Question

Implement an approximation algorithm for the Traveling Salesman problem, which outputs a solution which is a 2-approximation of the optimal weight. 1 import networkx as nx 2 3 # This function takes as input a graph g. 4 # The graph is complete (i.e., each pair of distinct vertices is connected by an edge), 5 # undirected (i.e., the edge from u to v has the same weight as the edge from v to u), 6 # and has no self-loops (i.e., there are no edges from i to i). 7 # 8 # The function should return a 2-approximation of an optimal Hamiltonian cycle. 9 10 def approximation(g): 11 #n is the number of vertices. 12 n = g.number_of_nodes() 13 14 # You might want to use the function "nx.minimum_spanning_tree(g)" 15 # which returns a Minimum Spanning Tree of the graph g 16 17 18 # You also might want to use the command "list(nx.dfs_preorder_nodes (graph, 0))" # which gives a list of vertices of the given graph in depth-first preorder. 19 20 return ???

          Implement an approximation algorithm for the Traveling Salesman problem, which outputs a solution which is a 2-approximation of the optimal weight.
1 import networkx as nx
2
3
# This function takes as input a graph g.
4
# The graph is complete (i.e., each pair of distinct vertices is connected by an edge),
5
# undirected (i.e., the edge from u to v has the same weight as the edge from v to u),
6
# and has no self-loops (i.e., there are no edges from i to i).
7
#
8
# The function should return a 2-approximation of an optimal Hamiltonian cycle.
9
10
def approximation(g):
11
#n is the number of vertices.
12
n = g.number_of_nodes()
13
14
# You might want to use the function "nx.minimum_spanning_tree(g)"
15
# which returns a Minimum Spanning Tree of the graph g
16
17
18
# You also might want to use the command "list(nx.dfs_preorder_nodes (graph, 0))"
# which gives a list of vertices of the given graph in depth-first preorder.
19
20
return ???

Implement an approximation algorithm for the Traveling Salesman problem, which outputs a solution which is a 2-approximation of the optimal weight.
1 import networkx as nx
2
3
# This function takes as input a graph g.
4
# The graph is complete (i.e., each pair of distinct vertices is connected by an edge),
5
# undirected (i.e., the edge from u to v has the same weight as the edge from v to u),
6
# and has no self-loops (i.e., there are no edges from i to i).
7
#
8
# The function should return a 2-approximation of an optimal Hamiltonian cycle.
9
10
def approximation(g):
11
#n is the number of vertices.
12
n = g.numberofnodes()
13
14
# You might want to use the function "nx.minimumspanningtree(g)"
15
# which returns a Minimum Spanning Tree of the graph g
16
17
18
# You also might want to use the command "list(nx.dfspreordernodes (graph, 0))"
# which gives a list of vertices of the given graph in depth-first preorder.
19
20
return ???

Added by Kenneth W.

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