struct::graph::op(n) Tcl Data Structures struct::graph::op(n) ______________________________________________________________________________
NAME
struct::graph::op - Operation for (un)directed graph objects
SYNOPSIS
package require Tcl 8.4
package require struct::graph::op ?0.11.3?
struct::graph:op::toAdjacencyMatrix g
struct::graph:op::toAdjacencyList G ?options...?
struct::graph:op::kruskal g
struct::graph:op::prim g
struct::graph:op::isBipartite? g ?bipartvar?
struct::graph:op::tarjan g
struct::graph:op::connectedComponents g
struct::graph:op::connectedComponentOf g n
struct::graph:op::isConnected? g
struct::graph:op::isCutVertex? g n
struct::graph:op::isBridge? g a
struct::graph:op::isEulerian? g ?tourvar?
struct::graph:op::isSemiEulerian? g ?pathvar?
struct::graph:op::dijkstra g start ?options...?
struct::graph:op::distance g origin destination ?options...?
struct::graph:op::eccentricity g n ?options...?
struct::graph:op::radius g ?options...?
struct::graph:op::diameter g ?options...?
struct::graph::op::BellmanFord G startnode
struct::graph::op::Johnsons G ?options...?
struct::graph::op::FloydWarshall G
struct::graph::op::MetricTravellingSalesman G
struct::graph::op::Christofides G
struct::graph::op::GreedyMaxMatching G
struct::graph::op::MaxCut G U V
struct::graph::op::UnweightedKCenter G k
struct::graph::op::WeightedKCenter G nodeWeights W
struct::graph::op::GreedyMaxIndependentSet G
struct::graph::op::GreedyWeightedMaxIndependentSet G nodeWeights
struct::graph::op::VerticesCover G
struct::graph::op::EdmondsKarp G s t
struct::graph::op::BusackerGowen G desiredFlow s t
struct::graph::op::ShortestsPathsByBFS G s outputFormat
struct::graph::op::BFS G s ?outputFormat...?
struct::graph::op::MinimumDiameterSpanningTree G
struct::graph::op::MinimumDegreeSpanningTree G
struct::graph::op::MaximumFlowByDinic G s t blockingFlowAlg
struct::graph::op::BlockingFlowByDinic G s t
struct::graph::op::BlockingFlowByMKM G s t
struct::graph::op::createResidualGraph G f
struct::graph::op::createAugmentingNetwork G f path
struct::graph::op::createLevelGraph Gf s
struct::graph::op::TSPLocalSearching G C
struct::graph::op::TSPLocalSearching3Approx G C
struct::graph::op::createSquaredGraph G
struct::graph::op::createCompleteGraph G originalEdges
_________________________________________________________________
DESCRIPTION
The package described by this document, struct::graph::op, is a compan-
ion to the package struct::graph. It provides a series of common opera-
tions and algorithms applicable to (un)directed graphs.
Despite being a companion the package is not directly dependent on
struct::graph, only on the API defined by that package. I.e. the opera-
tions of this package can be applied to any and all graph objects which
provide the same API as the objects created through struct::graph.
OPERATIONS
struct::graph:op::toAdjacencyMatrix g
This command takes the graph g and returns a nested list con-
taining the adjacency matrix of g.
The elements of the outer list are the rows of the matrix, the
inner elements are the column values in each row. The matrix has
"n+1" rows and columns, with the first row and column (index 0)
containing the name of the node the row/column is for. All other
elements are boolean values, True if there is an arc between the
2 nodes of the respective row and column, and False otherwise.
Note that the matrix is symmetric. It does not represent the
directionality of arcs, only their presence between nodes. It is
also unable to represent parallel arcs in g.
struct::graph:op::toAdjacencyList G ?options...?
Procedure creates for input graph G, it's representation as
Adjacency List. It handles both directed and undirected graphs
(default is undirected). It returns dictionary that for each
node (key) returns list of nodes adjacent to it. When consider-
ing weighted version, for each adjacent node there is also
weight of the edge included.
Arguments:
Graph object G (input)
A graph to convert into an Adjacency List.
Options:
-directed
By default G is operated as if it were an Undi-
rected graph. Using this option tells the command
to handle G as the directed graph it is.
-weights
By default any weight information the graph G may
have is ignored. Using this option tells the com-
mand to put weight information into the result.
In that case it is expected that all arcs have a
proper weight, and an error is thrown if that is
not the case.
struct::graph:op::kruskal g
This command takes the graph g and returns a list containing the
names of the arcs in g which span up a minimum weight spanning
tree (MST), or, in the case of an un-connected graph, a minimum
weight spanning forest (except for the 1-vertex components).
Kruskal's algorithm is used to compute the tree or forest. This
algorithm has a time complexity of O(E*log E) or O(E* log V),
where V is the number of vertices and E is the number of edges
in graph g.
The command will throw an error if one or more arcs in g have no
weight associated with them.
A note regarding the result, the command refrains from explic-
itly listing the nodes of the MST as this information is implic-
itly provided in the arcs already.
struct::graph:op::prim g
This command takes the graph g and returns a list containing the
names of the arcs in g which span up a minimum weight spanning
tree (MST), or, in the case of an un-connected graph, a minimum
weight spanning forest (except for the 1-vertex components).
Prim's algorithm is used to compute the tree or forest. This
algorithm has a time complexity between O(E+V*log V) and O(V*V),
depending on the implementation (Fibonacci heap + Adjacency list
versus Adjacency Matrix). As usual V is the number of vertices
and E the number of edges in graph g.
The command will throw an error if one or more arcs in g have no
weight associated with them.
A note regarding the result, the command refrains from explic-
itly listing the nodes of the MST as this information is implic-
itly provided in the arcs already.
struct::graph:op::isBipartite? g ?bipartvar?
This command takes the graph g and returns a boolean value indi-
cating whether it is bipartite (true) or not (false). If the
variable bipartvar is specified the two partitions of the graph
are there as a list, if, and only if the graph is bipartit. If
it is not the variable, if specified, is not touched.
struct::graph:op::tarjan g
This command computes the set of strongly connected components
(SCCs) of the graph g. The result of the command is a list of
sets, each of which contains the nodes for one of the SCCs of g.
The union of all SCCs covers the whole graph, and no two SCCs
intersect with each other.
The graph g is acyclic if all SCCs in the result contain only a
single node. The graph g is strongly connected if the result
contains only a single SCC containing all nodes of g.
struct::graph:op::connectedComponents g
This command computes the set of connected components (CCs) of
the graph g. The result of the command is a list of sets, each
of which contains the nodes for one of the CCs of g. The union
of all CCs covers the whole graph, and no two CCs intersect with
each other.
The graph g is connected if the result contains only a single
SCC containing all nodes of g.
struct::graph:op::connectedComponentOf g n
This command computes the connected component (CC) of the graph
g containing the node n. The result of the command is a sets
which contains the nodes for the CC of n in g.
The command will throw an error if n is not a node of the graph
g.
struct::graph:op::isConnected? g
This is a convenience command determining whether the graph g is
connected or not. The result is a boolean value, true if the
graph is connected, and false otherwise.
struct::graph:op::isCutVertex? g n
This command determines whether the node n in the graph g is a
cut vertex (aka articulation point). The result is a boolean
value, true if the node is a cut vertex, and false otherwise.
The command will throw an error if n is not a node of the graph
g.
struct::graph:op::isBridge? g a
This command determines whether the arc a in the graph g is a
bridge (aka cut edge, or isthmus). The result is a boolean
value, true if the arc is a bridge, and false otherwise.
The command will throw an error if a is not an arc of the graph
g.
struct::graph:op::isEulerian? g ?tourvar?
This command determines whether the graph g is eulerian or not.
The result is a boolean value, true if the graph is eulerian,
and false otherwise.
If the graph is eulerian and tourvar is specified then an euler
tour is computed as well and stored in the named variable. The
tour is represented by the list of arcs traversed, in the order
of traversal.
struct::graph:op::isSemiEulerian? g ?pathvar?
This command determines whether the graph g is semi-eulerian or
not. The result is a boolean value, true if the graph is semi-
eulerian, and false otherwise.
If the graph is semi-eulerian and pathvar is specified then an
euler path is computed as well and stored in the named variable.
The path is represented by the list of arcs traversed, in the
order of traversal.
struct::graph:op::dijkstra g start ?options...?
This command determines distances in the weighted g from the
node start to all other nodes in the graph. The options specify
how to traverse graphs, and the format of the result.
Two options are recognized
-arcmode mode
The accepted mode values are directed and undirected.
For directed traversal all arcs are traversed from source
to target. For undirected traversal all arcs are tra-
versed in the opposite direction as well. Undirected tra-
versal is the default.
-outputformat format
The accepted format values are distances and tree. In
both cases the result is a dictionary keyed by the names
of all nodes in the graph. For distances the value is the
distance of the node to start, whereas for tree the value
is the path from the node to start, excluding the node
itself, but including start. Tree format is the default.
struct::graph:op::distance g origin destination ?options...?
This command determines the (un)directed distance between the
two nodes origin and destination in the graph g. It accepts the
option -arcmode of struct::graph:op::dijkstra.
struct::graph:op::eccentricity g n ?options...?
This command determines the (un)directed eccentricity of the
node n in the graph g. It accepts the option -arcmode of
struct::graph:op::dijkstra.
The (un)directed eccentricity of a node is the maximal
(un)directed distance between the node and any other node in the
graph.
struct::graph:op::radius g ?options...?
This command determines the (un)directed radius of the graph g.
It accepts the option -arcmode of struct::graph:op::dijkstra.
The (un)directed radius of a graph is the minimal (un)directed
eccentricity of all nodes in the graph.
struct::graph:op::diameter g ?options...?
This command determines the (un)directed diameter of the graph
g. It accepts the option -arcmode of struct::graph:op::dijkstra.
The (un)directed diameter of a graph is the maximal (un)directed
eccentricity of all nodes in the graph.
struct::graph::op::BellmanFord G startnode
Searching for shortests paths between chosen node and all other
nodes in graph G. Based on relaxation method. In comparison to
struct::graph::op::dijkstra it doesn't need assumption that all
weights on edges in input graph G have to be positive.
That generality sets the complexity of algorithm to - O(V*E),
where V is the number of vertices and E is number of edges in
graph G.
Arguments:
Graph object G (input)
Directed, connected and edge weighted graph G,
without any negative cycles ( presence of cycles
with the negative sum of weight means that there
is no shortest path, since the total weight
becomes lower each time the cycle is traversed ).
Negative weights on edges are allowed.
Node startnode (input)
The node for which we find all shortest paths to
each other node in graph G.
Result:
Dictionary containing for each node (key) distances to
each other node in graph G.
Note: If algorithm finds a negative cycle, it will return error mes-
sage.
struct::graph::op::Johnsons G ?options...?
Searching for shortest paths between all pairs of vertices in
graph. For sparse graphs asymptotically quicker than
struct::graph::op::FloydWarshall algorithm. Johnson's algorithm
uses struct::graph::op::BellmanFord and struct::graph::op::dijk-
stra as subprocedures.
Time complexity: O(n**2*log(n) +n*m), where n is the number of
nodes and m is the number of edges in graph G.
Arguments:
Graph object G (input)
Directed graph G, weighted on edges and not con-
taining any cycles with negative sum of weights (
the presence of such cycles means there is no
shortest path, since the total weight becomes
lower each time the cycle is traversed ). Negative
weights on edges are allowed.
Options:
-filter
Returns only existing distances, cuts all Inf val-
ues for non-existing connections between pairs of
nodes.
Result:
Dictionary containing distances between all pairs of ver-
tices.
struct::graph::op::FloydWarshall G
Searching for shortest paths between all pairs of edges in
weighted graphs.
Time complexity: O(V^3) - where V is number of vertices.
Memory complexity: O(V^2).
Arguments:
Graph object G (input)
Directed and weighted graph G.
Result:
Dictionary containing shortest distances to each node
from each node.
Note: Algorithm finds solutions dynamically. It compares all possible
paths through the graph between each pair of vertices. Graph shouldn't
possess any cycle with negative sum of weights (the presence of such
cycles means there is no shortest path, since the total weight becomes
lower each time the cycle is traversed).
On the other hand algorithm can be used to find those cycles - if any
shortest distance found by algorithm for any nodes v and u (when v is
the same node as u) is negative, that node surely belong to at least
one negative cycle.
struct::graph::op::MetricTravellingSalesman G
Algorithm for solving a metric variation of Travelling salesman
problem. TSP problem is NP-Complete, so there is no efficient
algorithm to solve it. Greedy methods are getting extremely
slow, with the increase in the set of nodes.
Arguments:
Graph object G (input)
Undirected, weighted graph G.
Result:
Approximated solution of minimum Hamilton Cycle - closed
path visiting all nodes, each exactly one time.
Note: It's 2-approximation algorithm.
struct::graph::op::Christofides G
Another algorithm for solving metric TSP problem. Christofides
implementation uses Max Matching for reaching better approxima-
tion factor.
Arguments:
Graph Object G (input)
Undirected, weighted graph G.
Result:
Approximated solution of minimum Hamilton Cycle - closed
path visiting all nodes, each exactly one time.
Note: It's is a 3/2 approximation algorithm.
struct::graph::op::GreedyMaxMatching G
Greedy Max Matching procedure, which finds maximal matching (not
maximum) for given graph G. It adds edges to solution, beginning
from edges with the lowest cost.
Arguments:
Graph Object G (input)
Undirected graph G.
Result:
Set of edges - the max matching for graph G.
struct::graph::op::MaxCut G U V
Algorithm solving a Maximum Cut Problem.
Arguments:
Graph Object G (input)
The graph to cut.
List U (output)
Variable storing first set of nodes (cut) given by
solution.
List V (output)
Variable storing second set of nodes (cut) given
by solution.
Result:
Algorithm returns number of edges between found two sets
of nodes.
Note: MaxCut is a 2-approximation algorithm.
struct::graph::op::UnweightedKCenter G k
Approximation algorithm that solves a k-center problem.
Arguments:
Graph Object G (input)
Undirected complete graph G, which satisfies tri-
angle inequality.
Integer k (input)
Positive integer that sets the number of nodes
that will be included in k-center.
Result:
Set of nodes - k center for graph G.
Note: UnweightedKCenter is a 2-approximation algorithm.
struct::graph::op::WeightedKCenter G nodeWeights W
Approximation algorithm that solves a weighted version of k-cen-
ter problem.
Arguments:
Graph Object G (input)
Undirected complete graph G, which satisfies tri-
angle inequality.
Integer W (input)
Positive integer that sets the maximum possible
weight of k-center found by algorithm.
List nodeWeights (input)
List of nodes and its weights in graph G.
Result:
Set of nodes, which is solution found by algorithm.
Note:WeightedKCenter is a 3-approximation algorithm.
struct::graph::op::GreedyMaxIndependentSet G
A maximal independent set is an independent set such that adding
any other node to the set forces the set to contain an edge.
Algorithm for input graph G returns set of nodes (list), which
are contained in Max Independent Set found by algorithm.
struct::graph::op::GreedyWeightedMaxIndependentSet G nodeWeights
Weighted variation of Maximal Independent Set. It takes as an
input argument not only graph G but also set of weights for all
vertices in graph G.
Note: Read also Maximal Independent Set description for more
info.
struct::graph::op::VerticesCover G
Vertices cover is a set of vertices such that each edge of the
graph is incident to at least one vertex of the set. This
2-approximation algorithm searches for minimum vertices cover,
which is a classical optimization problem in computer science
and is a typical example of an NP-hard optimization problem that
has an approximation algorithm. For input graph G algorithm
returns the set of edges (list), which is Vertex Cover found by
algorithm.
struct::graph::op::EdmondsKarp G s t
Improved Ford-Fulkerson's algorithm, computing the maximum flow
in given flow network G.
Arguments:
Graph Object G (input)
Weighted and directed graph. Each edge should have
set integer attribute considered as maximum
throughputs that can be carried by that link
(edge).
Node s (input)
The node that is a source for graph G.
Node t (input)
The node that is a sink for graph G.
Result:
Procedure returns the dictionary containing throughputs
for all edges. For each key ( the edge between nodes u
and v in the form of list u v ) there is a value that is
a throughput for that key. Edges where throughput values
are equal to 0 are not returned ( it is like there was no
link in the flow network between nodes connected by such
edge).
The general idea of algorithm is finding the shortest augumenting paths
in graph G, as long as they exist, and for each path updating the
edge's weights along that path, with maximum possible throughput. The
final (maximum) flow is found when there is no other augumenting path
from source to sink.
Note: Algorithm complexity : O(V*E), where V is the number of nodes and
E is the number of edges in graph G.
struct::graph::op::BusackerGowen G desiredFlow s t
Algorithm finds solution for a minimum cost flow problem. So,
the goal is to find a flow, whose max value can be desiredFlow,
from source node s to sink node t in given flow network G. That
network except throughputs at edges has also defined a non-nega-
tive cost on each edge - cost of using that edge when directing
flow with that edge ( it can illustrate e.g. fuel usage, time or
any other measure dependent on usages ).
Arguments:
Graph Object G (input)
Flow network (directed graph), each edge in graph
should have two integer attributes: cost and
throughput.
Integer desiredFlow (input)
Max value of the flow for that network.
Node s (input)
The source node for graph G.
Node t (input)
The sink node for graph G.
Result:
Dictionary containing values of used throughputs for each
edge ( key ). found by algorithm.
Note: Algorithm complexity : O(V**2*desiredFlow), where V is the number
of nodes in graph G.
struct::graph::op::ShortestsPathsByBFS G s outputFormat
Shortest pathfinding algorithm using BFS method. In comparison
to struct::graph::op::dijkstra it can work with negative weights
on edges. Of course negative cycles are not allowed. Algorithm
is better than dijkstra for sparse graphs, but also there exist
some pathological cases (those cases generally don't appear in
practise) that make time complexity increase exponentially with
the growth of the number of nodes.
Arguments:
Graph Object G (input)
Input graph.
Node s (input)
Source node for which all distances to each other
node in graph G are computed.
Options and result:
distances
When selected outputFormat is distances - proce-
dure returns dictionary containing distances
between source node s and each other node in graph
G.
paths When selected outputFormat is paths - procedure
returns dictionary containing for each node v, a
list of nodes, which is a path between source node
s and node v.
struct::graph::op::BFS G s ?outputFormat...?
Breadth-First Search - algorithm creates the BFS Tree. Memory
and time complexity: O(V + E), where V is the number of nodes
and E is number of edges.
Arguments:
Graph Object G (input)
Input graph.
Node s (input)
Source node for BFS procedure.
Options and result:
graph When selected outputFormat is graph - procedure
returns a graph structure (struct::graph), which
is equivalent to BFS tree found by algorithm.
tree When selected outputFormat is tree - procedure
returns a tree structure (struct::tree), which is
equivalent to BFS tree found by algorithm.
struct::graph::op::MinimumDiameterSpanningTree G
The goal is to find for input graph G, the spanning tree that
has the minimum diameter value.
General idea of algorithm is to run BFS over all vertices in
graph G. If the diameter d of the tree is odd, then we are sure
that tree given by BFS is minimum (considering diameter value).
When, diameter d is even, then optimal tree can have minimum
diameter equal to d or d-1.
In that case, what algorithm does is rebuilding the tree given
by BFS, by adding a vertice between root node and root's child
node (nodes), such that subtree created with child node as root
node is the greatest one (has the greatests height). In the next
step for such rebuilded tree, we run again BFS with new node as
root node. If the height of the tree didn't changed, we have
found a better solution.
For input graph G algorithm returns the graph structure
(struct::graph) that is a spanning tree with minimum diameter
found by algorithm.
struct::graph::op::MinimumDegreeSpanningTree G
Algorithm finds for input graph G, a spanning tree T with the
minimum possible degree. That problem is NP-hard, so algorithm
is an approximation algorithm.
Let V be the set of nodes for graph G and let W be any subset of
V. Lets assume also that OPT is optimal solution and ALG is
solution found by algorithm for input graph G.
It can be proven that solution found with the algorithm must
fulfil inequality:
((|W| + k - 1) / |W|) <= ALG <= 2*OPT + log2(n) + 1.
Arguments:
Graph Object G (input)
Undirected simple graph.
Result:
Algorithm returns graph structure, which is equivalent to
spanning tree T found by algorithm.
struct::graph::op::MaximumFlowByDinic G s t blockingFlowAlg
Algorithm finds maximum flow for the flow network represented by
graph G. It is based on the blocking-flow finding methods, which
give us different complexities what makes a better fit for dif-
ferent graphs.
Arguments:
Graph Object G (input)
Directed graph G representing the flow network.
Each edge should have attribute throughput set
with integer value.
Node s (input)
The source node for the flow network G.
Node t (input)
The sink node for the flow network G.
Options:
dinic Procedure will find maximum flow for flow network
G using Dinic's algorithm
(struct::graph::op::BlockingFlowByDinic) for
blocking flow computation.
mkm Procedure will find maximum flow for flow network
G using Malhotra, Kumar and Maheshwari's algorithm
(struct::graph::op::BlockingFlowByMKM) for block-
ing flow computation.
Result:
Algorithm returns dictionary containing it's flow value
for each edge (key) in network G.
Note: struct::graph::op::BlockingFlowByDinic gives O(m*n^2) complexity
and struct::graph::op::BlockingFlowByMKM gives O(n^3) complexity, where
n is the number of nodes and m is the number of edges in flow network
G.
struct::graph::op::BlockingFlowByDinic G s t
Algorithm for given network G with source s and sink t, finds a
blocking flow, which can be used to obtain a maximum flow for
that network G.
Arguments:
Graph Object G (input)
Directed graph G representing the flow network.
Each edge should have attribute throughput set
with integer value.
Node s (input)
The source node for the flow network G.
Node t (input)
The sink node for the flow network G.
Result:
Algorithm returns dictionary containing it's blocking
flow value for each edge (key) in network G.
Note: Algorithm's complexity is O(n*m), where n is the number of nodes
and m is the number of edges in flow network G.
struct::graph::op::BlockingFlowByMKM G s t
Algorithm for given network G with source s and sink t, finds a
blocking flow, which can be used to obtain a maximum flow for
that network G.
Arguments:
Graph Object G (input)
Directed graph G representing the flow network.
Each edge should have attribute throughput set
with integer value.
Node s (input)
The source node for the flow network G.
Node t (input)
The sink node for the flow network G.
Result:
Algorithm returns dictionary containing it's blocking
flow value for each edge (key) in network G.
Note: Algorithm's complexity is O(n^2), where n is the number of nodes
in flow network G.
struct::graph::op::createResidualGraph G f
Procedure creates a residual graph (or residual network ) for
network G and given flow f.
Arguments:
Graph Object G (input)
Flow network (directed graph where each edge has
set attribute: throughput ).
dictionary f (input)
Current flows in flow network G.
Result:
Procedure returns graph structure that is a residual
graph created from input flow network G.
struct::graph::op::createAugmentingNetwork G f path
Procedure creates an augmenting network for a given residual
network G , flow f and augmenting path path.
Arguments:
Graph Object G (input)
Residual network (directed graph), where for every
edge there are set two attributes: throughput and
cost.
Dictionary f (input)
Dictionary which contains for every edge (key),
current value of the flow on that edge.
List path (input)
Augmenting path, set of edges (list) for which we
create the network modification.
Result:
Algorithm returns graph structure containing the modified
augmenting network.
struct::graph::op::createLevelGraph Gf s
For given residual graph Gf procedure finds the level graph.
Arguments:
Graph Object Gf (input)
Residual network, where each edge has it's
attribute throughput set with certain value.
Node s (input)
The source node for the residual network Gf.
Result:
Procedure returns a level graph created from input resid-
ual network.
struct::graph::op::TSPLocalSearching G C
Algorithm is a heuristic of local searching for Travelling
Salesman Problem. For some solution of TSP problem, it checks if
it's possible to find a better solution. As TSP is well known
NP-Complete problem, so algorithm is a approximation algorithm
(with 2 approximation factor).
Arguments:
Graph Object G (input)
Undirected and complete graph with attributes
"weight" set on each single edge.
List C (input)
A list of edges being Hamiltonian cycle, which is
solution of TSP Problem for graph G.
Result:
Algorithm returns the best solution for TSP problem, it
was able to find.
Note: The solution depends on the choosing of the beginning cycle C.
It's not true that better cycle assures that better solution will be
found, but practise shows that we should give starting cycle with as
small sum of weights as possible.
struct::graph::op::TSPLocalSearching3Approx G C
Algorithm is a heuristic of local searching for Travelling
Salesman Problem. For some solution of TSP problem, it checks if
it's possible to find a better solution. As TSP is well known
NP-Complete problem, so algorithm is a approximation algorithm
(with 3 approximation factor).
Arguments:
Graph Object G (input)
Undirected and complete graph with attributes
"weight" set on each single edge.
List C (input)
A list of edges being Hamiltonian cycle, which is
solution of TSP Problem for graph G.
Result:
Algorithm returns the best solution for TSP problem, it
was able to find.
Note: In practise 3-approximation algorithm turns out to be far more
effective than 2-approximation, but it gives worser approximation fac-
tor. Further heuristics of local searching (e.g. 4-approximation)
doesn't give enough boost to square the increase of approximation fac-
tor, so 2 and 3 approximations are mainly used.
struct::graph::op::createSquaredGraph G
X-Squared graph is a graph with the same set of nodes as input
graph G, but a different set of edges. X-Squared graph has edge
(u,v), if and only if, the distance between u and v nodes is not
greater than X and u != v.
Procedure for input graph G, returns its two-squared graph.
Note: Distances used in choosing new set of edges are consider-
ing the number of edges, not the sum of weights at edges.
struct::graph::op::createCompleteGraph G originalEdges
For input graph G procedure adds missing arcs to make it a com-
plete graph. It also holds in variable originalEdges the set of
arcs that graph G possessed before that operation.
BACKGROUND THEORY AND TERMS
SHORTEST PATH PROBLEM
Definition (single-pair shortest path problem):
Formally, given a weighted graph (let V be the set of vertices,
and E a set of edges), and one vertice v of V, find a path P
from v to a v' of V so that the sum of weights on edges along
the path is minimal among all paths connecting v to v'.
Generalizations:
o The single-source shortest path problem, in which we have
to find shortest paths from a source vertex v to all
other vertices in the graph.
o The single-destination shortest path problem, in which we
have to find shortest paths from all vertices in the
graph to a single destination vertex v. This can be
reduced to the single-source shortest path problem by
reversing the edges in the graph.
o The all-pairs shortest path problem, in which we have to
find shortest paths between every pair of vertices v, v'
in the graph.
Note: The result of Shortest Path problem can be Shortest Path tree,
which is a subgraph of a given (possibly weighted) graph constructed so
that the distance between a selected root node and all other nodes is
minimal. It is a tree because if there are two paths between the root
node and some vertex v (i.e. a cycle), we can delete the last edge of
the longer path without increasing the distance from the root node to
any node in the subgraph.
TRAVELLING SALESMAN PROBLEM
Definition:
For given edge-weighted (weights on edges should be positive)
graph the goal is to find the cycle that visits each node in
graph exactly once (Hamiltonian cycle).
Generalizations:
o Metric TSP - A very natural restriction of the TSP is to
require that the distances between cities form a metric,
i.e., they satisfy the triangle inequality. That is, for
any 3 cities A, B and C, the distance between A and C
must be at most the distance from A to B plus the dis-
tance from B to C. Most natural instances of TSP satisfy
this constraint.
o Euclidean TSP - Euclidean TSP, or planar TSP, is the TSP
with the distance being the ordinary Euclidean distance.
Euclidean TSP is a particular case of TSP with triangle
inequality, since distances in plane obey triangle
inequality. However, it seems to be easier than general
TSP with triangle inequality. For example, the minimum
spanning tree of the graph associated with an instance of
Euclidean TSP is a Euclidean minimum spanning tree, and
so can be computed in expected O(n log n) time for n
points (considerably less than the number of edges). This
enables the simple 2-approximation algorithm for TSP with
triangle inequality above to operate more quickly.
o Asymmetric TSP - In most cases, the distance between two
nodes in the TSP network is the same in both directions.
The case where the distance from A to B is not equal to
the distance from B to A is called asymmetric TSP. A
practical application of an asymmetric TSP is route opti-
misation using street-level routing (asymmetric due to
one-way streets, slip-roads and motorways).
MATCHING PROBLEM
Definition:
Given a graph G = (V,E), a matching or edge-independent set M in
G is a set of pairwise non-adjacent edges, that is, no two edges
share a common vertex. A vertex is matched if it is incident to
an edge in the matching M. Otherwise the vertex is unmatched.
Generalizations:
o Maximal matching - a matching M of a graph G with the
property that if any edge not in M is added to M, it is
no longer a matching, that is, M is maximal if it is not
a proper subset of any other matching in graph G. In
other words, a matching M of a graph G is maximal if
every edge in G has a non-empty intersection with at
least one edge in M.
o Maximum matching - a matching that contains the largest
possible number of edges. There may be many maximum
matchings. The matching number of a graph G is the size
of a maximum matching. Note that every maximum matching
is maximal, but not every maximal matching is a maximum
matching.
o Perfect matching - a matching which matches all vertices
of the graph. That is, every vertex of the graph is inci-
dent to exactly one edge of the matching. Every perfect
matching is maximum and hence maximal. In some litera-
ture, the term complete matching is used. A perfect
matching is also a minimum-size edge cover. Moreover, the
size of a maximum matching is no larger than the size of
a minimum edge cover.
o Near-perfect matching - a matching in which exactly one
vertex is unmatched. This can only occur when the graph
has an odd number of vertices, and such a matching must
be maximum. If, for every vertex in a graph, there is a
near-perfect matching that omits only that vertex, the
graph is also called factor-critical.
Related terms:
o Alternating path - given a matching M, an alternating
path is a path in which the edges belong alternatively to
the matching and not to the matching.
o Augmenting path - given a matching M, an augmenting path
is an alternating path that starts from and ends on free
(unmatched) vertices.
CUT PROBLEMS
Definition:
A cut is a partition of the vertices of a graph into two dis-
joint subsets. The cut-set of the cut is the set of edges whose
end points are in different subsets of the partition. Edges are
said to be crossing the cut if they are in its cut-set.
Formally:
o a cut C = (S,T) is a partition of V of a graph G = (V,
E).
o an s-t cut C = (S,T) of a flow network N = (V, E) is a
cut of N such that s is included in S and t is included
in T, where s and t are the source and the sink of N
respectively.
o The cut-set of a cut C = (S,T) is such set of edges from
graph G = (V, E) that each edge (u, v) satisfies condi-
tion that u is included in S and v is included in T.
In an unweighted undirected graph, the size or weight of a cut is the
number of edges crossing the cut. In a weighted graph, the same term is
defined by the sum of the weights of the edges crossing the cut.
In a flow network, an s-t cut is a cut that requires the source and the
sink to be in different subsets, and its cut-set only consists of edges
going from the source's side to the sink's side. The capacity of an s-t
cut is defined by the sum of capacity of each edge in the cut-set.
The cut of a graph can sometimes refer to its cut-set instead of the
partition.
Generalizations:
o Minimum cut - A cut is minimum if the size of the cut is
not larger than the size of any other cut.
o Maximum cut - A cut is maximum if the size of the cut is
not smaller than the size of any other cut.
o Sparsest cut - The Sparsest cut problem is to bipartition
the vertices so as to minimize the ratio of the number of
edges across the cut divided by the number of vertices in
the smaller half of the partition.
K-CENTER PROBLEM
Definitions:
Unweighted K-Center
For any set S ( which is subset of V ) and node v, let
the connect(v,S) be the cost of cheapest edge connecting
v with any node in S. The goal is to find such S, that
|S| = k and max_v{connect(v,S)} is possibly small.
In other words, we can use it i.e. for finding best loca-
tions in the city ( nodes of input graph ) for placing k
buildings, such that those buildings will be as close as
possible to all other locations in town.
Weighted K-Center
The variation of unweighted k-center problem. Besides the
fact graph is edge-weighted, there are also weights on
vertices of input graph G. We've got also restriction W.
The goal is to choose such set of nodes S ( which is a
subset of V ), that it's total weight is not greater than
W and also function: max_v { min_u { cost(u,v) }} has the
smallest possible worth ( v is a node in V and u is a
node in S ).
FLOW PROBLEMS
Definitions:
o the maximum flow problem - the goal is to find a feasible
flow through a single-source, single-sink flow network
that is maximum. The maximum flow problem can be seen as
a special case of more complex network flow problems,
such as the circulation problem. The maximum value of an
s-t flow is equal to the minimum capacity of an s-t cut
in the network, as stated in the max-flow min-cut theo-
rem.
More formally for flow network G = (V,E), where for each
edge (u, v) we have its throuhgput c(u,v) defined. As
flow F we define set of non-negative integer attributes
f(u,v) assigned to edges, satisfying such conditions:
[1] for each edge (u, v) in G such condition should be
satisfied: 0 <= f(u,v) <= c(u,v)
[2] Network G has source node s such that the flow F
is equal to the sum of outcoming flow decreased by
the sum of incoming flow from that source node s.
[3] Network G has sink node t such that the the -F
value is equal to the sum of the incoming flow
decreased by the sum of outcoming flow from that
sink node t.
[4] For each node that is not a source or sink the sum
of incoming flow and sum of outcoming flow should
be equal.
o the minimum cost flow problem - the goal is finding the
cheapest possible way of sending a certain amount of flow
through a flow network.
o blocking flow - a blocking flow for a residual network Gf
we name such flow b in Gf that:
[1] Each path from sink to source is the shortest path
in Gf.
[2] Each shortest path in Gf contains an edge with
fully used throughput in Gf+b.
o residual network - for a flow network G and flow f resid-
ual network is built with those edges, which can send
larger flow. It contains only those edges, which can send
flow larger than 0.
o level network - it has the same set of nodes as residual
graph, but has only those edges (u,v) from Gf for which
such equality is satisfied: distance(s,u)+1 = dis-
tance(s,v).
o augmenting network - it is a modification of residual
network considering the new flow values. Structure stays
unchanged but values of throughputs and costs at edges
are different.
APPROXIMATION ALGORITHM
k-approximation algorithm:
Algorithm is a k-approximation, when for ALG (solution returned
by algorithm) and OPT (optimal solution), such inequality is
true:
o for minimalization problems: ALG/OPT <= k
o for maximalization problems: OPT/ALG <= k
REFERENCES
[1] Adjacency matrix [http://en.wikipedia.org/wiki/Adjacency_matrix]
[2] Adjacency list [http://en.wikipedia.org/wiki/Adjacency_list]
[3] Kruskal's algorithm
[http://en.wikipedia.org/wiki/Kruskal%27s_algorithm]
[4] Prim's algorithm [http://en.wikipedia.org/wiki/Prim%27s_algo-
rithm]
[5] Bipartite graph [http://en.wikipedia.org/wiki/Bipartite_graph]
[6] Strongly connected components
[http://en.wikipedia.org/wiki/Strongly_connected_components]
[7] Tarjan's strongly connected components algorithm
[http://en.wikipedia.org/wiki/Tarjan%27s_strongly_connected_com-
ponents_algorithm]
[8] Cut vertex [http://en.wikipedia.org/wiki/Cut_vertex]
[9] Bridge [http://en.wikipedia.org/wiki/Bridge_(graph_theory)]
[10] Bellman-Ford's algorithm [http://en.wikipedia.org/wiki/Bellman-
Ford_algorithm]
[11] Johnson's algorithm [http://en.wikipedia.org/wiki/Johnson_algo-
rithm]
[12] Floyd-Warshall's algorithm [http://en.wikipedia.org/wiki/Floyd-
Warshall_algorithm]
[13] Travelling Salesman Problem [http://en.wikipedia.org/wiki/Trav-
elling_salesman_problem]
[14] Christofides Algorithm
[http://en.wikipedia.org/wiki/Christofides_algorithm]
[15] Max Cut [http://en.wikipedia.org/wiki/Maxcut]
[16] Matching [http://en.wikipedia.org/wiki/Matching]
[17] Max Independent Set [http://en.wikipedia.org/wiki/Maximal_inde-
pendent_set]
[18] Vertex Cover [http://en.wikipedia.org/wiki/Vertex_cover_problem]
[19] Ford-Fulkerson's algorithm [http://en.wikipedia.org/wiki/Ford-
Fulkerson_algorithm]
[20] Maximum Flow problem [http://en.wikipedia.org/wiki/Maxi-
mum_flow_problem]
[21] Busacker-Gowen's algorithm [http://en.wikipedia.org/wiki/Mini-
mum_cost_flow_problem]
[22] Dinic's algorithm [http://en.wikipedia.org/wiki/Dinic's_algo-
rithm]
[23] K-Center problem [http://www.csc.kth.se/~viggo/wwwcom-
pendium/node128.html]
[24] BFS [http://en.wikipedia.org/wiki/Breadth-first_search]
[25] Minimum Degree Spanning Tree
[http://en.wikipedia.org/wiki/Degree-constrained_spanning_tree]
[26] Approximation algorithm [http://en.wikipedia.org/wiki/Approxima-
tion_algorithm]
BUGS, IDEAS, FEEDBACK
This document, and the package it describes, will undoubtedly contain
bugs and other problems. Please report such in the category struct ::
graph of the Tcllib SF Trackers [http://source-
forge.net/tracker/?group_id=12883]. Please also report any ideas for
enhancements you may have for either package and/or documentation.
KEYWORDS
adjacency list, adjacency matrix, adjacent, approximation algorithm,
arc, articulation point, augmenting network, augmenting path, bfs,
bipartite, blocking flow, bridge, complete graph, connected component,
cut edge, cut vertex, degree, degree constrained spanning tree, diame-
ter, dijkstra, distance, eccentricity, edge, flow network, graph,
heuristic, independent set, isthmus, level graph, local searching,
loop, matching, max cut, maximum flow, minimal spanning tree, minimum
cost flow, minimum degree spanning tree, minimum diameter spanning
tree, neighbour, node, radius, residual graph, shortest path, squared
graph, strongly connected component, subgraph, travelling salesman,
vertex, vertex cover
CATEGORY
Data structures
COPYRIGHT
Copyright (c) 2008 Alejandro Paz <vidriloco@gmail.com>
Copyright (c) 2008 (docs) Andreas Kupries <andreas_kupries@users.sourceforge.net>
Copyright (c) 2009 Michal Antoniewski <antoniewski.m@gmail.com>
struct 0.11.3 struct::graph::op(n)
Mac OS X 10.8 - Generated Fri Sep 7 15:40:50 CDT 2012