 2015 Outline Alex Angelopoulos (NTUA) FoCS: Reductions & NP-completeness-

More information

Decision Mathematics 1 TUESDAY 22 JANUARY 2008

Decision Mathematics 1 TUESDAY 22 JANUARY 2008 ADVANCED SUBSIDIARY GCE 4736/01 MATHEMATICS Decision Mathematics 1 TUESDAY 22 JANUARY 2008 Additional materials: Answer Booklet (8 pages) Graph paper Insert for Questions 3 and 4 List of Formulae (MF1)

More information

5. A full binary tree with n leaves contains [A] n nodes. [B] log n 2 nodes. [C] 2n 1 nodes. [D] n 2 nodes.

5. A full binary tree with n leaves contains [A] n nodes. [B] log n 2 nodes. [C] 2n 1 nodes. [D] n 2 nodes. 1. The advantage of.. is that they solve the problem if sequential storage representation. But disadvantage in that is they are sequential lists. [A] Lists [B] Linked Lists [A] Trees [A] Queues 2. The

More information

International Journal of Software and Web Sciences (IJSWS) www.iasir.net

International Journal of Software and Web Sciences (IJSWS) www.iasir.net International Association of Scientific Innovation and Research (IASIR) (An Association Unifying the Sciences, Engineering, and Applied Research) ISSN (Print): 2279-0063 ISSN (Online): 2279-0071 International

More information

OPTIMAL DESIGN OF DISTRIBUTED SENSOR NETWORKS FOR FIELD RECONSTRUCTION

OPTIMAL DESIGN OF DISTRIBUTED SENSOR NETWORKS FOR FIELD RECONSTRUCTION OPTIMAL DESIGN OF DISTRIBUTED SENSOR NETWORKS FOR FIELD RECONSTRUCTION Sérgio Pequito, Stephen Kruzick, Soummya Kar, José M. F. Moura, A. Pedro Aguiar Department of Electrical and Computer Engineering

More information

Decision Mathematics D1 Advanced/Advanced Subsidiary. Tuesday 5 June 2007 Afternoon Time: 1 hour 30 minutes

Decision Mathematics D1 Advanced/Advanced Subsidiary. Tuesday 5 June 2007 Afternoon Time: 1 hour 30 minutes Paper Reference(s) 6689/01 Edexcel GCE Decision Mathematics D1 Advanced/Advanced Subsidiary Tuesday 5 June 2007 Afternoon Time: 1 hour 30 minutes Materials required for examination Nil Items included with

More information

Data Structures. Chapter 8

Data Structures. Chapter 8 Chapter 8 Data Structures Computer has to process lots and lots of data. To systematically process those data efficiently, those data are organized as a whole, appropriate for the application, called a

More information

V. Adamchik 1. Graph Theory. Victor Adamchik. Fall of 2005

V. Adamchik 1. Graph Theory. Victor Adamchik. Fall of 2005 V. Adamchik 1 Graph Theory Victor Adamchik Fall of 2005 Plan 1. Basic Vocabulary 2. Regular graph 3. Connectivity 4. Representing Graphs Introduction A.Aho and J.Ulman acknowledge that Fundamentally, computer

More information

Directed Graphs. digraph search transitive closure topological sort strong components. References: Algorithms in Java, Chapter 19

Directed Graphs. digraph search transitive closure topological sort strong components. References: Algorithms in Java, Chapter 19 Directed Graphs digraph search transitive closure topological sort strong components References: Algorithms in Java, Chapter 19 http://www.cs.princeton.edu/introalgsds/52directed 1 Directed graphs (digraphs)

More information

Approximation Algorithms

Approximation Algorithms Approximation Algorithms or: How I Learned to Stop Worrying and Deal with NP-Completeness Ong Jit Sheng, Jonathan (A0073924B) March, 2012 Overview Key Results (I) General techniques: Greedy algorithms

More information

3. The Junction Tree Algorithms

3. The Junction Tree Algorithms A Short Course on Graphical Models 3. The Junction Tree Algorithms Mark Paskin mark@paskin.org 1 Review: conditional independence Two random variables X and Y are independent (written X Y ) iff p X ( )

More information

Mining Social Network Graphs

Mining Social Network Graphs Mining Social Network Graphs Debapriyo Majumdar Data Mining Fall 2014 Indian Statistical Institute Kolkata November 13, 17, 2014 Social Network No introduc+on required Really? We s7ll need to understand

More information

Picture Maze Generation by Successive Insertion of Path Segment

Picture Maze Generation by Successive Insertion of Path Segment 1 2 3 Picture Maze Generation by Successive Insertion of Path Segment 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. ABSTRACT Tomio Kurokawa 1* 1 Aichi Institute of Technology,

More information

Dynamic programming. Doctoral course Optimization on graphs - Lecture 4.1. Giovanni Righini. January 17 th, 2013

Dynamic programming. Doctoral course Optimization on graphs - Lecture 4.1. Giovanni Righini. January 17 th, 2013 Dynamic programming Doctoral course Optimization on graphs - Lecture.1 Giovanni Righini January 1 th, 201 Implicit enumeration Combinatorial optimization problems are in general NP-hard and we usually

More information

The Union-Find Problem Kruskal s algorithm for finding an MST presented us with a problem in data-structure design. As we looked at each edge,

The Union-Find Problem Kruskal s algorithm for finding an MST presented us with a problem in data-structure design. As we looked at each edge, The Union-Find Problem Kruskal s algorithm for finding an MST presented us with a problem in data-structure design. As we looked at each edge, cheapest first, we had to determine whether its two endpoints

More information

Mathematics for Algorithm and System Analysis

Mathematics for Algorithm and System Analysis Mathematics for Algorithm and System Analysis for students of computer and computational science Edward A. Bender S. Gill Williamson c Edward A. Bender & S. Gill Williamson 2005. All rights reserved. Preface

More information

Diversity Coloring for Distributed Data Storage in Networks 1

Diversity Coloring for Distributed Data Storage in Networks 1 Diversity Coloring for Distributed Data Storage in Networks 1 Anxiao (Andrew) Jiang and Jehoshua Bruck California Institute of Technology Pasadena, CA 9115, U.S.A. {jax, bruck}@paradise.caltech.edu Abstract

More information

Problem Set 7 Solutions

Problem Set 7 Solutions 8 8 Introduction to Algorithms May 7, 2004 Massachusetts Institute of Technology 6.046J/18.410J Professors Erik Demaine and Shafi Goldwasser Handout 25 Problem Set 7 Solutions This problem set is due in

More information

International Journal of Advanced Research in Computer Science and Software Engineering

International Journal of Advanced Research in Computer Science and Software Engineering Volume 3, Issue 7, July 23 ISSN: 2277 28X International Journal of Advanced Research in Computer Science and Software Engineering Research Paper Available online at: www.ijarcsse.com Greedy Algorithm:

More information

Warshall s Algorithm: Transitive Closure

Warshall s Algorithm: Transitive Closure CS 0 Theory of Algorithms / CS 68 Algorithms in Bioinformaticsi Dynamic Programming Part II. Warshall s Algorithm: Transitive Closure Computes the transitive closure of a relation (Alternatively: all paths

More information

http://www.castlelearning.com/review/teacher/assignmentprinting.aspx 5. 2 6. 2 1. 10 3. 70 2. 55 4. 180 7. 2 8. 4

http://www.castlelearning.com/review/teacher/assignmentprinting.aspx 5. 2 6. 2 1. 10 3. 70 2. 55 4. 180 7. 2 8. 4 of 9 1/28/2013 8:32 PM Teacher: Mr. Sime Name: 2 What is the slope of the graph of the equation y = 2x? 5. 2 If the ratio of the measures of corresponding sides of two similar triangles is 4:9, then the

More information

How to Write a Checker in 24 Hours

How to Write a Checker in 24 Hours How to Write a Checker in 24 Hours Clang Static Analyzer Anna Zaks and Jordan Rose Apple Inc. What is this talk about? The Clang Static Analyzer is a bug finding tool It can be extended with custom checkers

More information

MATHEMATICS Unit Decision 1

MATHEMATICS Unit Decision 1 General Certificate of Education January 2007 Advanced Subsidiary Examination MATHEMATICS Unit Decision 1 MD01 Tuesday 16 January 2007 9.00 am to 10.30 am For this paper you must have: an 8-page answer

More information

Exponential time algorithms for graph coloring

Exponential time algorithms for graph coloring Exponential time algorithms for graph coloring Uriel Feige Lecture notes, March 14, 2011 1 Introduction Let [n] denote the set {1,..., k}. A k-labeling of vertices of a graph G(V, E) is a function V [k].

More information

Simple Graphs Degrees, Isomorphism, Paths

Simple Graphs Degrees, Isomorphism, Paths Mathematics for Computer Science MIT 6.042J/18.062J Simple Graphs Degrees, Isomorphism, Types of Graphs Simple Graph this week Multi-Graph Directed Graph next week Albert R Meyer, March 10, 2010 lec 6W.1

More information

Boolean Algebra Part 1

Boolean Algebra Part 1 Boolean Algebra Part 1 Page 1 Boolean Algebra Objectives Understand Basic Boolean Algebra Relate Boolean Algebra to Logic Networks Prove Laws using Truth Tables Understand and Use First Basic Theorems

More information

Asynchronous Computations

Asynchronous Computations Asynchronous Computations Asynchronous Computations Computations in which individual processes operate without needing to synchronize with other processes. Synchronizing processes is an expensive operation

More information

Computational Geometry. Lecture 1: Introduction and Convex Hulls

Computational Geometry. Lecture 1: Introduction and Convex Hulls Lecture 1: Introduction and convex hulls 1 Geometry: points, lines,... Plane (two-dimensional), R 2 Space (three-dimensional), R 3 Space (higher-dimensional), R d A point in the plane, 3-dimensional space,

More information

UPPER BOUNDS ON THE L(2, 1)-LABELING NUMBER OF GRAPHS WITH MAXIMUM DEGREE

UPPER BOUNDS ON THE L(2, 1)-LABELING NUMBER OF GRAPHS WITH MAXIMUM DEGREE UPPER BOUNDS ON THE L(2, 1)-LABELING NUMBER OF GRAPHS WITH MAXIMUM DEGREE ANDREW LUM ADVISOR: DAVID GUICHARD ABSTRACT. L(2,1)-labeling was first defined by Jerrold Griggs [Gr, 1992] as a way to use graphs

More information

5.1 Midsegment Theorem and Coordinate Proof

5.1 Midsegment Theorem and Coordinate Proof 5.1 Midsegment Theorem and Coordinate Proof Obj.: Use properties of midsegments and write coordinate proofs. Key Vocabulary Midsegment of a triangle - A midsegment of a triangle is a segment that connects

More information

Optimized e-transportation System customized to user need

Optimized e-transportation System customized to user need Optimized e-transportation System customized to user need 1 Ankita Bihani, 2 Pooja Kabra, 3 Supriya Moond 1 School of Computing Sciences and Engineering, VIT University 2 School of Computing Sciences and

More information

The Classes P and NP. mohamed@elwakil.net

The Classes P and NP. mohamed@elwakil.net Intractable Problems The Classes P and NP Mohamed M. El Wakil mohamed@elwakil.net 1 Agenda 1. What is a problem? 2. Decidable or not? 3. The P class 4. The NP Class 5. TheNP Complete class 2 What is a

More information

6.852: Distributed Algorithms Fall, 2009. Class 2

6.852: Distributed Algorithms Fall, 2009. Class 2 .8: Distributed Algorithms Fall, 009 Class Today s plan Leader election in a synchronous ring: Lower bound for comparison-based algorithms. Basic computation in general synchronous networks: Leader election

More information

Static Load Balancing

Static Load Balancing Load Balancing Load Balancing Load balancing: distributing data and/or computations across multiple processes to maximize efficiency for a parallel program. Static load-balancing: the algorithm decides

More information

1 Definitions. Supplementary Material for: Digraphs. Concept graphs

1 Definitions. Supplementary Material for: Digraphs. Concept graphs Supplementary Material for: van Rooij, I., Evans, P., Müller, M., Gedge, J. & Wareham, T. (2008). Identifying Sources of Intractability in Cognitive Models: An Illustration using Analogical Structure Mapping.

More information

Graph Theory Algorithms for Mobile Ad Hoc Networks

Graph Theory Algorithms for Mobile Ad Hoc Networks Informatica 36 (2012) 185-200 185 Graph Theory Algorithms for Mobile Ad Hoc Networks Natarajan Meghanathan Department of Computer Science, Jackson State University Jackson, MS 39217, USA E-mail: natarajan.meghanathan@jsums.edu

More information

Binary storage of graphs and related data

Binary storage of graphs and related data EÖTVÖS LORÁND UNIVERSITY Faculty of Informatics Department of Algorithms and their Applications Binary storage of graphs and related data BSc thesis Author: Frantisek Csajka full-time student Informatics

More information

Large induced subgraphs with all degrees odd

Large induced subgraphs with all degrees odd Large induced subgraphs with all degrees odd A.D. Scott Department of Pure Mathematics and Mathematical Statistics, University of Cambridge, England Abstract: We prove that every connected graph of order

More information

Minimum cost maximum flow, Minimum cost circulation, Cost/Capacity scaling

Minimum cost maximum flow, Minimum cost circulation, Cost/Capacity scaling 6.854 Advanced Algorithms Lecture 16: 10/11/2006 Lecturer: David Karger Scribe: Kermin Fleming and Chris Crutchfield, based on notes by Wendy Chu and Tudor Leu Minimum cost maximum flow, Minimum cost circulation,

More information

Binary Trees and Huffman Encoding Binary Search Trees

Binary Trees and Huffman Encoding Binary Search Trees Binary Trees and Huffman Encoding Binary Search Trees Computer Science E119 Harvard Extension School Fall 2012 David G. Sullivan, Ph.D. Motivation: Maintaining a Sorted Collection of Data A data dictionary

More information

Lecture 15 An Arithmetic Circuit Lowerbound and Flows in Graphs

Lecture 15 An Arithmetic Circuit Lowerbound and Flows in Graphs CSE599s: Extremal Combinatorics November 21, 2011 Lecture 15 An Arithmetic Circuit Lowerbound and Flows in Graphs Lecturer: Anup Rao 1 An Arithmetic Circuit Lower Bound An arithmetic circuit is just like

More information

Euclidean Minimum Spanning Trees Based on Well Separated Pair Decompositions Chaojun Li. Advised by: Dave Mount. May 22, 2014

Euclidean Minimum Spanning Trees Based on Well Separated Pair Decompositions Chaojun Li. Advised by: Dave Mount. May 22, 2014 Euclidean Minimum Spanning Trees Based on Well Separated Pair Decompositions Chaojun Li Advised by: Dave Mount May 22, 2014 1 INTRODUCTION In this report we consider the implementation of an efficient

More information

Class One: Degree Sequences

Class One: Degree Sequences Class One: Degree Sequences For our purposes a graph is a just a bunch of points, called vertices, together with lines or curves, called edges, joining certain pairs of vertices. Three small examples of

More information

root node level: internal node edge leaf node CS@VT Data Structures & Algorithms 2000-2009 McQuain

root node level: internal node edge leaf node CS@VT Data Structures & Algorithms 2000-2009 McQuain inary Trees 1 A binary tree is either empty, or it consists of a node called the root together with two binary trees called the left subtree and the right subtree of the root, which are disjoint from each

More information

CIS 700: algorithms for Big Data

CIS 700: algorithms for Big Data CIS 700: algorithms for Big Data Lecture 6: Graph Sketching Slides at http://grigory.us/big-data-class.html Grigory Yaroslavtsev http://grigory.us Sketching Graphs? We know how to sketch vectors: v Mv

More information

1. Relational database accesses data in a sequential form. (Figures 7.1, 7.2)

1. Relational database accesses data in a sequential form. (Figures 7.1, 7.2) Chapter 7 Data Structures for Computer Graphics (This chapter was written for programmers - option in lecture course) Any computer model of an Object must comprise three different types of entities: 1.

More information

Contents. System Development Models and Methods. Design Abstraction and Views. Synthesis. Control/Data-Flow Models. System Synthesis Models

Contents. System Development Models and Methods. Design Abstraction and Views. Synthesis. Control/Data-Flow Models. System Synthesis Models System Development Models and Methods Dipl.-Inf. Mirko Caspar Version: 10.02.L.r-1.0-100929 Contents HW/SW Codesign Process Design Abstraction and Views Synthesis Control/Data-Flow Models System Synthesis

More information

A binary heap is a complete binary tree, where each node has a higher priority than its children. This is called heap-order property

A binary heap is a complete binary tree, where each node has a higher priority than its children. This is called heap-order property CmSc 250 Intro to Algorithms Chapter 6. Transform and Conquer Binary Heaps 1. Definition A binary heap is a complete binary tree, where each node has a higher priority than its children. This is called

More information

WOLLONGONG COLLEGE AUSTRALIA. Diploma in Information Technology

WOLLONGONG COLLEGE AUSTRALIA. Diploma in Information Technology First Name: Family Name: Student Number: Class/Tutorial: WOLLONGONG COLLEGE AUSTRALIA A College of the University of Wollongong Diploma in Information Technology Final Examination Spring Session 2008 WUCT121

More information

Report on the Train Ticketing System

Report on the Train Ticketing System Report on the Train Ticketing System Author: Zaobo He, Bing Jiang, Zhuojun Duan 1.Introduction... 2 1.1 Intentions... 2 1.2 Background... 2 2. Overview of the Tasks... 3 2.1 Modules of the system... 3

More information

Question 1. [7 points] Consider the following scenario and assume host H s routing table is the one given below:

Question 1. [7 points] Consider the following scenario and assume host H s routing table is the one given below: Computer Networks II Master degree in Computer Engineering Exam session: 11/02/2009 Teacher: Emiliano Trevisani Last name First name Student Identification number You are only allowed to use a pen and

More information

Keywords are identifiers having predefined meanings in C programming language. The list of keywords used in standard C are : unsigned void

Keywords are identifiers having predefined meanings in C programming language. The list of keywords used in standard C are : unsigned void 1. Explain C tokens Tokens are basic building blocks of a C program. A token is the smallest element of a C program that is meaningful to the compiler. The C compiler recognizes the following kinds of

More information

MATHEMATICS Unit Decision 1

MATHEMATICS Unit Decision 1 General Certificate of Education June 2007 Advanced Subsidiary Examination MATHEMATICS Unit Decision 1 MD01 Thursday 7 June 2007 9.00 am to 10.30 am For this paper you must have: an 8-page answer book

More information

Data Structure and Algorithm I Midterm Examination 120 points Time: 9:10am-12:10pm (180 minutes), Friday, November 12, 2010

Data Structure and Algorithm I Midterm Examination 120 points Time: 9:10am-12:10pm (180 minutes), Friday, November 12, 2010 Data Structure and Algorithm I Midterm Examination 120 points Time: 9:10am-12:10pm (180 minutes), Friday, November 12, 2010 Problem 1. In each of the following question, please specify if the statement

More information

Project Report BIG-DATA CONTENT RETRIEVAL, STORAGE AND ANALYSIS FOUNDATIONS OF DATA-INTENSIVE COMPUTING. Masters in Computer Science

Project Report BIG-DATA CONTENT RETRIEVAL, STORAGE AND ANALYSIS FOUNDATIONS OF DATA-INTENSIVE COMPUTING. Masters in Computer Science Data Intensive Computing CSE 486/586 Project Report BIG-DATA CONTENT RETRIEVAL, STORAGE AND ANALYSIS FOUNDATIONS OF DATA-INTENSIVE COMPUTING Masters in Computer Science University at Buffalo Website: http://www.acsu.buffalo.edu/~mjalimin/

More information

INTERNATIONAL JOURNAL OF PURE AND APPLIED RESEARCH IN ENGINEERING AND TECHNOLOGY

INTERNATIONAL JOURNAL OF PURE AND APPLIED RESEARCH IN ENGINEERING AND TECHNOLOGY INTERNATIONAL JOURNAL OF PURE AND APPLIED RESEARCH IN ENGINEERING AND TECHNOLOGY A PATH FOR HORIZING YOUR INNOVATIVE WORK A REVIEW ON THE USAGE OF OLD AND NEW DATA STRUCTURE ARRAYS, LINKED LIST, STACK,

More information