QUESTION3The time complexity of binary search in a sorted array of n elements is:O a. 0(n)Ob.O(n log n)O. O(log n)od on3)QUESTION4In the context of Single Linked List (SLL), which one of the following is NOT correct?O a In general, searching in SLL is slower than searching in arrayso b. The SLL can be traversed in the reverse direction using the backward linksOain SLL, a pointer (reference) head points to the first node in the listod. On average, searching in SLL takes O(n) timeQUE STION5While sorting an array in the ascending order thefirst finds the smallest element in the list and then puts it in the appropriate position.O a Selection sortob. Merge sortc. Bubble sortShell sort

Given questions about Time complexity of binary search in n sorted elements

QUESTION 3 The time complexity of binary search in a sorted array of n elements is: O a. O(n) Ob.O(n log n) O. O(log n) od On3 QUE STION 4 In the context of Single Linked List (SLL), which one of the following is NOT correct? O a In general, searching in SLL is slower than searching in arrays O b. The SLL can be traversed in the reverse direction using the backward links OcIn SLL, a pointer (reference) head points to the first node in the list Od. On average, searching in SLL takes O(n) time QUE STION5 While sorting an array in the ascending order the first finds the smallest element in the list and then puts it in the appropriate position. O a Selection sort ob. Merge sort c. Bubble sort Shell sort

QUESTION 3 The time complexity of binary search in a sorted array of n elements is: O a. O(n) Ob.O(n log n) O. O(log n) od On3 QUE STION 4 In the context of Single Linked List (SLL), which one of the following is NOT correct? O a In general, searching in SLL is slower than searching in arrays O b. The SLL can be traversed in the reverse direction using the backward links OcIn SLL, a pointer (reference) head points to the first node in the list Od. On average, searching in SLL takes O(n) time QUE STION5 While sorting an array in the ascending order the first finds the smallest element in the list and then puts it in the appropriate position. O a Selection sort ob. Merge sort c. Bubble sort Shell sort

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) L is a linear list of data elements. Implement the list as:i) a linear open addressed hash table using an appropriate hash function of yourchoice; andii) an ordered list.Search for a list of keys on the representations i) and ii) using a) hashing andb) binary search, respectively. Compare the performance of the two methods overthe list L.
Please explain Q#Do the time complexities of a singly linked list differ from the time complexities of the operations (update, insert, lookup, delete) of a doubly-linked list? Group of answer choices a. Yes, the Big-O complexities are different for some of the operations. Adding the additional pointer to the previous node in the doubly linked list makes half of the operations slower. b. No, the Big-O complexities do not differ. While a Doubly linked list uses an additional pointer of memory, it does not in fact modify the Big-O run-time. c. Yes, the Big-O complexities are different for all of the operations. Adding the additional pointer to the previous node in the doubly linked list makes all operations slower.
if we compare a hash table performanve with a binary search of a sorted array, the number od comparisons required by a hash table vs the binary search tree is _____ A) O(1) vs O(n) B) O(n) vs O(1) c) O(n^2) vs O(log n) d) O(1) vs O(log n) e) O(n) vs O(n)
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.
Code it. The mergeTwoLists function in my code comes from the problem Merge Two Sorted Listswhose complexity obviously is O(n), n is the sum of length of l1 and l2. To put it simpler, assume the k is 2^x, So the progress of combination is like a full binary tree,from bottom to top. So on every level of tree, the combination complexity is n,because every level have all n numbers without repetition.The level of tree is x, ie log k. So the complexity is O(n log k). for example, 8 ListNode, and the length of every ListNode is x1, x2,x3, x4, x5, x6, x7, x8, total is n. on level 3: x1+x2, x3+x4, x5+x6, x7+x8 sum: n on level 2: x1+x2+x3+x4, x5+x6+x7+x8 sum: n on level 1: x1+x2+x3+x4+x5+x6+x7+x8 sum: n.
illustrates the procedure for Fibonacci search. Here n, thenumber of data elements is such that: i) Fk+1 > (n+1); andii) Fk + m = (n +1) for some m ≥ 0, where Fk+1 and Fk are two consecutiveFibonacci numbers.
QuickSort is run for an array A in a manner that PARTITION consistently produces a 5:1 split for the (sub)arrays to be sorted (recursively) next. Picture the recurrence tree for this scenario ... What will be the length of the longest path from the root to a leaf (= deepest depth)in this tree? Group of answer choices log_5/6 (n) ... "log of n with base 5/6" log_6/5 (n) ... "log of n with base 6/5" log_6 (n) ... "log n with base 6" n^log_5(6) ... "n to the power of log_5(6)"
Consider an unordered list of data pieces L[0:5] = 23, 14, 98, 45, 67, 53. Let's look for the key K = 53. Obviously, the search proceeds along the list, comparing key K with each element until it finds it as the final member in the list. In the instance of looking for the key K = 110, the search proceeds but falls off the list, declaring it failed. Create Algorithm Procedures for both sorted and unordered linear searches, as well as their times.
4) Given that a queue is being represented by the following circular representation in an array (front is currently 4 and back is currently 7): array component: 0 1 2 4 5 6 7 8 | | | | 37 | 41 | 12 | 34 | | a) Show what this representation would like after the following sequence of operations are performed: deq(q, i); deq(q, j); enq(q, 81); enq(q, 72); enq(q, 38); deq(q, k); (indicate the current position of front and back for each operation) b) What is an advantage of this implementation of a queue? c) What is a disadvantage of this implementation of a queue?
Select true or false for the statements below. Explain your answers if you like to receive partial credit 10) Which of the following is true about the linked list functions where the linked list onlycontains a head pointer?a. If the linked list is ordered, you can use binary search and improve theperformance to O(log n)b. The worst case performance of the delete function is in the same Big-Ocategory as the worst case performance of the delete function with anunordered arrayc. If the node you are deleting is at the end of the list, the performance is O(1)
: A binary search tree was created by traversing through an array from left to rightand inserting each element. Given a binary search tree with distinct elements, print all possiblearrays that could have led to this tree.EXAMPLEInput: 1<-2->3Output: {2, 1, 3}, {2, 3, 1}
Please code it. The mergeTwoLists function in my code comes from the problem Merge Two Sorted Listswhose complexity obviously is O(n), n is the sum of length of l1 and l2. To put it simpler, assume the k is 2^x, So the progress of combination is like a full binary tree,from bottom to top. So on every level of tree, the combination complexity is n,because every level have all n numbers without repetition.The level of tree is x, ie log k. So the complexity is O(n log k). for example, 8 ListNode, and the length of every ListNode is x1, x2,x3, x4, x5, x6, x7, x8, total is n. on level 3: x1+x2, x3+x4, x5+x6, x7+x8 sum: n on level 2: x1+x2+x3+x4, x5+x6+x7+x8 sum: n on level 1: x1+x2+x3+x4+x5+x6+x7+x8 sum: n.