# L04 - 6.889 Lecture 4 Single-Source Shortest Paths...

This preview shows pages 1–3. Sign up to view the full content.

6.889 — Lecture 4: Single-Source Shortest Paths Christian Sommer September 19 and 26, 2011 Single-Source Shortest Path (SSSP) Problem : given a graph G = ( V,E ) and a source vertex s V , compute shortest-path distance d G ( s,v ) for each v V (and encode shortest-path tree) Assumption (all of Lecture 4) non-negative edge lengths : E R + General Graphs fastest: Dijkstra’s algorithm O ( m + n log n ) Planar Graphs? can we use r –division? recall that r –division of G is decomposition into O ( n/r ) edge-disjoint pieces , each with 6 r vertices and O ( r ) boundary vertices. vertices with edges to at least two pieces assume (temporarily) we can compute r –division in O ( n ) . (Simple) Algorithm note that Dijkstra running time not “balanced,” nodes are bottleneck. idea: densify r –division with r := log n/ log log n (wlog assume s is on boundary) FOR EACH piece P , FOR EACH boundary node p ∂P compute SSSP (Dijkstra) in P , store in ∂P × ∂P distance matrix (complete graph on ∂P ) time required: O n log log n log n s log n log log n ! · O ± log n log log n log ± log n log log n ²² = O ³ n p log n log log n ´ let G 0 denote graph with each piece P replaced by complete graph on ∂P compute SSSP (Dijkstra) in G 0 time required: n 0 := | V ( G 0 ) | = O ( n p log log n/ log n ) and m 0 := | E ( G 0 ) | = O ( n ) , therefore O ( n 0 log n 0 + m 0 ) = O ³ n p log n log log n ´ FOR EACH piece P , FOR EACH boundary node p ∂P compute SSSP in P starting with d [ p ] := d G ( s,p ) (which computes, for every p 0 P , the distance d G ( s,p 0 ) and the last boundary node p ∂P on the shortest path from s to p 0 ) note that output not necessarily in sorted order let’s try recursion? issues: r instances of SSSP per piece, G 0 is non-planar graph 1

This preview has intentionally blurred sections. Sign up to view the full version.

View Full Document
1 Fast r –division need r –division in many algorithms. ﬁrst step of SSSP algorithm. want to improve upon O ( n log n ) for SSSP need fast r –division Lemma. For planar G , we can compute an r –division in time O ( n log r ) . improvement over O ( n log n ) Idea do ﬁrst O (log n ) recursion levels on smaller graph, then O (log r ) levels on original graph Def. A ρ –clustering of G is a decomposition into O ( n/ρ ) vertex-disjoint connected pieces , each with Θ( ρ ) vertices. Lemma. A ρ –clustering can be found in linear time (assuming G has bounded degree).
This is the end of the preview. Sign up to access the rest of the document.

{[ snackBarMessage ]}

### Page1 / 5

L04 - 6.889 Lecture 4 Single-Source Shortest Paths...

This preview shows document pages 1 - 3. Sign up to view the full document.

View Full Document
Ask a homework question - tutors are online