Function Description Complete the function getMergedintervals in the editor below. getMergedintervals has the following parameter(s): int intervals[[n][2]]: the time intervals Returns intO[2]: the merged intervals in sorted order Constraints 1sns 105 • 1s intervals[ij[2] < 10° • intervals[i][0] s intervals[i][1] for all i. v Input Format For Custom Testing The first line contains an integer, n, size of intervals]. The second line contains the integer 2, the size of intervals[i). Each line i of the n subsequent lines (where 0si

Function Description Complete the function getMergedintervals in the editor below. getMergedintervals has the following parameter(s): int intervals[[n][2]]: the time intervals Returns intO[2]: the merged intervals in sorted order Constraints 1sns 105 • 1s intervals[ij[2] < 10° • intervals[i][0] s intervals[i][1] for all i. v Input Format For Custom Testing The first line contains an integer, n, size of intervals]. The second line contains the integer 2, the size of intervals[i). Each line i of the n subsequent lines (where 0si

C++ Programming: From Problem Analysis to Program Design

8th Edition

ISBN:9781337102087

Author:D. S. Malik

Publisher:D. S. Malik

Chapter5: Control Structures Ii (repetition)

Section: Chapter Questions

Problem 7PE

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We are given a collection of n key-value pairs (k,m) in which k is a positive integer number in arange [0,N-1] for small enough N. Assume that we may have multiple values for the same keys.a) if m is a positive number in a range [0,M-1] for some small enough integer M, design alookup table for storing these items such that the time complexity of search, insert and deleteare constant. b) if m is an arbitrary integer number, design a lookup table for storing these itemssuch that the time complexity of search is log n.
Note: Your solution should have O(n) time complexity, where n is the number of elements in l, and O(1) additional space complexity, since this is what you would be asked to accomplish in an interview. Given a linked list l, reverse its nodes k at a time and return the modified list. k is a positive integer that is less than or equal to the length of l. If the number of nodes in the linked list is not a multiple of k, then the nodes that are left out at the end should remain as-is. You may not alter the values in the nodes - only the nodes themselves can be changed.
#. Consider a hash table of size 11 with hash function h(x) = x mod 11.Draw both hash tables (Closed-Bucket and Open-Bucket) that results after inserting, in the given order, the following values: 45, 12, 35, 67, 25, 52, 88, 70, 68, 53, 55
Consider 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.
Consider an initially empty hash table, with an initial size of two buckets, and a load factor threshold of 0.725. After inserting the values 27, 13, 11, 47, 35, and 19 in that order, using big-oh notation, give the average-case time to search for a number between 10 and 50, and the worst-case for the same search. You may assume that the size of the hash table is doubled when necessary. public int hashCode( int k ) { int h = k * 3 - 2; return h % this.size(); }
Consider a list of data components L[0:5] = 23, 14, 98, 45, 67, and 53. Let's look for the K = 53 key. Naturally, the search moves down the list, comparing key K with each element until it discovers it as the final element in the list. When looking for the key K = 110, the search moves forward but eventually drops off the list, making it a failed search. Write the algorithmic steps for both sorted and unordered linear searches, along with the times involved.
Consider the following variation of mergesort:(a) If n ≤ 1, we are done.(b) Divide the n elements into b subarrays of n/b elements each.(c) Recursively call mergesort to sort each of the b subarrays.(d) Merge the b sorted subarrays.For example, the standard mergesort has b = 2, and merging takes Θ(n) comparisons.If b = 3, then we have a 3-way mergesort, and one can show that it takes Θ(n) comparisons to merge 3 sorted lists. In fact, one may show that merging b sorted lists (for any fixed b) can be done in Θ(n) comparisons.Now consider the following argument: if we set b = n, we recursively sort n subarrays of size 1, and then merge them into one list with Θ(n) comparisons. Thus, the complexitycan be expressed by:T(1) = 0T(n) = nT(1) + Θ(n) = Θ(n)Therefore, this variation of mergesort sorts an array in Θ(n) comparisons.What is wrong with this argument? Hint: think about how to implement this variation.
Implement a commonly used hash table in a program that handles collision using linear probing. Using (K mod 13) as the hash function, store the following elements in the table: {1, 5, 21, 26, 39, 14, 15, 16, 17, 18, 19, 20, 111, 145, 146}.
Is it always the case that a hash table of size m contains exactly m linked lists? I've been trying to understand the value of hash functions without success. Throw in a real-world case study to back up your claims.
3-way intersectionsort. Instead of splitting in half at each stage, imagine dividing into thirds, sorting each third, and combining with a three-way merge. What is the rate of increase of this algorithm's total running time?
Implement a commonly used hash table in a program that handles collision using linear probing. Using (K mod 13) as the hash function, store the following elements in the table: {1, 5, 21, 26, 39, 14, 15, 16, 17, 18, 19, 20, 111, 145, 146}. Use c++
) Implement a quicksort based on using a sample of size 2k 1. First, sort the sample, then arrange to have the recursiveroutine partition on the median of the sample and to move the two halves of the rest ofthe sample to each subarray, such that they can be used in the subarrays, without havingto be sorted again. This algorithm is called samplesort.