'Undirected graph' presentation slideshows

NP-Completeness

NP-Completeness. Andreas Klappenecker [based on slides by Prof. Welch]. 1. Prelude: Informal Discussion. (Incidentally, we will never get very formal in this course). 2. Polynomial Time Algorithms.

1.66k views • 141 slides

Computing Fundamentals 2 Lecture 1 A Theory of Graphs

Computing Fundamentals 2 Lecture 1 A Theory of Graphs. Lecturer: Patrick Browne http://www.comp.dit.ie/pbrowne/ Lecture Room K408, Labs A305 & A306 Based on Chapter 19. A Logical approach to Discrete Math By David Gries and Fred B. Schneider. A Theory of Graphs.

1.58k views • 116 slides

CSE 332 Data Abstractions: Graphs and Graph Traversals

CSE 332 Data Abstractions: Graphs and Graph Traversals. Kate Deibel Summer 2012. Last Time. We introduced the idea of graphs and their associated terminology Key terms included: Directed versus Undirected Weighted versus Unweighted Cyclic or Acyclic Connected or Disconnected

1.3k views • 111 slides

Analysis of Algorithms Chapter - 06 Greedy Graph Algorithms

Analysis of Algorithms Chapter - 06 Greedy Graph Algorithms. This Chapter Contains the following Topics: Introduction Graph Categorization Graph Terminology Graph Representation Searching Graphs Depth-First Search Breadth-First Search Greedy Methods Fractional Knapsack Problem

747 views • 63 slides

Network Simplex Method

Network Simplex Method. Fatme Elmoukaddem Jignesh Patel Martin Porcelli. Outline. Definitions Economic Interpretation Algebraic Explanation Initialization Termination. Transshipment Problem.

1.44k views • 52 slides

Lecture 15 Bayesian Networks in Computer Vision

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Interval, circle graphs and circle graph recognition using split decomposition

Interval, circle graphs and circle graph recognition using split decomposition. Presented by Steven Correia Kent state university Nov-18-2011 Email: scorreia@kent.edu. Based on :[1][4]. Background and motivation. Can highly connectivity of protein in cells be found easily?

642 views • 44 slides

Graph Triangulation

453 views • 34 slides

Balanced Binary Search Trees

Balanced Binary Search Trees . height is O(log n) , where n is the number of elements in the tree AVL (Adelson-Velsky and Landis) trees red-black trees get, put, and remove take O(log n) time. Balanced Binary Search Trees . Indexed AVL trees Indexed red-black trees

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Decrease and Conquer

Decrease and Conquer. Reduce problem instance to smaller instance of the same problem Solve smaller instance Extend solution of smaller instance to obtain solution to original problem Also referred to as inductive or incremental approach. Examples of Decrease and Conquer.

826 views • 30 slides

Graph Theory and Representation

Graph Theory and Representation. Graph Algorithms. Graphs and Theorems about Graphs Graph ADT and implementation Graph Algorithms Shortest paths minimum spanning tree. What can graphs model?. Cost of wiring electronic components together. Shortest route between two cities.

423 views • 28 slides

CSCE350 Algorithms and Data Structure

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Lecture 11

Lecture 11. CSE 331 Sep 20, 2019. Mini Project group due week from Monday!. Please respect the TAs. HW 3 is out!. Solutions to HW 2. Handed out at the end of the lecture. Graded HW 1. Should be released by tonight. Formally define everything.

257 views • 25 slides

Graph Theory and Representation

Graph Theory and Representation. Graph Algorithms. Graphs and Theorems about Graphs Graph ADT and implementation Graph Algorithms Shortest paths minimum spanning tree. What can graphs model?. Cost of wiring electronic components together. Shortest route between two cities.

471 views • 22 slides

Graphs

Graphs. 1843. ORD. SFO. 802. 1743. 337. 1233. LAX. DFW. A graph is a pair ( V, E ) , where V is a set of nodes, called vertices E is a collection of pairs of vertices, called edges Vertices and edges are positions and store elements Example:

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Introduction Graphs

Vertices. Edges. Introduction Graphs . A graph G=(V,E) V =set of vertices (nodes) E =set of edges Examples : a map with cities connected by streets Cities are vertices Streets are edges Two basic types Undirected graphs (graphs) edge (u,v)=edge (v,u) Directed graphs (digraphs)

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RELATION on {N 1 ,..N n } is a subset of ? i =1..n N i

RELATION on {N 1 ,..N n } is a subset of ? i =1..n N i FUNCTION ,f:D ? R is a relation on {D,R} ?: {d,r 1 },{d,r 2 } ? f ? r 1 =r 2 . Thus we can safely use the notation, r=f(d). Relation, R, on (N,N) (or just a relation on N) is:

292 views • 10 slides

Rapid Spanning Tree protocol

Rapid Spanning Tree protocol. Outline. Background knowledge Bridge and switch Spanning Tree Algorithm Spanning Tree Protocol Performance Rapid Spanning Tree Protocol Conclusion Reference. Bridge and switch. Spanning Tree Algorithm. Problem definition

293 views • 10 slides

Kernighan-Lin Algorithm

Kernighan-Lin Algorithm. Perform single KL pass on the following circuit: KL needs undirected graph (clique-based weighting). First Swap. Second Swap. Third Swap. Fourth Swap. Last swap does not require gain computation. Summary. Cutsize reduced from 5 to 3

952 views • 6 slides