What is the time complexity of the following three algorithms? Express your answer in terms of Big- O notation and justify your answer: A) def display_names_with_ID (names): ID = 0; for n in names: print ("ID for ", n, "is", ID) ID = ID + 1

Computer Networking: A Top-Down Approach (7th Edition)
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ISBN:9780133594140
Author:James Kurose, Keith Ross
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Chapter1: Computer Networks And The Internet
Section: Chapter Questions
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B)
def find_item (mylist, item) :
if len (mylist)
return False
== 0:
else:
n = len (mylist)//2
print ("Midpoint =", mylist[n] )
if mylist [n] == item:
return True
else:
if item<mylist [n]:
return find item (mylist[:n], item)
else:
return find_item (mylist [n+1:], item)
C)
print ("Times tables grid")
n = 10
for i in range (1, n+1) :
print (i, end="\t")
for i in range (0, 75):
print ("=", end='')
print ()
for i in range (1, n+1):
for j in range (1, n+1):
print (i * j, end="\t")
Transcribed Image Text:B) def find_item (mylist, item) : if len (mylist) return False == 0: else: n = len (mylist)//2 print ("Midpoint =", mylist[n] ) if mylist [n] == item: return True else: if item<mylist [n]: return find item (mylist[:n], item) else: return find_item (mylist [n+1:], item) C) print ("Times tables grid") n = 10 for i in range (1, n+1) : print (i, end="\t") for i in range (0, 75): print ("=", end='') print () for i in range (1, n+1): for j in range (1, n+1): print (i * j, end="\t")
Section 1- Algorithm Efficiency
Question 1.1
Assume that each of the expressions below gives the processing time T(n) spent by an algorithm for
solving a problem of input size n. Specify the dominant term having the steepest increase in n. What
is O() in each case? Give your answers by completing the table.
Expression
Dominant term
0()
5+ n+ 0.25n
500n + 50n*log(n)
200 + n/2
n + 2n + 0.5n² + 3n?
n+n5 + n05 + 20n
3n + n² + 7n + 10
Question 1.2
What is the time complexity of the following three algorithms? Express your answer in terms of Big-
O notation and justify your answer:
A)
def display_names_with_ID(names):
ID = 0;
for n in names:
print ("ID for ", n, "is", ID)
ID = ID + 1
Transcribed Image Text:Section 1- Algorithm Efficiency Question 1.1 Assume that each of the expressions below gives the processing time T(n) spent by an algorithm for solving a problem of input size n. Specify the dominant term having the steepest increase in n. What is O() in each case? Give your answers by completing the table. Expression Dominant term 0() 5+ n+ 0.25n 500n + 50n*log(n) 200 + n/2 n + 2n + 0.5n² + 3n? n+n5 + n05 + 20n 3n + n² + 7n + 10 Question 1.2 What is the time complexity of the following three algorithms? Express your answer in terms of Big- O notation and justify your answer: A) def display_names_with_ID(names): ID = 0; for n in names: print ("ID for ", n, "is", ID) ID = ID + 1
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