Alternate Algorithm for DSM Partitioning

Size: px
Start display at page:

Download "Alternate Algorithm for DSM Partitioning"

Transcription

1 lternate lgorithm for SM Partitioning Power of djacency Matrix pproach Using the power of the adjacency matrix approach, a binary SM is raised to its n th power to indicate which tasks can be traced back to themselves in n steps; thus constituting a cycle (Yassine et al., ). owever, eo (7) reported that when a matrix is raised to its n th power, the n-zero numbers represent the edge sequence and t a path (an edge may appear more than once in an edge sequence but t in a path). or illustration, consider an activity SM as seen in igure (a). nitially topological sorting is performed to determine the acyclic activities (dependent and independent). This process is achieved by tracing the empty rows and empty columns for identifying the acyclic activities at the start and end of the project respectively (refer igures (b) to (d)). ctivities and does t contain any marks along the rows and hence they are moved to the top of the matrix as shown in igure (b)-(c). Similarly, activities and contain empty columns and they are moved to the bottom of the matrix and omitted from further consideration as seen in igure (d). The active matrix after topological sorting is displayed in igure (e). This active matrix as represented in igure (a) (same as igure (e)) is converted to binary matrix by replacing the cells with marks as and all other empty cells as as shown in igure (b). Now, the binary matrix is raised to its powers till n ( n number of activities in the active matrix). igures (c) to (h) represents the powers of the binary matrix. nalyzing the n-zero value of the diagonal cell for instance, say at power as in igure (f). This implies that the length of edge sequence/cycle formed is. onsider the nzero value along the diagonal for activity is. This implies that there are numbers of edge sequences (of length ) can be formed starting with activity. ence, from the above observation, the numbers of edge sequences of length are and as visualized in igure (f). The five-step cycles formed with activity are t simple cycles ( Simple cycles are the cycles with repetition of des except the first one). s there are repeating edges, the cycles identified from igure (f) are t simple cycles. or matrices with densely populated marks, the diagonal cell values adds confusion and it fails to identify the cycles/blocks. ence, this method cant identify the blocks for any network pattern. ut, the significance of the n-zero value along the diagonal at the n th power can be utilized in detecting minimum length simple cycles. This is because, edges cant occur more than

2 igure lock iagram to llustrate Topological Sorting igure (a) ctivity SM igure (b) Topological Sorting igure (c) Topological Sorting igure (d) Topological Sorting igure (e) ctive Matrix after Topological Sorting igure (f) raphical Representation of the ctive Matrix

3 igure lock iagram to llustrate Power of djacency Matrix pproach igure (b) inary Matrix igure (a) ctive Matrix after Topological Sorting igure (c) Power igure (d) Power igure (g) Power 6 igure (f) Power igure (h) Power igure (e) Power

4 once (other than the first and last) in minimum length edge sequences and hence the cycles formed are always simple. Proposed Partitioning Procedure The algorithm or procedure for the formation of partitioned SM is represented in the form of a flowchart in igure and is explained below:. onsider an activity SM.. heck for the presence of any mark along the upper diagonal of the matrix. f there are marks along the upper diagonal it implies that the matrix is partitioned. ence, stop the procedure or else continue with step. (a) Topological Sorting. heck for empty rows in the matrix and move all those empty rows to the top of the matrix and the corresponding columns to the left of the matrix and omit those activities for further consideration. o The remaining activities in the matrix form the active matrix.. or the reduced (active) matrix, check for any empty columns and move all those empty columns to the right and the corresponding rows to the bottom of the active matrix and block those activities from further consideration.. Repeat steps & until there are empty rows & columns in the active matrix (b) dentification of Loops 6. Select the activities in the active matrix along with the dependency relationships for identifying loops Loops are identified by the repeated process of powers of adjacency matrix and condensation of the strongly connected components. (i) djacency Matrix Multiplication 7. onvert the active matrix into a binary matrix (adjacency matrix) by replacing the marks with and all other empty cells as. 8. Raise the power of the adjacency matrix until you identify any n-zero entry along the diagonal. The maximum power can be anywhere till the order of number of activities in the current active matrix. o o f there occurs any n-zero entry along the diagonal, stop the process or else continue the matrix multiplication until the power is raised to n, where n is the number of activities in the current active matrix. The resultant matrix where the matrix multiplication is stopped is the end matrix.. On analyzing the end matrix, the corresponding power at which the end matrix is identified signifies the length of the cycle and the number formed along the diagonal during the matrix multiplication represents the number of cycles that particular activity is involved in.

5 Start Matrix Multiplication in detecting cycles/ loops heck for marks above the diagonal? heck for empty rows? Topological Sorting lgorithm onvert the active matrix to binary dentify the activities corresponding to the n-zero entry Raise the powers of the matrix until there occurs an n-zero entry along the diagonal anywhere before power n or else stop at power n heck for empty columns? Move that particular row & column to the top & left respectively & omit those activities for further consideration ondensation of the Strongly onnected omponents heck for individual loops (strongly connected components) for the identified activities ollapse/ merge all the activities in the loop into a single activity Repeat topological sorting Move that particular row & column to the bottom & right respectively & omit those activities for further consideration heck for marks above the diagonal? ighlight the individual condensed loops as blocked activities re there empty rows or columns? Repeat matrix multiplication Partitioned SM n Number of activities in the current matrix Note: ence, the value of n is dynamic igure lowchart for Proposed Partitioning lgorithm

6 o f the power is square, the corresponding length of the cycle is ; if it is cube, the corresponding cycle length is and so on. onsider an active matrix of size. On raising the binary matrix to its powers, say at cube, if n-zero entry occurred along the diagonal, the matrix multiplication process would be stopped. The cube matrix forms the end matrix. On analyzing the end matrix, if the number along the diagonal for an activity say was it implies that activity is involved in two numbers of three-step cycle. (ii) Strongly onnected omponents & ondensation. Trace out the pairs/cycles among the identified activities.. Move those activities (which form a cycle) together and merge/condense all those activities together o f the cycles are overlapping, bring all those activities together and merge them together (c) dentification of locks. The condensed matrix forms the present active matrix. Repeat topological sorting for the condensed matrix and check for any upper diagonal marks. f there are marks above the diagonal it implies the presence of loops and hence repeat the process from step 7 or else continue to step. The condensed activities represent blocks and the resultant matrix is the partitioned SM. llustration The example shown in igure (a) is used for the present illustration. fter topological sorting, the matrix of activities reduces to 7 activities as shown in igure (d). Remaining activities form the active matrix as revealed in igure (e). This active matrix is converted to a binary matrix as seen in igure (a). On raising the binary matrix to its powers, n-zero value occurred along the diagonal at power as in igure (b) for activities, and. This implies that activities, and are involved in a cycle of length (interdependent). The numbers along the diagonal for the above activities was,, and and it corresponds to the number of cycles each activity is involved in. The above concepts imply that activities and are each involved in one cycle of length two and activity is involved in two cycles of length two. The two two-step cycles are traced out as -- and --. Since activity is common in both the loops; all the three activities had been combined together as a single activity through condensation process and is illustrated in igure (c). Perform topological sorting to detect any acyclic activities. f the matrix still has marks above the diagonal, repeat the steps such as matrix multiplication, condensation and topological

7 igure lock iagram for ormation of Partitioned SM (Proposed pproach) igure (a) inary (ctive) Matrix igure (b) Power igure (c) ctive Matrix after ondensation & Topological Sorting igure (e) Power igure (f) ctive Matrix after ondensation igure (l) Partitioned SM igure (h) Power igure (i) Power igure (k) inal ondensed Matrix after Topological Sorting igure (j) ctive Matrix after ondensation igure (d) inary Matrix igure (g) inary Matrix

8 sorting to form the final condensed matrix and is illustrated through igures (d) to (k). Once the matrix multiplication cant identify any loops, the above process is stopped. The condensed activities are unmerged into the rmal activities and this matrix forms the partitioned SM and is as shown in igure (l). This partitioned SM reveals two blocks through the above approach. iscussion & onclusion Mathematical proof for the proposed algorithm is attempted (Maheswari, 6). This algorithm, which is based on powers of adjacency matrix, can partition any matrix OMPLTLY. The drawback with this approach is its incapability in identifying LL the individual cycles/loops (it can identify only the minimum length cycle available in a particular block). urther, it is t as NT as available alternate algorithms. ence, this algorithm is t recommended for developing SM software. References. eo, N. (7) raph Theory with pplications to ngineering and omputer Science, Prentice all ndia.. Maheswari,. U. (6) Modeling ctivity Sequencing for onstruction Projects using ependency Structure Matrix, Ph Thesis, T Madras.. Yassine,.,. alkenburg, and. helst () ngineering esign Management: n nformation Structure pproach. nternational ournal of Production Research, 7 (), 7 7.

Simplification of two-level combinational logic

Simplification of two-level combinational logic ombinational logic optimization! lternate representations of oolean functions " cubes " karnaugh maps! Simplification " two-level simplification " exploiting don t cares " algorithm for simplification

More information

New Optimal Layer Assignment for Bus-Oriented Escape Routing

New Optimal Layer Assignment for Bus-Oriented Escape Routing New Optimal Layer ssignment for us-oriented scape Routing in-tai Yan epartment of omputer Science and nformation ngineering, hung-ua University, sinchu, Taiwan, R. O.. STRT n this paper, based on the optimal

More information

Design and Analysis of Algorithms

Design and Analysis of Algorithms S 101, Winter 2018 esign and nalysis of lgorithms Lecture 3: onnected omponents lass URL: http://vlsicad.ucsd.edu/courses/cse101-w18/ nd of Lecture 2: s, Topological Order irected cyclic raph.g., Vertices

More information

CSE 101- Winter 18 Discussion Section Week 2

CSE 101- Winter 18 Discussion Section Week 2 S 101- Winter 18 iscussion Section Week 2 Topics Topological ordering Strongly connected components Binary search ntroduction to ivide and onquer algorithms Topological ordering Topological ordering =>

More information

Analysis of Algorithms Prof. Karen Daniels

Analysis of Algorithms Prof. Karen Daniels UMass Lowell omputer Science 91.404 nalysis of lgorithms Prof. Karen aniels Spring, 2013 hapter 22: raph lgorithms & rief Introduction to Shortest Paths [Source: ormen et al. textbook except where noted]

More information

Module 5 Graph Algorithms

Module 5 Graph Algorithms Module 5 Graph lgorithms Dr. Natarajan Meghanathan Professor of Computer Science Jackson State University Jackson, MS 97 E-mail: natarajan.meghanathan@jsums.edu 5. Graph Traversal lgorithms Depth First

More information

Lecture 22 Tuesday, April 10

Lecture 22 Tuesday, April 10 CIS 160 - Spring 2018 (instructor Val Tannen) Lecture 22 Tuesday, April 10 GRAPH THEORY Directed Graphs Directed graphs (a.k.a. digraphs) are an important mathematical modeling tool in Computer Science,

More information

Data Organization: Creating Order Out Of Chaos

Data Organization: Creating Order Out Of Chaos ata Organization: reating Order Out Of haos 3 Non-linear data structures 5-5 Principles of omputation, arnegie Mellon University - ORTIN Trees tree is a hierarchical (usually non-linear) data structure.

More information

Chapter 9: Elementary Graph Algorithms Basic Graph Concepts

Chapter 9: Elementary Graph Algorithms Basic Graph Concepts hapter 9: Elementary Graph lgorithms asic Graph oncepts msc 250 Intro to lgorithms graph is a mathematical object that is used to model different situations objects and processes: Linked list Tree (partial

More information

Digraphs ( 12.4) Directed Graphs. Digraph Application. Digraph Properties. A digraph is a graph whose edges are all directed.

Digraphs ( 12.4) Directed Graphs. Digraph Application. Digraph Properties. A digraph is a graph whose edges are all directed. igraphs ( 12.4) irected Graphs OR OS digraph is a graph whose edges are all directed Short for directed graph pplications one- way streets flights task scheduling irected Graphs 1 irected Graphs 2 igraph

More information

Efficient Rectilinear Steiner Tree Construction with Rectangular Obstacles

Efficient Rectilinear Steiner Tree Construction with Rectangular Obstacles Proceedings of the th WSES Int. onf. on IRUITS, SYSTEMS, ELETRONIS, ONTROL & SIGNL PROESSING, allas, US, November 1-3, 2006 204 Efficient Rectilinear Steiner Tree onstruction with Rectangular Obstacles

More information

Reading. Graph Introduction. What are graphs? Graphs. Motivation for Graphs. Varieties. Reading. CSE 373 Data Structures Lecture 18

Reading. Graph Introduction. What are graphs? Graphs. Motivation for Graphs. Varieties. Reading. CSE 373 Data Structures Lecture 18 Reading Graph Introduction Reading Section 9.1 S 373 ata Structures Lecture 18 11/25/02 Graph Introduction - Lecture 18 2 What are graphs? Yes, this is a graph. Graphs Graphs are composed of Nodes (vertices)

More information

Graphs: Connectivity. A graph. Transportation systems. Social networks

Graphs: Connectivity. A graph. Transportation systems. Social networks graph raphs: onnectivity ordi ortadella and ordi Petit epartment of omputer Science Source: Wikipedia The network graph formed by Wikipedia editors (edges) contributing to different Wikipedia language

More information

System of Systems Complexity Identification and Control

System of Systems Complexity Identification and Control System of Systems Identification and ontrol Joseph J. Simpson Systems oncepts nd ve NW # Seattle, W jjs-sbw@eskimo.com Mary J. Simpson Systems oncepts nd ve NW # Seattle, W mjs-sbw@eskimo.com bstract System

More information

Note. Out of town Thursday afternoon. Willing to meet before 1pm, me if you want to meet then so I know to be in my office

Note. Out of town Thursday afternoon. Willing to meet before 1pm,  me if you want to meet then so I know to be in my office raphs and Trees Note Out of town Thursday afternoon Willing to meet before pm, email me if you want to meet then so I know to be in my office few extra remarks about recursion If you can write it recursively

More information

DFS on Directed Graphs BOS. Outline and Reading ( 6.4) Digraphs. Reachability ( 6.4.1) Directed Acyclic Graphs (DAG s) ( 6.4.4)

DFS on Directed Graphs BOS. Outline and Reading ( 6.4) Digraphs. Reachability ( 6.4.1) Directed Acyclic Graphs (DAG s) ( 6.4.4) S on irected Graphs OS OR JK SO LX W MI irected Graphs S 1.3 1 Outline and Reading ( 6.4) Reachability ( 6.4.1) irected S Strong connectivity irected cyclic Graphs (G s) ( 6.4.4) Topological Sorting irected

More information

The University of Sydney MATH 2009

The University of Sydney MATH 2009 The University of Sydney MTH 009 GRPH THORY Tutorial 10 Solutions 004 1. In a tournament, the score of a vertex is its out-degree, and the score sequence is a list of all the scores in non-decreasing order.

More information

The Need for a Process Mining Evaluation Framework in Research and Practice

The Need for a Process Mining Evaluation Framework in Research and Practice The Need for a Process Mining valuation ramework in Research and Practice Position Paper. Rozinat,.K. lves de Medeiros,.W. ünther,.j.m.m. Weijters, and W.M.P. van der alst indhoven University of Technology

More information

MT365 Examination 2007 Part 1. Q1 (a) (b) (c) A

MT365 Examination 2007 Part 1. Q1 (a) (b) (c) A MT6 Examination Part Solutions Q (a) (b) (c) F F F E E E G G G G is both Eulerian and Hamiltonian EF is both an Eulerian trail and a Hamiltonian cycle. G is Hamiltonian but not Eulerian and EF is a Hamiltonian

More information

Lesson 22: Basic Graph Concepts

Lesson 22: Basic Graph Concepts Lesson 22: asic Graph oncepts msc 175 iscrete Mathematics 1. Introduction graph is a mathematical object that is used to model different relations between objects and processes: Linked list Flowchart of

More information

L10 Graphs. Alice E. Fischer. April Alice E. Fischer L10 Graphs... 1/37 April / 37

L10 Graphs. Alice E. Fischer. April Alice E. Fischer L10 Graphs... 1/37 April / 37 L10 Graphs lice. Fischer pril 2016 lice. Fischer L10 Graphs... 1/37 pril 2016 1 / 37 Outline 1 Graphs efinition Graph pplications Graph Representations 2 Graph Implementation 3 Graph lgorithms Sorting

More information

Abstract objective function graphs on the 3-cube a classification by realizability

Abstract objective function graphs on the 3-cube a classification by realizability Research ollection Report bstract objective function graphs on the 3-cube a classification by realizability uthor(s): ärtner, ernd; Kaibel, Volker Publication ate: 1998 Permanent Link: https://doi.org/10.3929/ethz-a-006652780

More information

A Schedulability-Preserving Transformation Scheme from Boolean- Controlled Dataflow Networks to Petri Nets

A Schedulability-Preserving Transformation Scheme from Boolean- Controlled Dataflow Networks to Petri Nets Schedulability-Preserving ransformation Scheme from oolean- ontrolled Dataflow Networks to Petri Nets ong Liu Edward. Lee University of alifornia at erkeley erkeley,, 94720, US {congliu,eal}@eecs. berkeley.edu

More information

CISC 320 Introduction to Algorithms Fall Lecture 15 Depth First Search

CISC 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 information

Graphs: Definitions and Representations (Chapter 9)

Graphs: Definitions and Representations (Chapter 9) oday s Outline Graphs: efinitions and Representations (hapter 9) dmin: HW #4 due hursday, Nov 10 at 11pm Memory hierarchy Graphs Representations SE 373 ata Structures and lgorithms 11/04/2011 1 11/04/2011

More information

CSE 332: Data Structures & Parallelism Lecture 19: Introduction to Graphs. Ruth Anderson Autumn 2018

CSE 332: Data Structures & Parallelism Lecture 19: Introduction to Graphs. Ruth Anderson Autumn 2018 SE 332: ata Structures & Parallelism Lecture 19: Introduction to Graphs Ruth nderson utumn 2018 Today Graphs Intro & efinitions 11/19/2018 2 Graphs graph is a formalism for representing relationships among

More information

Integrated Framework for Automating the Structural Design Iteration

Integrated Framework for Automating the Structural Design Iteration Integrated Framework for Automating the Structural Design Iteration P. Mujumdar a and J.U. Maheswari b a,b Department of Civil Engineering, Indian Institute of Technology Delhi, India E-mail: purvamujumdar@gmail.com

More information

Graphs: Definitions and Representations (Chapter 9)

Graphs: Definitions and Representations (Chapter 9) oday s Outline Graphs: efinitions and Representations (hapter 9) SE 373 ata Structures and lgorithms dmin: Homework #4 - due hurs, Nov 8 th at 11pm Midterm 2, ri Nov 16 Memory hierarchy Graphs Representations

More information

CHAPTER-2 A GRAPH BASED APPROACH TO FIND CANDIDATE KEYS IN A RELATIONAL DATABASE SCHEME **

CHAPTER-2 A GRAPH BASED APPROACH TO FIND CANDIDATE KEYS IN A RELATIONAL DATABASE SCHEME ** HPTR- GRPH S PPROH TO FIN NIT KYS IN RLTIONL TS SHM **. INTROUTION Graphs have many applications in the field of knowledge and data engineering. There are many graph based approaches used in database management

More information

Project and Production Management Prof. Arun Kanda Department of Mechanical Engineering Indian Institute of Technology, Delhi

Project and Production Management Prof. Arun Kanda Department of Mechanical Engineering Indian Institute of Technology, Delhi Project and Production Management Prof. Arun Kanda Department of Mechanical Engineering Indian Institute of Technology, Delhi Lecture - 8 Consistency and Redundancy in Project networks In today s lecture

More information

These definitions cover some basic terms and concepts of graph theory and how they have been implemented as C++ classes.

These definitions cover some basic terms and concepts of graph theory and how they have been implemented as C++ classes. hapter 5. raph Theory *raph Theory, *Mathematics raph theory is an area of mathematics which has been incorporated into IS to solve some specific problems in oolean operations and sweeping. It may be also

More information

A Geometric Approach to the Bisection Method

A Geometric Approach to the Bisection Method Geometric pproach to the isection Method laudio Gutierrez 1, Flavio Gutierrez 2, and Maria-ecilia Rivara 1 1 epartment of omputer Science, Universidad de hile lanco Encalada 2120, Santiago, hile {cgutierr,mcrivara}@dcc.uchile.cl

More information

Algebraic Graph Theory- Adjacency Matrix and Spectrum

Algebraic Graph Theory- Adjacency Matrix and Spectrum Algebraic Graph Theory- Adjacency Matrix and Spectrum Michael Levet December 24, 2013 Introduction This tutorial will introduce the adjacency matrix, as well as spectral graph theory. For those familiar

More information

Vertex Magic Total Labelings of Complete Graphs 1

Vertex Magic Total Labelings of Complete Graphs 1 Vertex Magic Total Labelings of Complete Graphs 1 Krishnappa. H. K. and Kishore Kothapalli and V. Ch. Venkaiah Centre for Security, Theory, and Algorithmic Research International Institute of Information

More information

What are graphs? (Take 1)

What are graphs? (Take 1) Lecture 22: Let s Get Graphic Graph lgorithms Today s genda: What is a graph? Some graphs that you already know efinitions and Properties Implementing Graphs Topological Sort overed in hapter 9 of the

More information

Understand graph terminology Implement graphs using

Understand graph terminology Implement graphs using raphs Understand graph terminology Implement graphs using djacency lists and djacency matrices Perform graph searches Depth first search Breadth first search Perform shortest-path algorithms Disjkstra

More information

3.3 Hardware Karnaugh Maps

3.3 Hardware Karnaugh Maps 2P P = P = 3.3 Hardware UIntroduction A Karnaugh map is a graphical method of Boolean logic expression reduction. A Boolean expression can be reduced to its simplest form through the 4 simple steps involved

More information

Vertex Magic Total Labelings of Complete Graphs

Vertex Magic Total Labelings of Complete Graphs AKCE J. Graphs. Combin., 6, No. 1 (2009), pp. 143-154 Vertex Magic Total Labelings of Complete Graphs H. K. Krishnappa, Kishore Kothapalli and V. Ch. Venkaiah Center for Security, Theory, and Algorithmic

More information

Hybrid Connection Simulation Using Dynamic Nodal Numbering Algorithm

Hybrid Connection Simulation Using Dynamic Nodal Numbering Algorithm merican Journal of pplied Sciences 7 (8): 1174-1181, 2010 ISSN 1546-9239 2010 Science Publications Hybrid onnection Simulation Using ynamic Nodal Numbering lgorithm 1 longkorn Lamom, 2 Thaksin Thepchatri

More information

An Application of Prim s Algorithm in Defining a SoS Operational Boundary

An Application of Prim s Algorithm in Defining a SoS Operational Boundary Paper #37 n pplication of Prim s lgorithm in efining a SoS Operational oundary lex orod harles V. Schaefer, Jr. School of ngineering Stevens nstitute of Technology astle Point on udson oboken, N.J. 07030,

More information

1. What is the sum of the measures of the angles in a triangle? Write the proof (Hint: it involves creating a parallel line.)

1. What is the sum of the measures of the angles in a triangle? Write the proof (Hint: it involves creating a parallel line.) riangle asics irst: Some basics you should already know. eometry 4.0 1. What is the sum of the measures of the angles in a triangle? Write the proof (Hint: it involves creating a parallel line.) 2. In

More information

Enhanced Dependency Structure Matrix

Enhanced Dependency Structure Matrix Ecole Nationale Supérieure des Ingénieurs des Etudes et Techniques d rmement (ENSIET) Final year project Enhanced Dependency Structure Matrix Institut National de Recherche en Informatique et utomatique

More information

Sample Solutions to Homework #2

Sample Solutions to Homework #2 National aiwan University andout #1 epartment of lectrical ngineering November, 01 lgorithms, Fall 01 : Zhi-en in and Yen-hun iu ample olutions to omework # 1. (15) (a) ee Figure 1. 3 1 3 1 3 1 3 1 1 1

More information

SAMPLE. MODULE 5 Undirected graphs

SAMPLE. MODULE 5 Undirected graphs H P T R MOUL Undirected graphs How do we represent a graph by a diagram and by a matrix representation? How do we define each of the following: graph subgraph vertex edge (node) loop isolated vertex bipartite

More information

Computational Geometry

Computational Geometry Orthogonal Range Searching omputational Geometry hapter 5 Range Searching Problem: Given a set of n points in R d, preprocess them such that reporting or counting the k points inside a d-dimensional axis-parallel

More information

Data Structures Lecture 14

Data Structures Lecture 14 Fall 2018 Fang Yu Software Security Lab. ept. Management Information Systems, National hengchi University ata Structures Lecture 14 Graphs efinition, Implementation and Traversal Graphs Formally speaking,

More information

CSE 373: Data Structures and Algorithms. Graphs. Autumn Shrirang (Shri) Mare

CSE 373: Data Structures and Algorithms. Graphs. Autumn Shrirang (Shri) Mare SE 373: Data Structures and lgorithms Graphs utumn 2018 Shrirang (Shri) Mare shri@cs.washington.edu Thanks to Kasey hampion, en Jones, dam lank, Michael Lee, Evan Mcarty, Robbie Weber, Whitaker rand, Zora

More information

Routing. Effect of Routing in Flow Control. Relevant Graph Terms. Effect of Routing Path on Flow Control. Effect of Routing Path on Flow Control

Routing. Effect of Routing in Flow Control. Relevant Graph Terms. Effect of Routing Path on Flow Control. Effect of Routing Path on Flow Control Routing Third Topic of the course Read chapter of the text Read chapter of the reference Main functions of routing system Selection of routes between the origin/source-destination pairs nsure that the

More information

TCOM 501: Networking Theory & Fundamentals. Lecture 11 April 16, 2003 Prof. Yannis A. Korilis

TCOM 501: Networking Theory & Fundamentals. Lecture 11 April 16, 2003 Prof. Yannis A. Korilis TOM 50: Networking Theory & undamentals Lecture pril 6, 2003 Prof. Yannis. Korilis 2 Topics Routing in ata Network Graph Representation of a Network Undirected Graphs Spanning Trees and Minimum Weight

More information

Dr. Amotz Bar-Noy s Compendium of Algorithms Problems. Problems, Hints, and Solutions

Dr. Amotz Bar-Noy s Compendium of Algorithms Problems. Problems, Hints, and Solutions Dr. Amotz Bar-Noy s Compendium of Algorithms Problems Problems, Hints, and Solutions Chapter 1 Searching and Sorting Problems 1 1.1 Array with One Missing 1.1.1 Problem Let A = A[1],..., A[n] be an array

More information

MT365 Examination 2017 Part 1 Solutions Part 1

MT365 Examination 2017 Part 1 Solutions Part 1 MT xamination 0 Part Solutions Part Q. G (a) Number of vertices in G =. Number of edges in G = (i) The graph G is simple no loops or multiple edges (ii) The graph G is not regular it has vertices of deg.,

More information

Sequence Alignment (chapter 6) p The biological problem p Global alignment p Local alignment p Multiple alignment

Sequence Alignment (chapter 6) p The biological problem p Global alignment p Local alignment p Multiple alignment Sequence lignment (chapter 6) p The biological problem p lobal alignment p Local alignment p Multiple alignment Local alignment: rationale p Otherwise dissimilar proteins may have local regions of similarity

More information

Lesson 4.2. Critical Paths

Lesson 4.2. Critical Paths Lesson. ritical Paths It is relatively easy to find the shortest time needed to complete a project if the project consists of only a few activities. ut as the tasks increase in number, the problem becomes

More information

CS.17.A.MachineI.Overview

CS.17.A.MachineI.Overview P R T I I : L G O R I T H M S, M H I N E S, a n d T H E O R Y P R T I I : L G O R I T H M S, M H I N E S, a n d T H E O R Y omputer Science 7. omputing Machine omputer Science 7. omputing Machine Overview

More information

Motion Planning. O Rourke, Chapter 8

Motion Planning. O Rourke, Chapter 8 O Rourke, Chapter 8 Outline Translating a polygon Moving a ladder Shortest Path (Point-to-Point) Goal: Given disjoint polygons in the plane, and given positions s and t, find the shortest path from s to

More information

Introduction to Graphs. common/notes/ppt/

Introduction to Graphs.   common/notes/ppt/ Introduction to Graphs http://people.cs.clemson.edu/~pargas/courses/cs212/ common/notes/ppt/ Introduction Graphs are a generalization of trees Nodes or verticies Edges or arcs Two kinds of graphs irected

More information

CS 561, Lecture 9. Jared Saia University of New Mexico

CS 561, Lecture 9. Jared Saia University of New Mexico CS 561, Lecture 9 Jared Saia University of New Mexico Today s Outline Minimum Spanning Trees Safe Edge Theorem Kruskal and Prim s algorithms Graph Representation 1 Graph Definition A graph is a pair of

More information

Data Struct. & Prob. Solving, M.C.Q. BANK, FOR UNIT 3, SECOND YEAR COMP. ENGG. SEM 1, 2012 PATTERN, U.O.P.

Data Struct. & Prob. Solving, M.C.Q. BANK, FOR UNIT 3, SECOND YEAR COMP. ENGG. SEM 1, 2012 PATTERN, U.O.P. UNIT distribution for +1+1 + 1 + + = 11 (Only 1 will be asked for marks, s will be asked for 1 & s asked for marks ) Syllabus for Graphs Programmers perspective of graphs, Graph operations, storage structure,

More information

Mathematics and Statistics, Part A: Graph Theory Problem Sheet 1, lectures 1-4

Mathematics and Statistics, Part A: Graph Theory Problem Sheet 1, lectures 1-4 1. Draw Mathematics and Statistics, Part A: Graph Theory Problem Sheet 1, lectures 1-4 (i) a simple graph. A simple graph has a non-empty vertex set and no duplicated edges. For example sketch G with V

More information

Course Introduction / Review of Fundamentals of Graph Theory

Course Introduction / Review of Fundamentals of Graph Theory Course Introduction / Review of Fundamentals of Graph Theory Hiroki Sayama sayama@binghamton.edu Rise of Network Science (From Barabasi 2010) 2 Network models Many discrete parts involved Classic mean-field

More information

DSM (Dependency/Design Structure Matrix)

DSM (Dependency/Design Structure Matrix) DSM (Dependency/Design Structure Matrix) L T JayPrakash Courtsey: DSMweb.org Organization of the Talk 1 Different DSM Types How to Read a DSM Building and Creating a DSM Hands-on Exercises Operations on

More information

Functional Block: Decoders

Functional Block: Decoders University of Wisconsin - Madison EE/omp Sci 352 Digital Systems Fundamentals harles R. Kime Section 2 Fall 2 hapter 3 ombinational Logic Design Part 2 Tom Kaminski & harles R. Kime harles Kime & Thomas

More information

An array is a collection of data that holds fixed number of values of same type. It is also known as a set. An array is a data type.

An array is a collection of data that holds fixed number of values of same type. It is also known as a set. An array is a data type. Data Structures Introduction An array is a collection of data that holds fixed number of values of same type. It is also known as a set. An array is a data type. Representation of a large number of homogeneous

More information

GRAPH THEORY. What are graphs? Why graphs? Graphs are usually used to represent different elements that are somehow related to each other.

GRAPH THEORY. What are graphs? Why graphs? Graphs are usually used to represent different elements that are somehow related to each other. GRPH THEORY Hadrian ng, Kyle See, March 2017 What are graphs? graph G is a pair G = (V, E) where V is a nonempty set of vertices and E is a set of edges e such that e = {a, b where a and b are vertices.

More information

Data Structures - Binary Trees and Operations on Binary Trees

Data Structures - Binary Trees and Operations on Binary Trees ata Structures - inary Trees and Operations on inary Trees MS 275 anko drovic (UI) MS 275 October 15, 2018 1 / 25 inary Trees binary tree is a finite set of elements. It can be empty partitioned into three

More information

BACKGROUND: A BRIEF INTRODUCTION TO GRAPH THEORY

BACKGROUND: A BRIEF INTRODUCTION TO GRAPH THEORY BACKGROUND: A BRIEF INTRODUCTION TO GRAPH THEORY General definitions; Representations; Graph Traversals; Topological sort; Graphs definitions & representations Graph theory is a fundamental tool in sparse

More information

Parallel Algorithms for (PRAM) Computers & Some Parallel Algorithms. Reference : Horowitz, Sahni and Rajasekaran, Computer Algorithms

Parallel Algorithms for (PRAM) Computers & Some Parallel Algorithms. Reference : Horowitz, Sahni and Rajasekaran, Computer Algorithms Parallel Algorithms for (PRAM) Computers & Some Parallel Algorithms Reference : Horowitz, Sahni and Rajasekaran, Computer Algorithms Part 2 1 3 Maximum Selection Problem : Given n numbers, x 1, x 2,, x

More information

Applications of BFS and DFS

Applications of BFS and DFS pplications of S and S S all // : PM Some pplications of S and S S o find the shortest path from a vertex s to a vertex v in an unweighted graph o find the length of such a path o construct a S tree/forest

More information

Distributed Method Selection and Dispatching of Contingent, Temporally Flexible Plans

Distributed Method Selection and Dispatching of Contingent, Temporally Flexible Plans istributed ethod Selection and ispatching of ontingent, Temporally lexible Plans Stephen. lock and rian. Williams omputer Science and rtificial ntelligence Laboratory, assachusetts nstitute of Technology,

More information

A Linear Programming Based Algorithm for Multiple Sequence Alignment by Using Markov Decision Process

A Linear Programming Based Algorithm for Multiple Sequence Alignment by Using Markov Decision Process inear rogramming Based lgorithm for ultiple equence lignment by Using arkov ecision rocess epartment of omputer cience University of aryland, ollege ark hirin ehraban dvisors: r. ern unt and r. hau-en

More information

Lecture Outine. Why Automatic Memory Management? Garbage Collection. Automatic Memory Management. Three Techniques. Lecture 18

Lecture Outine. Why Automatic Memory Management? Garbage Collection. Automatic Memory Management. Three Techniques. Lecture 18 Lecture Outine Why utomatic Memory Management? utomatic Memory Management Lecture 18 Garbage ollection Three Techniques Mark and Sweep Stop and opy Reference ounting Prof. Bodik S 164 Lecture 18 1 Prof.

More information

Objectives. In this session, you will learn to:

Objectives. In this session, you will learn to: TRS Objectives n this session, you will learn to: Store data in a tree istinguish types of inary tree Traverse a inary Tree norder PreOrder PostOrder onstruct a inary Tree onstruct an expression inary

More information

A Novel Approach of Prioritizing Use Case Scenarios

A Novel Approach of Prioritizing Use Case Scenarios 4th sia-pacific Software Engineering onference Novel pproach of Prioritizing Use ase Scenarios ebasish Kundu School of nformation Technology ndian nstitute of Technology, Kharagpur Kharagpur, West engal,

More information

LATIN SQUARES AND TRANSVERSAL DESIGNS

LATIN SQUARES AND TRANSVERSAL DESIGNS LATIN SQUARES AND TRANSVERSAL DESIGNS *Shirin Babaei Department of Mathematics, University of Zanjan, Zanjan, Iran *Author for Correspondence ABSTRACT We employ a new construction to show that if and if

More information

Algorithms. Definition: An algorithm is a set of rules followed to solve a problem. In general, an algorithm has the steps:

Algorithms. Definition: An algorithm is a set of rules followed to solve a problem. In general, an algorithm has the steps: Matchings 7.2 99 lgorithms efinition: n algorithm is a set of rules followed to solve a problem. In general, an algorithm has the steps: 1 Organize the input. 2 Repeatedly apply some steps until a termination

More information

Execution Tracing and Profiling

Execution Tracing and Profiling lass 8 Questions/comments iscussion of academic honesty, GT Honor ode fficient path profiling inal project presentations: ec, 3; 4:35-6:45 ssign (see Schedule for links) Problem Set 7 discuss Readings

More information

Abstract Data Types CHAPTER 12. Review Questions

Abstract Data Types CHAPTER 12. Review Questions PTR bstract ata Types Review Questions. n abstract data type is a data declaration packaged together with the operations that are meaningful for the data type with the implementation hidden from the user..

More information

1. (a) O(log n) algorithm for finding the logical AND of n bits with n processors

1. (a) O(log n) algorithm for finding the logical AND of n bits with n processors 1. (a) O(log n) algorithm for finding the logical AND of n bits with n processors on an EREW PRAM: See solution for the next problem. Omit the step where each processor sequentially computes the AND of

More information

Today s Outline CSE 221: Algorithms and Data Structures Graphs (with no Axes to Grind)

Today s Outline CSE 221: Algorithms and Data Structures Graphs (with no Axes to Grind) Today s Outline S : lgorithms and ata Structures raphs (with no xes to rind) Steve Wolfman 0W Topological Sort: etting to Know raphs with a Sort raph T and raph Representations raph Terminology (a lot

More information

Machine Learning Lecture 16

Machine Learning Lecture 16 ourse Outline Machine Learning Lecture 16 undamentals (2 weeks) ayes ecision Theory Probability ensity stimation Undirected raphical Models & Inference 28.06.2016 iscriminative pproaches (5 weeks) Linear

More information

Spanning Trees. CSE373: Data Structures & Algorithms Lecture 17: Minimum Spanning Trees. Motivation. Observations. Spanning tree via DFS

Spanning Trees. CSE373: Data Structures & Algorithms Lecture 17: Minimum Spanning Trees. Motivation. Observations. Spanning tree via DFS Spanning Trees S: ata Structures & lgorithms Lecture : Minimum Spanning Trees simple problem: iven a connected undirected graph =(V,), find a minimal subset of edges such that is still connected graph

More information

Splay tree, tree Marko Berezovský Radek Mařík PAL 2012

Splay tree, tree Marko Berezovský Radek Mařík PAL 2012 Splay tree, --4 tree Marko erezovský Radek Mařík PL 0 p < Hi!?/ x+y x--y To read [] Weiss M.., Data Structures and lgorithm nalysis in ++, rd Ed., ddison Wesley, 4.5, pp.49-58. [] Daniel D. Sleator and

More information

Programming Abstractions

Programming Abstractions Programming bstractions S 1 0 6 X ynthia ee Upcoming Topics raphs! 1. asics What are they? ow do we represent them? 2. Theorems What are some things we can prove about graphs? 3. readth-first search on

More information

CSE 417 Dynamic Programming (pt 4) Sub-problems on Trees

CSE 417 Dynamic Programming (pt 4) Sub-problems on Trees CSE 417 Dynamic Programming (pt 4) Sub-problems on Trees Reminders > HW4 is due today > HW5 will be posted shortly Dynamic Programming Review > Apply the steps... 1. Describe solution in terms of solution

More information

LECTURE NOTES OF ALGORITHMS: DESIGN TECHNIQUES AND ANALYSIS

LECTURE NOTES OF ALGORITHMS: DESIGN TECHNIQUES AND ANALYSIS Department of Computer Science University of Babylon LECTURE NOTES OF ALGORITHMS: DESIGN TECHNIQUES AND ANALYSIS By Faculty of Science for Women( SCIW), University of Babylon, Iraq Samaher@uobabylon.edu.iq

More information

CS473-Algorithms I. Lecture 13-A. Graphs. Cevdet Aykanat - Bilkent University Computer Engineering Department

CS473-Algorithms I. Lecture 13-A. Graphs. Cevdet Aykanat - Bilkent University Computer Engineering Department CS473-Algorithms I Lecture 3-A Graphs Graphs A directed graph (or digraph) G is a pair (V, E), where V is a finite set, and E is a binary relation on V The set V: Vertex set of G The set E: Edge set of

More information

Supplement to. Logic and Computer Design Fundamentals 4th Edition 1

Supplement to. Logic and Computer Design Fundamentals 4th Edition 1 Supplement to Logic and Computer esign Fundamentals 4th Edition MORE OPTIMIZTION Selected topics not covered in the fourth edition of Logic and Computer esign Fundamentals are provided here for optional

More information

3.1 Basic Definitions and Applications

3.1 Basic Definitions and Applications Graphs hapter hapter Graphs. Basic efinitions and Applications Graph. G = (V, ) n V = nodes. n = edges between pairs of nodes. n aptures pairwise relationship between objects: Undirected graph represents

More information

Analysis of Algorithms. Unit 4 - Analysis of well known Algorithms

Analysis of Algorithms. Unit 4 - Analysis of well known Algorithms Analysis of Algorithms Unit 4 - Analysis of well known Algorithms 1 Analysis of well known Algorithms Brute Force Algorithms Greedy Algorithms Divide and Conquer Algorithms Decrease and Conquer Algorithms

More information

Chapter 3 Part 1 Combinational Logic Design

Chapter 3 Part 1 Combinational Logic Design Universit of Wisconsin - Madison EE/omp Sci 352 igital Sstems undamentals Kewal K. Saluja and Yu Hen Hu Spring 22 hapter 3 Part ombinational Logic esign Originals b: harles R. Kime and Tom Kamisnski Modified

More information

Graph Theory(Due with the Final Exam)

Graph Theory(Due with the Final Exam) Graph Theory(ue with the Final Exam) Possible Walking Tour.. Is it possible to start someplace(either in a room or outside) and walk through every doorway once and only once? Explain. If it is possible,

More information

Subject: Computer Science

Subject: Computer Science Subject: Computer Science Topic: Data Types, Variables & Operators 1 Write a program to print HELLO WORLD on screen. 2 Write a program to display output using a single cout statement. 3 Write a program

More information

8th Grade. Slide 1 / 87. Slide 2 / 87. Slide 3 / 87. Equations with Roots and Radicals. Table of Contents

8th Grade. Slide 1 / 87. Slide 2 / 87. Slide 3 / 87. Equations with Roots and Radicals. Table of Contents Slide 1 / 87 Slide 2 / 87 8th Grade Equations with Roots and Radicals 2015-12-17 www.njctl.org Table of ontents Slide 3 / 87 Radical Expressions ontaining Variables Simplifying Non-Perfect Square Radicands

More information

(Refer Slide Time 04:53)

(Refer Slide Time 04:53) Programming and Data Structure Dr.P.P.Chakraborty Department of Computer Science and Engineering Indian Institute of Technology, Kharagpur Lecture 26 Algorithm Design -1 Having made a preliminary study

More information

CSE/EE 461 Lecture 7 Bridging LANs. Last Two Times. This Time -- Switching (a.k.a. Bridging)

CSE/EE 461 Lecture 7 Bridging LANs. Last Two Times. This Time -- Switching (a.k.a. Bridging) S/ 461 Lecture 7 ridging LNs Last Two Times Medium ccess ontrol (M) protocols Part of the Link Layer t the heart of Local rea Networks (LNs) ow do multiple parties share a wire or the air? Random access

More information

11/26/17. directed graphs. CS 220: Discrete Structures and their Applications. graphs zybooks chapter 10. undirected graphs

11/26/17. directed graphs. CS 220: Discrete Structures and their Applications. graphs zybooks chapter 10. undirected graphs directed graphs S 220: iscrete Structures and their pplications collection of vertices and directed edges graphs zybooks chapter 10 collection of vertices and edges undirected graphs What can this represent?

More information

Discovering Reference Models by Mining Process Variants Using a Heuristic Approach

Discovering Reference Models by Mining Process Variants Using a Heuristic Approach iscovering Reference Models by Mining Process Variants Using a euristic pproach hen Li 1, Manfred Reichert 2, and ndreas Wombacher 1 1 omputer Science epartment, University of Twente, The Netherlands lic@cs.utwente.nl;

More information

QUICK EXCEL TUTORIAL. The Very Basics

QUICK EXCEL TUTORIAL. The Very Basics QUICK EXCEL TUTORIAL The Very Basics You Are Here. Titles & Column Headers Merging Cells Text Alignment When we work on spread sheets we often need to have a title and/or header clearly visible. Merge

More information

Decision tables. Combinational Models. Necessary Characteristics of the Implementation. Decision tables. Deriving decision tables

Decision tables. Combinational Models. Necessary Characteristics of the Implementation. Decision tables. Deriving decision tables ombinational Models Generating test cases when the test model is a decision table Textbook Reading: hapter 6 ecision tables Ideal representation for a test model for the following reasons: Straightforward

More information

Dynamic Maintenance of Majority Information in Constant Time per Update

Dynamic Maintenance of Majority Information in Constant Time per Update RIS RS-95-45 Frandsen & Skyum: ynamic Maintenance of Majority Information RIS asic Research in omputer Science ynamic Maintenance of Majority Information in onstant Time per pdate Gudmund Skovbjerg Frandsen

More information