Direction: Assume n is a positive integer (since it refers to input size), identify the complexity...

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Direction: Assume n is a positive integer (since it refers to input size), identify the complexity (Big-O set) of the following algorithms and briefly explain why they belong to that Big-O complexity. #Problem 1 def f1(n): #Problem 4 def f4(n): m=n for 1 in range (n): print(n) for i in range(n): print(n) #Problem 2 def f2(n): print("n is n") for i in range(n): if n =0: break print(n) # Problem 3 def f3(n): while n! 1: n-math.floor(n/2) print(n) n=1 m=m/m m=m*10 for i in range (int(m/2)): print("n") #Problem 5 def f5(n): for i in range(n): for 1 in range(n): #Problem 6 def f6(n): x=1 print(n) x=2 x=3 for i in range (n): for 1 in range(10): print(n) Direction: Assume n is a positive integer (since it refers to input size), identify the complexity (Big-O set) of the following algorithms and briefly explain why they belong to that Big-O complexity. #Problem 1 def f1(n): #Problem 4 def f4(n): m=n for 1 in range (n): print(n) for i in range(n): print(n) #Problem 2 def f2(n): print("n is n") for i in range(n): if n =0: break print(n) # Problem 3 def f3(n): while n! 1: n-math.floor(n/2) print(n) n=1 m=m/m m=m*10 for i in range (int(m/2)): print("n") #Problem 5 def f5(n): for i in range(n): for 1 in range(n): #Problem 6 def f6(n): x=1 print(n) x=2 x=3 for i in range (n): for 1 in range(10): print(n)

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Lets analyze each of the given algorithms and determine their BigO complexity 1 Problem 1 python def ... View the full answer