def main():
G_in=get_graph.edge_list() # read the input instance
G=copy.deepcopy(G_in)
k=int(input("Enter k:"))
d=int(input("Enter d:"))
G,k,kernel_solution=kernel.kernelization(G,k) # do kernelization
print("Kernelization",k,kernel_solution)
search_solution=search(G,[],[],G.nodes(),[],k,d,[0],[])
print("No Solution")
def main():
G = get_graph.edge_list() # read the input instance
print_graph.input(G) # construct input graph for display
k = int(input("Enter k:"))
G, k, solution = kernel.kernelization(G, k) # do kernelization
print("Kernelization", k, solution)
if not branching(G, k, solution, []):
print("No Solution")
else:
G.remove_nodes_from(sol)
print_graph.output(G) # construct output graph for display
def main():
G = get_graph.edge_list() # read the input instance
print_graph.input(G)
k = int(input("Enter k:"))
G, k, solution = kernel.kernelization(G, k) # do kernelization
print("Kernelization", k, solution)
p3s = kernel.get_p3(G)
if not three_way_branching(p3s, k, solution):
print("No Solution")
else:
G.remove_nodes_from(sol)
print_graph.output(G)
print_graph.show()
def main():
G = get_graph.edge_list() # read the input instance
print_graph.input(G)
k = int(input("Enter k:"))
d = int(input("Enter d:"))
G, k, kernel_solution = kernel.kernelization(G, k) # do kernelization
print("Kernelization", k, kernel_solution)
if not branching(G, [], k, d, []):
print("No Solution")
else:
print("CVD solution: ", sol)
G.remove_nodes_from(sol)
print_graph.output(G)
print_graph.show()
def main():
G=get_graph.edge_list() # read the input instance
print_graph.input(G)
k=int(input("Enter k:"))
G,k,kernel_solution=kernel.kernelization(G,k) # do kernelization
print("Kernelization",k,kernel_solution)
brute_solution=brute(G,k) # do brute force
if k>0 and brute_solution == []:
print ("No Solution")
else:
solution=list(set(kernel_solution)|set(brute_solution)) # combine kernelization and brute force approach results
print("CVD solution: ",solution)
G.remove_nodes_from(solution)
print_graph.output(G)
print_graph.show()
#print("rr1:",rr1)
rr2 = reduction_rule2(G_guess, out, G_S) # reduction rule 2
G_S.remove_nodes_from(rr2)
G_guess.remove_nodes_from(rr2)
#print("rr2:",rr2)
solution_d = construct_G(G_guess, out,
G_S) # weighted bipartite matching
if len(solution_d) + len(rr1) + len(rr2) + len(
guess) <= k: # check for a k- sized solution
# return the k sized solution
return list(set().union(solution_d, rr1, rr2, list(guess)))
# return the k+1 sized solution
return solution
G_input = get_graph.edge_list() # get the input instances
k = int(input("Enter K:"))
print_input_graph(G_input) # display the input graph
G, k, solution = kernel.kernelization(G_input, k) # do kernelization
print("Kernelization", k, solution)
G.remove_nodes_from(solution)
for nodes in itertools.combinations(G.nodes(),
k + 2): # to get a k+2 number of nodes
break
nodes = list(nodes)
G_induced = G.subgraph(nodes).copy() # induce a graph with k+2 nodes
for solution in itertools.combinations(
G.nodes(),
k + 1): # get a k+1 solution for the graph induced with k+2 nodes