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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 5+ n + 0.25n 500n + 50n*log(n) Dominant term 0() 200 + n/2 n? + 2n + 0.5n? + 3n n+n'5+ n°5+ 20n 3n + n + 7n + 10

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 5+ n + 0.25n 500n + 50n*log(n) Dominant term 0() 200 + n/2 n? + 2n + 0.5n? + 3n n+n'5+ n°5+ 20n 3n + n + 7n + 10

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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Please answer the questions in image. Please answer question 1.1 and Question 1.2 (A, B,and C ) Separately . Please explain also. The 1 and 2 written in black just shows that this is the 1st picture and this is the 2nd picture
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
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")
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