Sage Reference Manual: Numerical Optimization - Mirrors

Sage Reference Manual: Numerical Optimization, Release 6.1.1
5.3.2 Classes and methods
class sage.numerical.backends.glpk_graph_backend.GLPKGraphBackend
Bases: object
GLPK Backend for access to GLPK graph functions
The constructor can either be called without arguments (which results in an empty graph) or with arguments to
read graph data from a file.
INPUT:
•data – a filename or a Graph object.
•format – when data is a filename, specifies the format of the data read from a file. The format
parameter is a string and can take values as described in the table below.
Format parameters:
plain
Read data from a plain text file containing the following
information:
nv na
i[1] j[1]
i[2] j[2]
. . .
i[na] j[na]
where:
•nv is the number of vertices (nodes);
•na is the number of arcs;
•i[k], k = 1, . . . , na, is the index of tail vertex
of arc k;
•j[k], k = 1, . . . , na, is the index of head vertex
of arc k.
dimacs
mincost
maxflow
Read data from a plain ASCII text file in DIMACS
format. A discription of the DIMACS format can be
found at http://dimacs.rutgers.edu/Challenges/.
Reads the mincost flow problem data from a text file
in DIMACS format
Reads the maximum flow problem data from a text
file in DIMACS format
Note: When data is a Graph, the following restrictions are applied.
•vertices – the value of the demand of each vertex (see set_vertex_demand()) is obtained from the
numerical value associated with the key “rhs” if it is a dictionary.
•edges – The edge values used in the algorithms are read from the edges labels (and left undefined if the
edge labels are equal to None). To be defined, the labels must be dict objects with keys “low”, “cap”
and “cost”. See get_edge() for details.
EXAMPLES:
The following example creates an empty graph:
sage: from sage.numerical.backends.glpk_graph_backend import GLPKGraphBackend
sage: gbe = GLPKGraphBackend()
5.3. GLPK Backend for access to GLPK graph functions 81