Introduction to Algorithms
3rd Edition
ISBN: 9780262033848
Author: Thomas H. Cormen, Ronald L. Rivest, Charles E. Leiserson, Clifford Stein
Publisher: MIT Press
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Chapter 9, Problem 3P
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Program Plan Intro
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(b)
Program Plan Intro
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(c)
Program Plan Intro
To show that
(d)
Program Plan Intro
To show that
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Justify each answer.
Write pseudocode for an algorithm that interchanges the values of the variables m and n, using only assignments (assigning values to variables).
Arrange the functions (1.5)n, n100, (log2n)3, 10n, n!, and n99 so that each function is big-O of the next function.
Give a big-O estimate for the following algorithm:
count = array of k + 1 zeros
for x in input do
count[key(x)] + = 1
end for
total = 0
for i in 0,1,...k do
count[i], total = total, count[i] + total
end for
output = array of the same length as input
for x in input do
output[count[key(x)]] = x
count[key(x)] + = 1
end for return output
Consider a variation of the Bucket Sort algorithm which uses an unknown number of buckets. We're given that the worst-case
runtime of the algorithm is O(n.), Let the number of buckets be n* (n to the power of x). Fill in the following box with the proper
numeric value of x such that the algorithm has the given runtime. For example, you could fill in 0, 1, 2, 3, 3.14 4.2, etc.
Recall that we use Insertion Sort to sort each bucket.
Answer:
Write an algorithm to find the “Peak". From a list of numbers, if a number is
not smaller than its left and right is called "Peak". Usually it will take O(n),
where “n" is the number of elements in the list. Prove that, your algorithm
finds the answer within O(log, n) in worse-case.
Example:
Input
1 2 3 4 5
10 7 5 3 1
21 5 17 11 39
Output
10
17
Chapter 9 Solutions
Introduction to Algorithms
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- Suppose that I searched for a number x in a sorted list of n items by comparing against the 5th item, then the 10th, then the 15th, etc. until I found an item bigger than x, and then I searched backwards from that point. Which expression best describes the approximate running time of this algorithm:arrow_forwardConsider a divide-and-conquer algorithm that calculates the sum of all elements in a set of n numbers by dividing the set into two sets of n/2 numbers each, finding the sum of each of the two subsets recursively, and then adding the result. What is the recurrence relation for the number of operations required for this algorithm? Answer is f(n) = 2 f(n/2) + 1. Please show why this is the case.arrow_forwardImplement a quicksort with a sample size of 2k 1. Sort the sample first, then have the recursive procedure partition on the sample's median and shift the two halves of the rest of the sample to each subarray so that they may be utilised in the subarrays without having to be sorted again. This algorithm is known as samplesort. Put into practise a quicksort based on a 2k sample. The sample should be sorted first, after which you should set up the recursive procedure to split the sample based on its median and to shift the two halves of the remaining sample to each subarray so they can be utilised in the subarrays without needing to be sorted again. The name of this algorithm is samplesort.arrow_forward
- Using dynamic programming, it is often possible to change an iteration overpermutations into an iteration over subsets1. The benefit of this is that n!, thenumber of permutations, is much larger than 2n, the number of subsets. Forexample, if n = 20, then n! ≈ 2.4·1018 and 2n ≈ 106. Thus, for certain values of n,we can efficiently go through the subsets but not through the permutations. As an example, consider the following problem: There is an elevator withmaximum weight x, and n people with known weights who want to get from theground floor to the top floor. What is the minimum number of rides needed if thepeople enter the elevator in an optimal order?arrow_forwardSuppose that, in a divide-and-conquer algorithm, we always divide an instance of size n of a problem into 10 sub-instances of size n=3, and the dividing and combining steps take a time in O(n2). Write a recurrence equation for the running time T (n), and solve the equation for T (n).arrow_forwardLet’s assume that you want to find both the minimum and maximum of m numbers. Design an algorithm that achieves the goal in [3n/2]−2 comparisons in the worst case (you can write a pseudo code or a paragraph,don’t write code). Explain it.arrow_forward
- Create a simple matching algorithm for a round robin competition. in which the number of participants is n (1 n) and the round index is r (0 r 2 (n 1)/2.out: When i = 0,..., n/2 1, a sequence R of n player indices signaling the match pairings between players R2i and R2i+1; if n is odd, Rn1 denoting the resting playerarrow_forwardDescribe a divide and conquer algorithm approach that will compute the number of times a specific number appears in a sequence of numbers. Example {3,4,5,4,3,5,4,4,3} want to know the number of 5s - returns 2. Show all the steps on how your algorithm would work on the example numbers.arrow_forwardImplement Queens algorithm: pre-cond: C = 1, c1, 2, c2, ... , r, cr places the jth queen in the jth row and the cjth column. The remaining rows have no queen.post-cond: Returned if possible is a placement optSol of the n queens consistent with this initial placement of the first r queens. A placement is legal if no two queens can capture each other. Whether this is possible is flagged with optCost equal to one or zero.arrow_forward
- Write a Brute force algorithm to find all the common elements in two lists of integer numbers. (e.g., the output for the lists [1, 3, 4, 7] and [1, 2, 3, 4, 5, 6] should be 1, 3, 4). Show the time complexity of the algorithm if the lengths of the two given lists are m and n, respectively.arrow_forwardWrite Iterative Algorithms and Loop InvariantsforMax(a, b, c)PreCond: Input has 3 numbers.arrow_forwardGiven an integer composed input list A = <a1, a2, … , an> and a target integer z, return the largest number of elements of A that will sum to z. Only the number of elements need be returned and not the list of those specific elements that were used in the summation. Detail an efficient algorithm if it exists or explain why it doesn’t.arrow_forward
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