Download presentation

Presentation is loading. Please wait.

Published byIyana Laughton Modified over 3 years ago

1
CSE 390B: Graph Algorithms Based on CSE 373 slides by Jessica Miller, Ruth Anderson 1

2
2 A Graph: Course Prerequisites 321 143 142 322 326 341 370 378 401 421 Nodes = courses Directed edge = prerequisite 373 410 413 415 417 461

3
Depth-First Search (DFS) depth-first search (DFS): find path between two vertices by exploring each path as many steps as possible before backtracking – often implemented recursively with a stack for the path in progress – always finds a path, but not necessarily the shortest one – easy to reconstruct the path once you have found it (just unroll the calls) DFS path search order from A to others (assumes ABC edge order): – A – A B – A B D – A B F – A B F E – A C – A C G 3

4
DFS pseudocode dfs(v1, v2): path = new Stack(). dfs(v1, v2, path) dfs(v1, v2, path): path.push(v1). mark v1 as visited. if v1 = v2: path is found. for each unvisited neighbor v i of v1 with edge (v1 v i ): if dfs(v i, v2, path) finds a path, path is found. path.pop(). // path is not found. 4

5
Breadth-First Search (BFS) breadth-first search (DFS): find path between two nodes by taking one step down all paths and then immediately backtracking – often implemented with a queue of next vertices to visit – always finds the shortest path (fewest edges); optimal for unweighted graphs – harder to reconstruct the path once you have found it BFS path search order from A to others: – A – A B – A C – A E – A B D – A B F – A C G 5

6
BFS pseudocode bfs(v1, v2): Q = {v1}. mark v1 as visited. while Q not empty: v = Q.dequeue(). // remove from front if v is v2: path is found. for each unvisited neighbor v i of v1 with edge (v1 v i ): mark v i as visited. Q.enqueue(v i ). // add at end path is not found. 6

7
Implementation: Adjacency Matrix an n × n 2D array where M[a][b] = edges from v a to v b – Sometimes implemented as a Map > – Good for quickly asking, "is there an edge from vertex i to j?" – How do we figure out the degree of a vertex? 1 2 3 4 5 6 7 0 1 0 0 1 1 0 12345671234567 1 0 1 0 0 0 1 0 1 0 1 0 0 0 0 0 1 0 1 0 1 1 0 0 1 0 1 1 1 0 0 0 1 0 0 0 1 0 1 1 0 0 1 2 3 4 5 6 7 7

8
Implementation: Adjacency Lists n lists of neighbors; L[a] = all edges out from v a – Sometimes implemented as a Map > – Good for processing all-neighbors, sparse graphs (few edges) – How do we figure out the degree of a vertex? 1 2 3 4 5 6 7 12345671234567 256 137 24 357 1467 15 245

9
Dijkstra's algorithm Dijkstra's algorithm: finds shortest (min weight) path between a pair of vertices in a weighted directed graph with nonnegative edges – solves the "one vertex, shortest path" problem – basic algorithm concept: create a table of information about the currently known best way to reach each vertex (distance, previous vertex) and improve it until it reaches the best solution 9 A GF B ECD 4 1 2 103 64 22 85 1

10
Dijkstra pseudocode Dijkstra(v1, v2): for each vertex v: // Initialize state v's distance := infinity. v's previous := none. v1's distance := 0. Q := {all vertices}. while Q is not empty: v := remove Q's vertex with min distance. mark v as known. for each unknown neighbor n of v: dist := v's distance + edge (v, n)'s weight. if dist is smaller than n's distance: n's distance := dist. n's previous := v. reconstruct path from v2 back to v1, following previous pointers. 10 A GF B ECD 4 1 2 3 64 22 85 1 0 examine A: update B(2),D(1) examine D: update C(3),E(3),F(9),G(5) examine B,E: update none examine C: update F(8) examine G: update F(6) examine F: update none

11
Floyd-Warshall algorithm Floyd-Warshall algorithm: finds shortest (min weight) path between all pairs of vertices in a weighted directed graph – solves the "all pairs, shortest paths" problem(demo)demo – idea: repeatedly find best path using only vertices 1..k inclusive floydWarshall(): int path[n][n]. for each (i, j) from (0, 0) to (n, n): path[i][j] = edge_weight[i][j]. for k = 0 to n: for i = 0 to n: for j = 0 to n: path[i][j] = min(path[i][j], path[i][k] + path[k][j]). 11

12
Topological Sort Topological sort: finds a total ordering of vertices such that for any edge (v, w) in E, v precedes w in the ordering – e.g. find an ordering in which all UW CSE courses can be taken 12 A B C D E F FAED BC

13
Topological Sort pseudocode V = {all vertices}. E = {all edges}. L = []. while V is not empty: for each vertex v in V: if v has no incoming edges: V.remove(v). L.append(v). for each edge e (v n): E.remove(e). return L. examine A,D examine B examine C examine E examine F B C D E A F

Similar presentations

OK

Search Related Algorithms. Graph Code Adjacency List Representation:

Search Related Algorithms. Graph Code Adjacency List Representation:

© 2018 SlidePlayer.com Inc.

All rights reserved.

To ensure the functioning of the site, we use **cookies**. We share information about your activities on the site with our partners and Google partners: social networks and companies engaged in advertising and web analytics. For more information, see the Privacy Policy and Google Privacy & Terms.
Your consent to our cookies if you continue to use this website.

Ads by Google

Ppt on different festivals of india Ppt online compressor mechanic school Ppt on narendra modi Ppt on development of dentition Jit ppt on manufacturing engineering Ppt on types of parallelograms quadrilateral Ppt on air pollution control act Download ppt on turbo generator diagram A ppt on srinivasa ramanujan Ppt on automobile related topics in biology