Java Question 3: What is the complexity of the binary search algorithm under the worst-case scenario? For the purpose of this item, the ^ symbol is used todenote exponentiation (i.e. "raised to"). *O(log n)O(n*3)O(n*2)O o(2*n)O(n)O(1)

Binary search is the search technique which works efficiently on the sorted lists. Binary search…

Answered: Java Question 3:

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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Consider the following algorithm that uses a list of n elements (alist). What is the worst case runtime of this algorithm? for each element in alist 1. ask the user for an input, call it value 2. search value in alist using the linear search algorithm 3. if value exists in alist, print "successful" otherwise print "unsuccessful" Question 9 options: O(n log n) O(n) O(2^n) O(1) O(log n) O(n^2)
Given a sorted array of n comparable items A, and a search value key, return the position (array index) of key in A if it is present, or -1 if it is not present. If key is present in A, your algorithm must run in order O(log k) time, where k is the location of key in A. Otherwise, if key is not present, your algorithm must run in O(log n) time.
Suppose we are to sort a set of 'n' integers using an algorithm with time complexity O (log2 n) that takes 0.5 ms (milliseconds) in the worst case when n = 20,000. How large an element can the algorithm then handle in 1 ms? (Note that only numeric answers can be provided.)
Consider the following algorithm that uses a sorted list of n elements (alist). What is the worst case runtime of this algorithm? for each element in alist 1. ask the user for an input, call it value 2. search value in alist using the binary search algorithm 3. if value exists in alist, print "successful" otherwise print "unsuccessful" Question 10 options: O(2^n) O(n log n) O(log n) O(n^2) O(n) O(1)
For the 8-queens problem, define a heuristic function, design a Best First Search algorithm in which the search process is guided by f(n) = g(n) + h(n), where g(n) is the depth of node n and h(n) is the heuristic function you define, and give the pseudo code description.
A fancy search algorithm is shown below. Suppose a key k is present in the array a, which contains unique numbers. What is the running time of this algorithm in expectation?
What is the best case time complexity of the binary search algorithm? A. O(1) B. O(n) C. O(log2n) D. O(n^2)
Suppose T(n) is the time to execute an algorithm with input size n. Which of the following algorithms with the T(n) show is not good to be solved by divide-and-conquer? (Choose the best answer) A. T(n) = 2T(n/2) + 2nB. T(n) = 10T(n/10) + 10nC. T(n) = 2T(n/2) + 2n2D. T(n) = 2T(n/2) + 100n10E. T(n) = T(n-1) + 2T(n-2)
Which one is the true statement? -A hash table can be used to make an algorithm run faster even in the worst case by trading space for time. -An iterative improvement algorithm starts with a sub-optimal feasible solution and seeks to improve it in each iteration until reaching a optimal feasible solution.
Create an algorithm that demonstrates the Fibonacci search procedure. The number of data elements n is chosen in such a way that: I Fk+1 > (n+1); and ii) Fk + m = (n +1) for some m 0, where Fk+1 and Fk are two consecutive Fibonacci numbers.
Considering the search problem, we have a list of ?n integers ?=⟨?1,?2,⋯??⟩A=⟨v1,v2,⋯vn⟩. We want to design an algorithm to check whether an item ?v exists or not such that it should return either the index, ?i, if it was found or −1−1 otherwise, when not found.
Assume that, in the worst case, an algorithm takes 1 second to process 100 elements. How long will it take to process 2,000 elements if the growth rate of the algorithm is? (a) O(N) (b) O(N2)

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