Data Structures and Algorithms in Java
6th Edition
ISBN: 9781118771334
Author: Michael T. Goodrich
Publisher: WILEY
expand_more
expand_more
format_list_bulleted
Expert Solution & Answer
Chapter 4, Problem 7R
Explanation of Solution
a)
Running time of
It is given that the running time of algorithm A is
Reason:
- If the
Explanation of Solution
b)
Running time of Algorithm A in worst case:
It is given that the worst-case running time of algorithm A is
Reason:
- It is important to note that the worst case running time is always greater than or equal to the average case running time
Expert Solution & Answer
Want to see the full answer?
Check out a sample textbook solution
Students have asked these similar questions
Which of the following best describes the useful criterion for comparing the
efficiency of algorithms?
None of the above
Memory
Time
Both Time and Memory
2. Algorithm A has a running time described by the recurrence T(n) = 7T(n/2) + n². A
competing algorithm B has a running time described by the recurrence T(n) =
aT (n/4) + n². What is the largest integer value for a such that B is asymptotically faster
than A? Explain your answer.
1. If I prove that an algorithm takes Θ(n2) worst-case time, is it possible that it takes O(n) on some inputs?
2. If I prove that an algorithm takes Θ(n2) worst-case time, is it possible that it takes O(n) on all inputs?
Chapter 4 Solutions
Data Structures and Algorithms in Java
Ch. 4 - Prob. 1RCh. 4 - The number of operations executed by algorithms A...Ch. 4 - The number of operations executed by algorithms A...Ch. 4 - Prob. 4RCh. 4 - Prob. 5RCh. 4 - Prob. 6RCh. 4 - Prob. 7RCh. 4 - Prob. 8RCh. 4 - Prob. 9RCh. 4 - Prob. 10R
Ch. 4 - Prob. 11RCh. 4 - Prob. 12RCh. 4 - Prob. 13RCh. 4 - Prob. 14RCh. 4 - Prob. 15RCh. 4 - Prob. 16RCh. 4 - Prob. 17RCh. 4 - Prob. 18RCh. 4 - Prob. 19RCh. 4 - Prob. 20RCh. 4 - Prob. 21RCh. 4 - Prob. 22RCh. 4 - Show that 2n+1 is O(2n).Ch. 4 - Prob. 24RCh. 4 - Prob. 25RCh. 4 - Prob. 26RCh. 4 - Prob. 27RCh. 4 - Prob. 28RCh. 4 - Prob. 29RCh. 4 - Prob. 30RCh. 4 - Prob. 31RCh. 4 - Prob. 32RCh. 4 - Prob. 33RCh. 4 - Prob. 34RCh. 4 - Prob. 35CCh. 4 - Prob. 36CCh. 4 - Prob. 37CCh. 4 - Prob. 38CCh. 4 - Prob. 39CCh. 4 - Prob. 40CCh. 4 - Prob. 41CCh. 4 - Prob. 42CCh. 4 - Prob. 43CCh. 4 - Draw a visual justification of Proposition 4.3...Ch. 4 - Prob. 45CCh. 4 - Prob. 46CCh. 4 - Communication security is extremely important in...Ch. 4 - Al says he can prove that all sheep in a flock are...Ch. 4 - Consider the following justification that the...Ch. 4 - Consider the Fibonacci function, F(n) (see...Ch. 4 - Prob. 51CCh. 4 - Prob. 52CCh. 4 - Prob. 53CCh. 4 - Prob. 54CCh. 4 - An evil king has n bottles of wine, and a spy has...Ch. 4 - Prob. 56CCh. 4 - Prob. 57CCh. 4 - Prob. 58CCh. 4 - Prob. 59CCh. 4 - Prob. 60PCh. 4 - Prob. 61PCh. 4 - Perform an experimental analysis to test the...Ch. 4 - Prob. 63P
Knowledge Booster
Similar questions
- Algorithm A and B have a worst case running time of 0 and O(log n), respectively. Therefore, algorithm B always run faster than algorithm A. Select one: O True O Falsearrow_forwardTrue or False?(a) If I prove that an algorithm takes O ( n 2 ) worst-case time, then it is possible that it takes O ( n1.9 ) on some inputs.(b) If I prove that an algorithm takes O ( n 3 ) worst-case time, then it is possible that it takes O ( n2.9 ) on all inputs.(c) If I prove that an algorithm takes Θ( n 4 ) worst-case time, then it is possible that it takes O ( n3.9 ) on some inputs.(d) If I prove that an algorithm takes Θ(n 5 ) worst-case time, then it is possible that it takes O ( n4.9 ) on all inputs.(e) An algorithm that takes n3+5n time is always faster than an algorithm that takes O(n3) time.arrow_forwardAnother recursive algorithm is applied to some data A = (a₁, ..., am) where m = 2* (i.e. 2, 4, 8,16 ...) where x is an integer ≥ 1. The running time T is characterised using the following recurrence equations: T(1) = c when the size of A is 1 T(m) = 2T (2) + c otherwise Determine the running time complexity of this algorithm.arrow_forward
- 2. For a problem we have come up with three algorithms: A, B, and C. Running time of Algorithm A is O(n¹000), Algorithm B runs in 0(2¹) and Algorithm C runs in O(n!). How do these algorithms compare in terms of speed, for large input? Explain why.arrow_forwardAlgorithm X has a growth rate that is proportional to n ∗ n ∗ n . What is the function that represents the growth rate of algorithm X? What is the order of algorithm X?arrow_forwardState True or false, for each of the following statements, and briefly explain why no points will be given even for a correct solution if no justification is presented. resented. 1. For all asymptotically positive f(n).f(n) + O(f(n)) = (f(n)) 2. The worst-case running time and expected running time are equal to within constant factors for any randomized algorithm. 3. There exists a data structure to maintain a dynamic set with operations Insert (x,S), Delete(x,S). and member? (x,S) that has an expected running time of O(1) per operation. 4. Let A₁, A₂ and A3 be three sorted arrays of n-real numbers (all distinct). In the comparison model constructing a balanced binary search tree of the set A₁ U A₂ U A3 requires Q(nlogn) time. 5. If dynamic programming problem satisfies the optimal-substructure property, then a locally optimal solution is globally optimal.arrow_forward
- Heuristics Prove or disprove: If h1(n), ..., hk(n) are admissible, so is h(n) = h1(n) + ... + hk(n)arrow_forwardConsider the following functions as time complexities of some algorithms. (a) First write the worst-case runtime of each algorithm in Big-O notation. (b) Then arrange functions from low to high (Consider their Big-O notations as you arrange them; as n grows, the function that grows slower should come sooner than the one that grows faster). (c) Arbitrary, select TWO of arrangements in part (b) and for each one of them justify why you have chosen such an ordering for the corresponding function. Provide formal proof. For example if fl<=f2<= f3<= f4<=f5 you may choose to formally justify why (1) fl<=f2 and why (2) f4<=f5 . fI(n)=n², f2(n)=210 nlogn, f3(n)=3", f4(n)=n, f5(n)=1, f6(n)=5*logn, f7(n)= n", f8(n)=4 n*n, f9(n)=2log(n³)arrow_forward3.1-6 Prove that the running time of an algorithm is O(g(n)) if and only if its worst-case running time is 0(g(n)) and its best-case running time is 2(g(n)).arrow_forward
- Consider two algorithms for the same problem: • Algorithm A, which runs in O(n) and produces a correct answer with probability 0.7, and a wrong answer with probability 0.3. • Algorithm B, which runs in O(n log n) and produces a correct answer with probability 0.99, and a wrong answer with probability 0.01. Which of the two algorithms should you use to build a more asymptotically (in n) efficient algorithm with probability 0.99 of producing a correct answer? Justify. Assume that you can always check if an answer is correct in constant time.arrow_forwardSuppose that f (n) = 0(g(n)) and f(n) = 0(h(n)), then it is ( always / sometimes / never ) the case that g(n) = 0(h(n)).arrow_forward2. Honest Goblins are Easy to Find At Hogwarts a new shipment of n goblins has arrived. To be of any use, a goblin must be completely truthful (never lies). Unfortunately, not all of the n goblins in the shipment are truth tellers. Only some are truth-tellers, and some are deceivers. It is your task to design an algorithm to separate the truth-teller goblins from the deceiver goblins. To do this, you have one tool available: You may combine any two goblins and have them state whether the other goblin is a truth-teller or a deceiver. A truth-teller will always say correctly what the other goblin is, but a deceiver may lie (but also may sometimes tell the truth to REALLY confuse you). For any two goblins that you test, the following can be concluded from the goblin responses: Goblin A says Goblin B says Conclusion B is a truth-teller A is a truth-teller both are truth-tellers or both are deceivers B is a truth-teller A is a deceiver at least one is a deceiver B is a deceiver A is a…arrow_forward
arrow_back_ios
SEE MORE QUESTIONS
arrow_forward_ios
Recommended textbooks for you
Database System ConceptsComputer ScienceISBN:9780078022159Author:Abraham Silberschatz Professor, Henry F. Korth, S. SudarshanPublisher:McGraw-Hill Education
Starting Out with Python (4th Edition)Computer ScienceISBN:9780134444321Author:Tony GaddisPublisher:PEARSON
Digital Fundamentals (11th Edition)Computer ScienceISBN:9780132737968Author:Thomas L. FloydPublisher:PEARSON
C How to Program (8th Edition)Computer ScienceISBN:9780133976892Author:Paul J. Deitel, Harvey DeitelPublisher:PEARSON
Database Systems: Design, Implementation, & Manag...Computer ScienceISBN:9781337627900Author:Carlos Coronel, Steven MorrisPublisher:Cengage Learning
Programmable Logic ControllersComputer ScienceISBN:9780073373843Author:Frank D. PetruzellaPublisher:McGraw-Hill Education
Database System Concepts
Computer Science
ISBN:9780078022159
Author:Abraham Silberschatz Professor, Henry F. Korth, S. Sudarshan
Publisher:McGraw-Hill Education
Starting Out with Python (4th Edition)
Computer Science
ISBN:9780134444321
Author:Tony Gaddis
Publisher:PEARSON
Digital Fundamentals (11th Edition)
Computer Science
ISBN:9780132737968
Author:Thomas L. Floyd
Publisher:PEARSON
C How to Program (8th Edition)
Computer Science
ISBN:9780133976892
Author:Paul J. Deitel, Harvey Deitel
Publisher:PEARSON
Database Systems: Design, Implementation, & Manag...
Computer Science
ISBN:9781337627900
Author:Carlos Coronel, Steven Morris
Publisher:Cengage Learning
Programmable Logic Controllers
Computer Science
ISBN:9780073373843
Author:Frank D. Petruzella
Publisher:McGraw-Hill Education