PPT-Dijkstra-Scholten

and ShavitFrancez termination algorithms Index Introduction Experimental Setup Result Analysis Conclusion Future Work Introduction DijkstraScholten algorithm detects the termination of a centralized basic computation. e antipathy networks over time Three competing hypotheses were formulated about the role of status antipathy relations result from either similarity in status competition hypothesis or dissimilarity in status when lower status peers reject higher sta What do we mean by “weakest”?lIf A = B but not (B = A), then B is “weaker” than A, and A is “stronger” than B.lThe weakest possible predicate is the one that is identic Navid. . adham. History. Dijkstra: 1959. Dantzig. . method: 1960 . “On the shortest route through a network” / management . science. Just an idea!. Berge and . Ghouila-Houri. : 1962. Incorrect stopping criterion. we say something and our conversational partner does not understand, forcing us to notice a gap in our competence. We then try again until we arrive at the correct version of the rule. The evidence Dwyane George. March 10, 2015. Outline. Definitions. Algorithm: Single-Source Shortest Path (SSSP. ). r. -Division (Clustering). Runtime Analysis. Proof . of . Correctness. Planar Graphs. Definition: a graph G that can be embedded in a plane, such that the edges only intersect at the endpoints. Ninth . Edition. by William Stallings. Chapter 12 – . Routing in. Switched Data Networks. Data and Computer Communications, Ninth Edition by William Stallings, (c) Pearson Education - Prentice Hall, 2011 . . Paths. Algorithms. and Networks 2015/2016. Hans L. . Bodlaender. Johan M. M. van Rooij. Shortest path problem(s). Undirected single-pair shortest path problem. Given a graph G=(V,E) and a length function . Richard . Anderson. Spring 2016. Announcements . . . 2. 3. Graphs. A formalism for representing relationships between objects. Graph. . G = (V,E). Set of . vertices. :. V. =. . {v. 1. ,v. 2. ,…,v. Algorithm. SSSP, non-. neg. s. c. b. d. 2. 6. 6. e. 2. 2. 1. 2. Edge weights . = w(. x,y. ). Final distances = d(. x,y. ) = . d. w. (. x,y. ). Dijkstra’s. Algorithm. SSSP, non-. neg. s. c. b. d. 2. Complete questions 1, 2, and 3 on your quiz. 1. Graph abstraction. 2. u. y. x. w. v. z. 2. 2. 1. 3. 1. 1. 2. 5. 3. 5. Graph: G = (N,E). N = set of routers = { u, v, w, x, y, z }. E = set of links ={ (. Minimum spanning . t. ree (MST). is a spanning tree whose weight is no larger than any other spanning tree. Prim . v.s. . . Kruskal. Prim:. At each time, add an edge connecting tree vertex and non-tree vertex. 1Chapter 4GreedySlides by Kevin WayneCopyright 2005 Pearson-Addison WesleyAll rights reserved44 Shortest Paths in a Graphshortest path from Princeton CS department to Einsteins house3Shortest Path P 1Chapter 4GreedySlides by Kevin WayneCopyright 2005 Pearson-Addison WesleyAll rights reserved44 Shortest Paths in a Graphshortest path from Princeton CS department to Einsteins house3Shortest Path P