Introduction to Algorithms
3rd Edition
ISBN: 9780262033848
Author: Thomas H. Cormen, Ronald L. Rivest, Charles E. Leiserson, Clifford Stein
Publisher: MIT Press
expand_more
expand_more
format_list_bulleted
Question
Chapter 35.4, Problem 1E
Program Plan Intro
Toprove that both variable and its negation yields a randomized 8/7 − approximation
Expert Solution & Answer
Want to see the full answer?
Check out a sample textbook solutionStudents have asked these similar questions
Give an example of a random variable X : {b, c, d, e} → N (Natural Number) with expectation 2, where each of {b, c, d, e} has equal probability
no handwritten
Alice and Bob are playing a match to see who is the first to win n games, for some fixed n > 0. Suppose Alice and Bob are equally competent, that is, each of them wins a game with probability 1/2. Further, suppose that they have already played i + j games, of which Alice won i and Bob won j.
Give an efficient algorithm to compute the probability that Alice will go on to win the match. For example, if i = n − 1 and j = n − 3, then the probability that Alice will win the match is 7/8, since she must win any of the next three games.
Using the code in the picture (Phyton 3):
Find the Recurrence relation for foo(a, b) when b > 0 (Follow the format)
T(n) = ___ T ( ___ / ___ ) + O ( ___ )
What is the worst-case time complexity of foo(a, b)?
What is the worst-case auxiliary space complexity of foo(a,b)?
Chapter 35 Solutions
Introduction to Algorithms
Ch. 35.1 - Prob. 1ECh. 35.1 - Prob. 2ECh. 35.1 - Prob. 3ECh. 35.1 - Prob. 4ECh. 35.1 - Prob. 5ECh. 35.2 - Prob. 1ECh. 35.2 - Prob. 2ECh. 35.2 - Prob. 3ECh. 35.2 - Prob. 4ECh. 35.2 - Prob. 5E
Ch. 35.3 - Prob. 1ECh. 35.3 - Prob. 2ECh. 35.3 - Prob. 3ECh. 35.3 - Prob. 4ECh. 35.3 - Prob. 5ECh. 35.4 - Prob. 1ECh. 35.4 - Prob. 2ECh. 35.4 - Prob. 3ECh. 35.4 - Prob. 4ECh. 35.5 - Prob. 1ECh. 35.5 - Prob. 2ECh. 35.5 - Prob. 3ECh. 35.5 - Prob. 4ECh. 35.5 - Prob. 5ECh. 35 - Prob. 1PCh. 35 - Prob. 2PCh. 35 - Prob. 3PCh. 35 - Prob. 4PCh. 35 - Prob. 5PCh. 35 - Prob. 6PCh. 35 - Prob. 7P
Knowledge Booster
Similar questions
- Use R to answer the following question According to the central limit theorem, the sum of n independent identically distributed random variables will start to resemble a normal distribution as n grows large. The mean of the resulting distribution will be n times the mean of the summands, and the variance n times the variance of the summands. Demonstrate this property using Monte Carlo simulation. Over 10,000 trials, take the sum of 100 uniform random variables (with min=0 and max=1). Note: the variance of the uniform distribution with min 0 and max 1 is 1/12. Include: 1. A histogram of the results of the MC simulation 2. A density plot of a normal distribution with the appropriate mean and standard deviation 3. The mean and standard deviation of the MC simulation. ps(plz do not use chatgpt)arrow_forwardAlgorithm A search using the heuristic h(n) = α for some fixed constant α > 0 is guaranteed to find an optimal solution Select one: True Falsearrow_forwardPlease, answer the whole question. Suppose you toss n biased coins independently. Given positive integers n and k, along with a set of non-negative real numbers p1,..., pn in [0, 1], where pi is the probability that the ith coin comes up head, your goal is to compute the probability of obtaining exactly k heads when tossing these n biased coins. Design an O(nk)-time algorithm for this task. Explain the algorithm, write down the pseudo code and do run time analysis.arrow_forward
- Given the following code snippet, find the following:1) Recurrence relation of func (x,y) when x>0 and length of y is n. Express in the form below:T(x,n) = T(?) + O(?)For 2-3, consider worst-case scenario and initial value of y as [1]2) Time complexity of func (x,y)3) Space Complexity and Auxiliary Space Complexity of func (x,y)arrow_forwardGiven the following code snippet, find the following:1) Recurrence relation of func (x,y) when x>0 and length of y is n.T(x,n) = T(_) + O(_) (Just fill in the blanks)For 2-3, consider worst-case scenario and initial value of y as [1]2) Time complexity of func (x,y)3) Auxiliary Space Complexity of func (x,y)arrow_forwardA certain cat shelter has devised a novel way of making prospective adopters choose their new pet. To remove pet owners’ biases regarding breed, age, or looks, they are led blindfolded into a room containing all the cats up for adoption and must bring home whichever they pick up. Suppose you are trying to adopt two cats, and the shelter contains a total of N cats in one of only two colors: black or orange. is it still possible to pick up two black cats with probability ½, given that there is an even number of orange cats in the room? If so, how many cats should be in the room? How many black, how many orange?arrow_forward
- (control variates) Reproduce the class example of estimating int 0 ^ 1 2 dz 1+x by the MC approach using 100 uniform random variables and after that by using a control variate with function g(U) = 1 + U as suggested in class. Compare the results.arrow_forwardFill in the bubbles for ALL correct choices: there may be more than one correct choice, but there is always at least one correct choice. Which of the following are true for the k-nearest neighbor (k-NN) algorithm? A: k-NN can be used for both classification and regression. B: As k increases, the bias usually increases. C: The decision boundary looks smoother with smaller values of k. D: As k increases, the variance usually increases.arrow_forwardWrite a program to find the solution to Maxone problem (You want to maximize the number of ones) using agenetic algorithm where the population size is 2000 and each chromosome is 20 genes long. Use a mutationprobability of 0.02 and a cross-over probability of 0.5. Make separate functions for different components of thegenetic algorithm.arrow_forward
- Under what circumstances might the A* algorithm fail even when using admissible heuristics? Give a clear example.arrow_forwardSubject : calculas Show that: ¬q 1) p→¬q 2) (p∧r)∨s 3) s→(t∨u) 4) ¬t∧¬u where ¬ is denied.arrow_forwardUse rules of inference to show that if ∀x(P (x) ∨ Q(x)) and ∀x((¬P (x) ∧ Q(x)) → R(x)) are true, then ∀x(¬R(x) → P (x)) is also true, where the domains of all quantifiers are the samearrow_forward
arrow_back_ios
SEE MORE QUESTIONS
arrow_forward_ios
Recommended textbooks for you
- Operations Research : Applications and AlgorithmsComputer ScienceISBN:9780534380588Author:Wayne L. WinstonPublisher:Brooks Cole
Operations Research : Applications and Algorithms
Computer Science
ISBN:9780534380588
Author:Wayne L. Winston
Publisher:Brooks Cole