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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)
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: What is the difference between a host and an end system? List several different types of end...
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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.

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. 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 # 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 ???
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. 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 # 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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