2.6 ORDER STATISTICS A problem closely related to sorting is that of selecting the kth smallest ele- ment in a sequence of n elements.† One obvious solution is to sort the sequence into nondecreasing order and then locate the kth element. As we have seen, this would require n log n comparisons. By a careful application of the divide-and-conquer strategy, we can find the kth smallest element in O(n) steps. An important special case occurs when k = [n/2], in which case we are finding the median of a sequence in linear time. Algorithm 3.6. Finding the kth smallest element. Input. A sequence S of n elements drawn from a linearly ordered set and an integer k, 1 ≤ k ≤ n. Output. The kth smallest element in S. Method. We use the recursive procedure SELECT in Fig. 3.10. ☐ Let us examine Algorithm 3.6 intuitively to see why it works. The basic idea is to partition the given sequence about some element m into three subsequences S₁, S., S₁ such that S, contains all elements less than m, S, all Strictly speaking, the kth smallest of a sequence a₁. da, is an element b in the sequence such that there are at most k - 1 values for i for which a,

2.6 ORDER STATISTICS A problem closely related to sorting is that of selecting the kth smallest ele- ment in a sequence of n elements.† One obvious solution is to sort the sequence into nondecreasing order and then locate the kth element. As we have seen, this would require n log n comparisons. By a careful application of the divide-and-conquer strategy, we can find the kth smallest element in O(n) steps. An important special case occurs when k = [n/2], in which case we are finding the median of a sequence in linear time. Algorithm 3.6. Finding the kth smallest element. Input. A sequence S of n elements drawn from a linearly ordered set and an integer k, 1 ≤ k ≤ n. Output. The kth smallest element in S. Method. We use the recursive procedure SELECT in Fig. 3.10. ☐ Let us examine Algorithm 3.6 intuitively to see why it works. The basic idea is to partition the given sequence about some element m into three subsequences S₁, S., S₁ such that S, contains all elements less than m, S, all Strictly speaking, the kth smallest of a sequence a₁. da, is an element b in the sequence such that there are at most k - 1 values for i for which a,

Database System Concepts

7th Edition

ISBN:9780078022159

Author:Abraham Silberschatz Professor, Henry F. Korth, S. Sudarshan

Publisher:Abraham Silberschatz Professor, Henry F. Korth, S. Sudarshan

Chapter1: Introduction

Section: Chapter Questions

Problem 1PE

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3. Binary search. (a) Suppose we want to check if a sorted sequence A contains an element v. For this, we can use Binary Search. Binary Search compares the value at the midpoint of the sequence A with v and eliminates half of the sequence from further consideration. The Binary Search algorithm repeats this procedure, halving the size of the remaining portion of the sequence each time. Write a recurrence for the running time of Binary search and solve this recurrence. (b) Ternary Search is a generalization of Binary Search that can be used to find an element in an array. It divides the array with n elements into three parts and determines, with two comparisons, which part may contain the value we are searching for. For instance, initially, the array is divided into three thirds by taking mid1 = ["¹] and mid2 = [²(n-¹)]. Write a recurrence for the running time of Ternary search and solve this recurrence.
Correct answer will be upvoted else Multiple Downvoted. Don't submit random answer. Computer science. Ridbit begins with an integer n. In one action, he can perform one of the accompanying tasks: partition n by one of its appropriate divisors, or take away 1 from n in case n is more prominent than 1. An appropriate divisor is a divisor of a number, barring itself. For instance, 1, 2, 4, 5, and 10 are appropriate divisors of 20, however 20 itself isn't. What is the base number of moves Ridbit is needed to make to decrease n to 1? Input The principal line contains a solitary integer t (1≤t≤1000) — the number of experiments. The main line of each experiment contains a solitary integer n (1≤n≤109). Output For each experiment, output the base number of moves needed to lessen n to 1.
1. Consider a problem of computing the prefix average of a sequence of numbers stored in an array P consisting of P integers. We want to compute an array P such that P is the average of P for P . Consider the two algorithms related to the above problem. Answer the questions given below
Coding in Python, we have the function to perform this task, V col_Hdeg whichinputs the adjacency matrix A of the graph and initializes the array vert_color s tohold vertex colors. It then calculates the degree of each vertex and stores it in thearray degrees.The while loop iterates until all vertices are colored and at each iteration, thevertex v with the highest degree is colored. The array temp is used to find the currentcolors of the neighbors of vertex v and an unused smallest color is determined aftersorting this array.
Case Study 1 Problem: Merge Sort Merge Sort follows the rule of Divide and Conquer to sort a given set of numbers/elements, recursively, hence consuming less time. Merge sort runs in O(n*log n) time in all the cases. Two functions are involved in this algorithm. The merge() function is used for the merging two halves and the mergesort() function recursively calls itself to divide the array until the size becomes one. Use the array below to perform a Merge Sort. Show all necessary workings. {75, 22, 65, 97, 35, 56, 18, 89} Case Study 2 Problem: Hashing Hashing is a technique to convert a range of key values into a range of indexes of an array. Load Factor is a measure of how full the hash table is allowed to get before its capacity is automatically increased which may cause a collision. When collision occurs, there are two simple solutions: Chaining and Linear Probe. In what order could the elements have been added using the output below and given the following hash table implemented…
Number of times you need to assign a value (let say -1) to all elements of a 4-demnsional array A[k, l, m, n] (to initialize it) can be expressed mathematically as product of sets cardinalities Write a pseudo-code that does this assignment.
“A” array has a members and “B” array has b members which are sorted. I need an algorithm to find the k-th member in a new array which is a mix of “A” and “B”. The runtime must be O(log(a+b))
7.Code for----- an array of meeting time intervals consisting ofstart and end times [[s1,e1],[s2,e2],...] (si < ei),determine if a person could attend all meetings. For example,Given [[0, 30],[5, 10],[15, 20]],return false.""".
The recursive algorithm below takes as input an array A of distinct integers, indexed between s andf, and an integer k. The algorithm returns the index of the integer k in the array A, or ?1 if the integerk is not contained within A. Complete the missing portion of the algorithm in such a way that you makethree recursive calls to subarrays of approximately one third the size of A.• Write and justify a recurrence for the runtime T(n) of the above algorithm.• Use the recursion tree to show that the algorithm runs in time O(n).FindK(A,s,f,k)if s < fif f = s + 1if k = A[s] return sif k = A[f] return felseq1 = b(2s + f)=3cq2 = b(q1 + 1 + f)=2c... to be continued.else... to be continued.
7. How many non-empty subsequences does a string of length n have? For example, for the sequence represented by the array [1,3,6,9], [1,3] is a subsequence, and [1,6] is a subsequence, but [6,1] is not. Notice that the order of the elements in a sequence must be preserved in any subsequence.
Question 2: Recurrences (a) Below is the pseudo-code for two algorithms: Practicel (A,s,f) and Practice2(A,s,f), which take as input a sorted array A, indexed from s to f. The algorithms make a call to Bsearch(A,s,f,k) which we saw in class. Determine the worst-case runtime recurrece for each algorithm: T1(n) and T2(n). Show that T(n) is O((log n)²) and T2(n) is O(n log n). Practicel (A,s,f) if s< f ql = L(s+ f)/2] if BSearch(A,s,ql,1) = true Practice2(A,s,f) if s < f if BSearch(A,s+1,f,1) = true return true return true else else return Practice2(A, s, f-1) return Practicel(A, ql+1, f) else else return false return false
Question 2: Recurrences (a) Below is the pseudo-code for two algorithms: Practicel1(A,s,f) and Practice2(A,s,f), which take as input a sorted array A, indexed from s to f. The algorithms make a call to Bsearch(A,s,f,k) which we saw in class. Determine the worst-case runtime recurrence for each algorithm: T1(n) and T2(n). Show that T1(n) is O((log n)²) and T2(n) is 0(n log n). Practicel (A,s,f) if s < f ql = [(s + f)/2] if BSearch(A,s,q1,1) = true Practice2(A,s,f) if s