EBK DATA STRUCTURES AND ALGORITHMS IN C
4th Edition
ISBN: 9781285415017
Author: DROZDEK
Publisher: YUZU
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Develop a merge implementation that reduces the extraspace requirement to max(M, N/M), based on the following idea: Divide the array intoN/M blocks of size M (for simplicity in this description, assume that N is a multipleof M). Then, (i) considering the blocks as items with their first key as the sort key, sortthem using selection sort; and (ii) run through the array merging the first block withthe second, then the second block with the third, and so forth.
Write a bottom-up mergesort that makes use of the array's order by carrying out the following steps each time it needs to locate two arrays to merge: locate the first element in an array that is smaller than its predecessor, then locate the next, and finally merge them to form a sorted subarray. Consider the array size and the number of maximal ascending sequences in the array while analysing the running time of this method.
Write a version of bottom-up mergesort that takes advantage of order in the array by proceeding as follows each time it needs to find two arrays to merge: find a sorted subarray (by incrementing a pointer until finding an entry thatis smaller than its predecessor in the array), then find the next, then merge them. Analyze the running time of this algorithm in terms of the array size and the number of maximal increasing sequences in the array.
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EBK DATA STRUCTURES AND ALGORITHMS IN C
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- Let us assume that we have an application that performs many retrievals on a table, but few insertions and deletions. We do not know the size of the table. Which implementation is more appropriate for this application? a sorted array-based implementation an unsorted array-based implementation a binary search tree implementationarrow_forwardWrite a version of bottom-up mergesort that takes advantage of order in the array by proceeding as follows each time it needs to find two arrays to merge: find a sorted subarray (by incrementing a pointer until finding an entry that is smaller than its predecessor in the array), then find the next, then merge them. Analyze the running time of this algorithm in terms of the array size and the number of maximal increasing sequences in the array.arrow_forwardDesign and implement an insertSorted() method for the MyArrayList class. The method should receive a single object and insert it in its correct position. You can assume that the calling array is already in a sorted order. The calling array is therefore updated.arrow_forward
- Consider the array L = 387, 690, 234 435 567 123 441 as an example. The number of components in this case is 7, the number of numbers is 3, and the radix is 10. This suggests that radix sort would require 10 bins and 3 cycles to complete the sorting. shows how the radix order is followed by the list. Each key is probably thrown into the garbage bin facing down. Each bin is turned into a key when the output to the is to be attached to the phrase: at the end of the bin.arrow_forwardConsider an Array arr= {2, 3, 4, 1, 5}, what are the pivots that are returned as a subsequent partitioning: 1 and 2 1 and 6 2 and 6 1 and 3arrow_forwardimport java.util.Scanner;import java.util.ArrayList; public class UserIDSorting {// TODO: Write the partitioning algorithm - pick the middle element as the // pivot, compare the values using two index variables l and h (low and high), // initialized to the left and right sides of the current elements being sorted,// and determine if a swap is necessarypublic static int partition(ArrayList<String> userIDs, int i, int k) {} // TODO: Write the quicksort algorithm that recursively sorts the low and // high partitionspublic static void quicksort(ArrayList<String> userIDs, int i, int k) {} public static void main(String[] args) {Scanner scnr = new Scanner(System.in); ArrayList<String> userIDList = new ArrayList<String>(); String userID; userID = scnr.next();while (!userID.equals("-1")) {userIDList.add(userID);userID = scnr.next();}// Initial call to quicksort quicksort(userIDList, 0, userIDList.size() - 1); for (int i = 0; i < userIDList.size(); ++i)…arrow_forward
- A hash table is an efficient data structure to store and access data via keys. In this problem, you need to find the longest subarray of distinct elements. For example, given an array A = <milk, water, 6724, water, soda, beer, apple, grape, wine>, the longest subarray is <milk, water, 6724, soda, beer, apple, grape, wine> Hint: you need to keep track and update the positions of the elements. a) Explain how many unique subarrays are there from a given array b) Describe your ideas on how to solve the problem, especially how hash tables can be used to keep track of the important information c) Write the most efficient algorithm (pseudo code) to solve this problem with complexity analysis. You can assume that a good hash function is given.arrow_forwardModify the ArrayDeque implementation so that it does not require the modulus (%) operator. The modulus operator is "expensive" on some systems. Instead, the implementation should make use of the fact that if a.length is a power of 2, then k % a.length = k & (a.length - 1). Here, & is the bitwise and operator. (p 61) Open Data Structures in Java, section 2.7arrow_forwardDevelop and implement a version of mergesort that does not rearrange the array, but returns an int[] array perm such that perm[i] is the index of the i th smallest entry in the array.arrow_forward
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