CS Algorithms and Complexity
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1 CS Algorithms and Complexity Graph Theory, Midterm Review Sean Anderson 2/6/18 Portland State University
2 Table of contents 1. Graph Theory 2. Graph Problems 3. Uninformed Exhaustive Search 4. Informed Search (Djikstra s) 5. PotD 1
3 Graph Theory
4 Graphs V 1 V 2 V 3 V 4 V 1 V 2 V 3 V 4 V 5 2
5 Sources, Sinks, and Trees Source: in a directed graph, a vertex with only outgoing edges Sink: in a directed graph, a vertex with only incoming edges Tree: Acyclic Connected Graph Rooted Tree: A tree with one node designated as the root - has implicit direction Directed Tree: A rooted tree with explicit direction in its edges, either: Arborescence - there exists a path from the root to every node Anti-arborescence - there exists a path from every node to the root Note that a directed tree must correspond to an undirected one, so every path is unique 3
6 Graph Problems
7 Graph Search Graph Search Instance: A graph (V, E), a root v 0, and a set of goal nodes G V Solution: A path from v 0 to an element of G, if one exists Tree Search Instance: A tree (V, E) rooted at v 0, and a set of goal nodes G V Solution: A path from R to an element of G, if one exists 4
8 Shortest Path(s) Shortest Path Instance: A graph (V, E), a root v 0, and a goal node v g Solution: The shortest path from v 0 to v g, if one exists Single Source Shortest Paths (SSSP) Instance: A graph (V, E) and a root v 0 Solution: For each v i V, the shortest path from v 0 to v i, if one exists All Shortest Paths Instance: A graph (V, E) Solution: For each v i, v j V, the shortest path from v i to v j, if one exists 5
9 Topological Sort Topological Sort Instance: A directed acyclic graph (V, E) Solution: A total order over V such that if (v i, v j ) E then v i v j 6
10 Cycle Detection Shortest Path Instance: A graph (V, E) Solution: True if (V, E) contains a cycle, false otherwise 7
11 Problem Relationships Many of these problems can be transformed into each other In particular, search (especially optimal search) and shortest paths Topological sort and cycle detection are highly related on digraphs 8
12 Uninformed Exhaustive Search
13 Exhaustive Search When R&C and D&C don t work, sometimes we just need to run through all the options Runtime: size of structure (e.g., V + E ) Information: Uninformed Search WARNING: AI BELOW THIS POINT Heuristic Search Greedy (Best-First) Search 9
14 Infinite Graphs If we re being exhaustive, we hit all the vertices. What about infinite graphs? (b, d) b - branching factor d - depth of goal 10
15 Depth-First Search DFS(v 0 ): for v 1 such that (v 0, v 1 ) E: if v 1 G: return [(v 0, v 1 )] sol DFS(v 1 ) if sol []: return (v 0, v 1 ) ++ sol return [] 11
16 Depth-First Search (Fixed) DFS(v 0 ): mark v 0 visited for v 1 such that (v 0, v 1 ) E: if v 1 visited if v 1 G: return [(v 0, v 1 )] sol DFS(v 1 ) if sol []: return (v 0, v 1 ) ++ sol return [] 12
17 DFS Analysis Naïve Time Complexity: O( V + E ) Time: O(b d ) Space: O(bd) Incomplete on infinite graph Suboptimal 13
18 Breadth-First Search BFS(v 0 ): Q empty queue Q.push(v 0 ) while Q not empty: mark v i visited v i Q.pop() for v j such that (v i, v j ) E: if v j G: return path else if v j not visited: Q.push(v j ) 14
19 BFS Analysis Naïve Time Complexity: O( V + E ) Time: O(b d ) Space: O(b d ) Complete on infinite graph if goal reachable Optimal on unweighted graph 15
20 Informed Search (Djikstra s)
21 Djikstra s Algorithm SSSP: BFS works on unweighted graphs What about weighted? Idea: like BFS, but predicting next vertex by path weight 16
22 Djikstra s Pseudocode SSSP(v 0 ): for v i V: dist i, prev i nil Q empty priority queue (min-heap) Q.insert(v 0, 0) while unvisited vertices exist: v i, dist i Q.extract_min() mark v i visited for v j such that (v i, v j, w) E: if dist i + w < dist j : dist j dist i + w; prev j v i ) Q.insert(v j, dist j ) 17
23 Djikstra s Analysis Time Complexity: O( E T ins + V T ext ) Space Complexity: with optimization, O( V + E ) Complete on infinite graphs? Yes. Cycle-proof? Yes. Optimal? Yes. 18
24 PotD
25 PotD Give an algorithm for deciding whether a connected undirected graph (V, E) has a cycle. Describe its time and space complexity. 19
26 Bonus Problem: Minimum Spanning Tree In the spirit of T&C, we often want to transform a graph into a tree to take advantage of useful structure Next lecture: greedy algorithms for this and topological sort 20
27 References i
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