CS 253: Algorithms
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CS 253: Algorithms Chapter 24 Shortest Paths Credit: Dr. George Bebis
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CS 253: Algorithms. Chapter 24 Shortest Paths. Credit : Dr. George Bebis. Shortest Path Problems. How can we find the shortest route between two points on a road map? Model the problem as a graph problem: Road map is a weighted graph: vertices = cities - PowerPoint PPT Presentation
Transcript of CS 253: Algorithms
CS 253: AlgorithmsChapter 24
Shortest Paths
Credit: Dr. George Bebis
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Shortest Path Problems
How can we find the shortest route between two points on a road map?
Model the problem as a graph problem:◦ Road map is a weighted graph:
vertices = cities
edges = road segments between cities
edge weights = road distances
◦ Goal: find a shortest path between two vertices (cities)
Shortest Path Problem
Input:
◦ Directed graph G = (V, E)
◦ Weight function w : E → R
Weight of path p = v0, v1, . . . , vk
Shortest-path weight from u to v:
δ(u, v) = min w(p) : u v if there exists a path from u to v
∞ otherwise
Note: there might be multiple shortest paths from u to v
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Negative-Weight Edges
Negative-weight edges may form negative-weight cycles If such cycles are reachable from the source,
then δ(s, v) is not properly defined!◦ Keep going around the cycle, and get
w(s, v) = - for all v on the cycle
Therefore, negative-weight edges will not be considered here
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CyclesCan shortest paths contain cycles?
No!
Negative-weight cycles◦ Shortest path is not well defined (we will not consider
this case)
If there is a positive-weight cycle, we can get a shorter path by removing the cycle.
Zero-weight cycles?◦ No reason to use them◦ Can remove them and obtain a path with the same weight
Shortest-Paths NotationFor each vertex v V: δ(s, v): shortest-path weight
d[v]: shortest-path weight estimate◦ Initially, d[v]=∞◦ d[v]δ(s,v) as algorithm progresses
[v] = predecessor of v on a shortest path from s◦ If no predecessor, [v] = NIL◦ induces a tree—shortest-path tree
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Initialization
Alg.: INITIALIZE-SINGLE-SOURCE(V, s)1. for each v V2. do d[v] ← 3. [v] ← NIL4. d[s] ← 0
All the shortest-paths algorithms start with INITIALIZE-SINGLE-SOURCE
Relaxation StepRelaxing an edge (u, v) = testing whether we can
improve the shortest path to v found so far by going through u
If d[v] > d[u] + w(u, v) we can improve the shortest path to v d[v]=d[u]+w(u,v) [v] ← u
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RELAX(u, v, w)
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RELAX(u, v, w)
After relaxation: d[v] d[u] + w(u, v)s s
no change
Dijkstra’s Algorithm Single-source shortest path problem:
◦ No negative-weight edges: w(u, v) > 0, (u, v) E Vertices in (V – S) reside in a min-priority queue
◦ Keys in Q are estimates of shortest-path weights d[u] Repeatedly select a vertex u (V – S), with the minimum
shortest-path estimate d[u] Relax all edges leaving u
STEPS1) Extract a vertex u from Q (i.e., u has the highest priority)2) Insert u to S3) Relax all edges leaving u4) Update Q
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Dijkstra (G, w, s)
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S=<> Q=<s,t,x,z,y> S=<s> Q=<y,t,x,z>
Example (cont.)
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S=<s,y> Q=<z,t,x> S=<s,y,z> Q=<t,x>
Example (cont.)
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S=<s,y,z,t> Q=<x> S=<s,y,z,t,x> Q=<>
Dijkstra (G, w, s)
1. INITIALIZE-SINGLE-SOURCE(V, s)2. S ← s3. Q ← V[G]4. while Q 5. do u ← EXTRACT-MIN(Q)6. S ← S {u} 7. for each vertex v Adj[u]8. do RELAX(u, v, w)9. Update Q (DECREASE_KEY)
(V)
(V) build min-heapExecuted (V) times
(lgV)
(E) times (max)
(lgV)
(VlgV)
(ElgV)
Running time: (VlgV + ElgV) = (ElgV)
Dijkstra’s SSSP Algorithm (adjacency matrix)
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L [.] = 1 0 5 1 S
new L[i] = Min{ L[i], L[k] + W[k, i] }where k is the newly-selected intermediate nodeand W[.] is the distance between k and i
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L [.] = 1 0 5 1 a b c d e
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SSSP cont.
new L[i] = Min{ L[i], L[k] + W[k, i] }where k is the newly-selected intermediate nodeand W[.] is the distance between k and i
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new L[i] = Min{ L[i], L[k] + W[k, i] }where k is the newly-selected intermediate nodeand W[.] is the distance between k and i
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L [.] = 1 0 3 1 5 3 a b c d e
f new L [.] = 1 0 3 1 4
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f new L [.] = 1 0 3 1 4
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Running time: (V2) (array representation)
(ElgV) (Min-Heap+Adjacency List)Which one is better?
All-Pairs Shortest PathsGiven:Directed graph G = (V, E)Weight function w : E → R
Compute: The shortest paths between all pairs of vertices in a
graphResult: an n × n matrix of shortest-path distances δ(u, v)
We can run Dijkstra’s algorithm once from each vertex:◦ O(VElgV) with binary heap and adjacency-list representation◦ if the graph is dense O(V3lgV)◦ We can achieve O(V3) by using an adjacency-matrix.
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Problem 1
We are given a directed graph G=(V, E) on which each edge (u,v) has an associated value r(u,v), which is a real number in the range 0 ≤ r(u,v) ≤ 1 that represents the reliability of a communication channel from vertex u to vertex v.
We interpret r(u,v) as the probability that the channel from u to v will not fail, and we assume that these probabilities are independent.
Design an efficient algorithm to find the most reliable path between two given vertices.
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Problem 1 (cont.)
◦r(u,v) = Pr(channel from u to v will not fail)◦Assuming that the probabilities are independent, the
reliability of a path p=<v1,v2,…,vk> is: r(v1,v2) r(v2,v3) … r(vk-1,vk)
Solution 1: modify Dijkstra’s algorithm
◦ Perform relaxation as follows: if d[v] < d[u] w(u,v) then
d[v] = d[u] w(u,v)
◦ Use “EXTRACT_MAX” instead of “EXTRACT_MIN”
Problem 1 (cont.)Solution 2: use Dijkstra’s algorithm without any
modifications!
◦ We want to find the channel with the highest reliability, i.e.,
◦ But Dijkstra’s algorithm computes
◦ Take the log
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max ( , )pu v p
r u v
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min ( , )pu v p
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lg(max ( , )) max lg( ( , ))p pu v pu v p
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Problem 1 (cont.)
Turn this into a minimization problem by taking the negative:
Run Dijkstra’s algorithm using
( , ) ( , )
min lg( ( , )) min lg( ( , ))p pu v p u v p
r u v r u v
( , ) lg( ( , ))w u v r u v
