bellman.ford.sp: Bellman-Ford shortest paths using boost C++
In RBGL: An interface to the BOOST graph library

Description Usage Arguments Details Value Author(s) References See Also Examples

Algorithm for the single-source shortest paths problem for a graph with both positive and negative edge weights.

1	bellman.ford.sp(g,start=nodes(g)[1])

`g`	instance of class graph
`start`	character: node name for start of path

This function interfaces to the Boost graph library C++ routines for Bellman-Ford shortest paths. Choose the appropriate algorithm to calculate the shortest path carefully based on the properties of the given graph. See documentation on Bellman-Ford algorithm in Boost Graph Library for more details.

A list with elements:

`all edges minimized`	true if all edges are minimized, false otherwise.
`distance`	The vector of distances from `start` to each node of `g`; includes `Inf` when there is no path from `start`.
`penult`	A vector of indices (in `nodes(g)`) of predecessors corresponding to each node on the path from that node back to `start`

. For example, if the element one of this vector has value 10, that means that the predecessor of node 1 is node 10. The next predecessor is found by examining penult[10].

start

The start node that was supplied in the call to bellman.ford.sp.

Li Long <li.long@isb-sib.ch>

Boost Graph Library ( www.boost.org/libs/graph/doc/index.html )

The Boost Graph Library: User Guide and Reference Manual; by Jeremy G. Siek, Lie-Quan Lee, and Andrew Lumsdaine; (Addison-Wesley, Pearson Education Inc., 2002), xxiv+321pp. ISBN 0-201-72914-8

dag.sp, dijkstra.sp, johnson.all.pairs.sp, sp.between

con <- file(system.file("XML/conn2.gxl",package="RBGL"), open="r")
dd <- fromGXL(con)
close(con)
bellman.ford.sp(dd)
bellman.ford.sp(dd,nodes(dd)[2])

Loading required package: graph
Loading required package: BiocGenerics
Loading required package: parallel

Attaching package: 'BiocGenerics'

The following objects are masked from 'package:parallel':

    clusterApply, clusterApplyLB, clusterCall, clusterEvalQ,
    clusterExport, clusterMap, parApply, parCapply, parLapply,
    parLapplyLB, parRapply, parSapply, parSapplyLB

The following objects are masked from 'package:stats':

    IQR, mad, sd, var, xtabs

The following objects are masked from 'package:base':

    Filter, Find, Map, Position, Reduce, anyDuplicated, append,
    as.data.frame, basename, cbind, colMeans, colSums, colnames,
    dirname, do.call, duplicated, eval, evalq, get, grep, grepl,
    intersect, is.unsorted, lapply, lengths, mapply, match, mget,
    order, paste, pmax, pmax.int, pmin, pmin.int, rank, rbind,
    rowMeans, rowSums, rownames, sapply, setdiff, sort, table, tapply,
    union, unique, unsplit, which, which.max, which.min

$`all edges minimized`
[1] TRUE

$distance
A B C D E G H F 
0 1 1 1 2 2 2 3 

$penult
A B C D E G H F 
1 1 1 1 3 3 4 5 

$start
[1] "A"

$`all edges minimized`
[1] TRUE

$distance
  A   B   C   D   E   G   H   F 
Inf   0   1   1   2   2   2   3 

$penult
A B C D E G H F 
1 2 2 2 3 3 4 5 

$start
[1] "B"