Python code for the following problem. The format needs to be the same as the instructions. eg: def approximation(g): n = g.number_of_nodes() Need to use function "nx.minimum_spanning_tree(g)" and command "list(nx.dfs_preorder_nodes(graph, 0))". Thank you.
Python code for the following problem. The format needs to be the same as the instructions. eg: def approximation(g): n = g.number_of_nodes() Need to use function "nx.minimum_spanning_tree(g)" and command "list(nx.dfs_preorder_nodes(graph, 0))". Thank you.
Computer Networking: A Top-Down Approach (7th Edition)
7th Edition
ISBN:9780133594140
Author:James Kurose, Keith Ross
Publisher:James Kurose, Keith Ross
Chapter1: Computer Networks And The Internet
Section: Chapter Questions
Problem R1RQ
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Question
Python code for the following problem.
The format needs to be the same as the instructions. eg:
def approximation(g):
n = g.number_of_nodes()
Need to use function "nx.minimum_spanning_tree(g)" and command "list(nx.dfs_preorder_nodes(graph, 0))".
Thank you.

Transcribed Image Text: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
3
# This function takes as input a graph g.
# The graph is complete (i.e., each pair of distinct vertices is connected by an edge),
# undirected (i.e., the edge from u to v has the same weight as the edge from v to u),
# and has no self-loops (i.e., there are no edges from i to i).
6.
7
#3
8.
# The function should return a 2-approximation of an optimal Hamiltonian cycle.
9.
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def approximation(g):
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# 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
# You also might want to use the command "list(nx.dfs_preorder_nodes(graph, 0) )"
18
# which gives a list of vertices of the given graph in depth-first preorder.
19
20
return ???
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