Problem 2In this problem, develop an algorithm to search a key in a sorted array.As shown in class, one general way to search a key in an array is to scan the array, comparing each element againstthe search key, until one element matches the key, or no match is found till the end of the array. In the latter case thealgorithm concludes that the key is not in the array.when the array is sorted, we can search in a more efficient way. Assume the array is sorted in ascending order. Theidea is, we retrieve the middle value from the array and compare it against the search key. If the middle value and thesearch key match, we’ve found it and the algorithm stops. If not so and the search key is smaller than the middlevalue, we search the 1st (left) half of the array as the key could not be in the 2nd (right) half (convince yourself!),otherwise -- the search key is larger than the middle value -- we search the 2nd half of the array as the key could notbe in the 1st half of thearray.In the half sub-array, we repeat the above steps, retrieving the middle key of the sub-array, comparing it against thesearch key. If they match, we've found it and stop. If they are not same, then go to either first half or second half ofthe subarray based on the comparison result.The search stops either when a match is found, or there is no subarray to search. In the latter case the algorithmconcludes that the key is not in the array. Following figure shows the steps of searching 38 in a sorted array.0 1 2 34 57 8 910Search 380205081216233856729197|L= 0 1 23M= 57 8H = 1038 > 23take 2nd Half02050812.162338567291971st Half2nd Half5 61234M= 8H = 10%3D38 < 72take 1st half02050812 16233856729197Discarded1st Half2nd HalfL=6M = 6230 1 234 5H = 78 910Found 38,Return 6050208121638567291974. Develop flowchart for this algorithm,The algorithm takes as input a search key and an array, output “key in ar: YES" or "key in arr: NO"Hint: use loop and two variables, one to store the starting index of the current search subarray andanother to store the end of the current search subarray. These variables are often called start, end, low,high, etc. Let's call it L and H. In each iteration we search array from index L to H. Initially L is Oand H is the last index of the original array. In each iteration of the loop, we get middle index M andmiddle value. If the search key is smaller than the middle values, search 1st half of the array by settingH to be M-1 (L does not change). If need to search 2nd half, set L to be M+1 (H does not change).If the search (sub)array is of even size, the middle index can be either the end of the 1st half or thebeginning of the 2nd half (so to be precise, the "middle" element should be called "middle-most"element).The loop should stop immediately when the key is found.What is the terminating condition of the search loop?Must use loops. You should not use recursions. (We will learn recursion later).Start your flowchart as shown in the following figure. The algorithm takes as input an array arr and asearch key keySave the flowchart as img_02.jpgstartarr[]keyImplement the algorithm using JavaScriptComplete the function problem20, and attach the onclick event to the corresponding button in htmlYour implementation should match your flowchart.

According to our guidelines we solve first one: ================================== Here i make…

Develop flowchart for this algorithm, The algorithm takes as input a search key and an array, output "key in arr: YES" or "key in arr: NO" Hint: use loop and two variables, one to store the starting index of the current search subarray and another to store the end of the current search subarray. These variables are often called start, end, low, high, etc. Let's call it L and H. In each iteration we search array from index L to H. Initially L is0 and H is the last index of the original array. In each iteration of the loop, we get middle index M and middle value. If the search key is smaller than the middle values, search 1st half of the array by setting

Develop flowchart for this algorithm, The algorithm takes as input a search key and an array, output "key in arr: YES" or "key in arr: NO" Hint: use loop and two variables, one to store the starting index of the current search subarray and another to store the end of the current search subarray. These variables are often called start, end, low, high, etc. Let's call it L and H. In each iteration we search array from index L to H. Initially L is0 and H is the last index of the original array. In each iteration of the loop, we get middle index M and middle value. If the search key is smaller than the middle values, search 1st half of the array by setting

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

See similar textbooks

Similar questions

1. (a) Suppose you're given the array below, and you're searching for the integer 17 using a linear search algorithm as discussed in class. Which elements of the array are inspected in the process of implementing the linear search, and in what order are they inspected? Explain thoroughly but succinctly. 1 2 4 9 10 11 14 18 19 20 23
A unimodal array is an array that has a sequence of monotonically increasing integers followed by a sequence of monotonically decreasing integers, assuming all elements in the array are unique. Example: А- (4, 5, 8, 9, 10, 11, 7, 3, 2, 1}: A is a unimodal array because there is an increasing sequence followed by a decreasing sequence and the maximum element is 11. B= {11, 9, 8, 7, 5, 4, 3, 2, 1}: B is not a unimodal array because there is no increasing sequence It is simply a decreasing sequence and the maximum element is 11. C= {1, 2, 3, 4, 5, 7, 8, 9, 11}: C is not a unimodal array because There is an increasing sequence, but there is no decreasing sequence and the maximum element is 11. Design an efficient algorithm with the lowest possible complexity to state whether a given array is unimodal or not, and explain why your algorithm is efficient. Analyze the complexity of your algorithm.
Let A and B be two integers valued arrays of sizes n1 & n2 respectively. The elements of the two arrays are sorted in increasing order and may contain duplicate elements.It is required to form a list, C, of distinct elements, in increasing order, that are in A but not in B and in B but not in A. (There shall be no duplicate elements in list C.)The two arrays, A & B, can only be traversed once.Example:A: -2, 2, 4, 4, 4, 7, 9, 9, 9, 12, 15,B: 1, 2, 5, 9, 15, 15, 15, 17, 17, 17C: -2, 1, 4, 5, 7, 12, 17
Given an integer array nums, return true if any value appears at least twice in the array, and return false if every element is distinct. 1. i. Description of the problem ii. Algorithm of the following codes (both methods) iii. Analyze the running time of each step of the algorithms (both methods) Method 1: class Solution {public:bool containsDuplicate(vector<int>& nums) { int n= nums.size();sort(begin(nums), end(nums)); for (int i=1; i<n; i++){ if (nums[i] == nums[i-1]){return true;}}return false;}}; Method 2: class Solution {public:bool containsDuplicate(vector<int>& nums) { int n = nums.size();set<int> s; for (int i=0; i<n; i++){if (s.count(nums[i])){return true;}s.insert(nums[i]);}return false;}};
OBJECT Lab Session 03 Application of the Linear and binary search on a list of elements stored in an array. THEORY Linear Search A nonempty array DATA with N numerical values is given. This algorithm finds the location LOC and required value of elements of DATA. The variable K is used as a counter. ALGORITHM 1. Set k := 1 & loc : = 0 Repeat step 3 & 4 while loc : = 0 &k < = n If (item = data[k]) loc : = k Else K = k + 1 If loc > = 0 or If (item = data[k]) then Print “loc is the location of item” Else Print “no. not found” Exit Binary Search Suppose DATA is an array that is sorted in increasing (or decreasing) numerical order or, equivalently, alphabetically. Then there is an extremely efficient searching algorithm, called binary search, which can be used to find the location LOC of a given ITEM of information in DATA. The binary search algorithm applied to our array DATA works as follows: During each stage of our algorithm, our search for ITEM is…
OBJECT Lab Session 03 Application of the Linear and binary search on a list of elements stored in an array. THEORY Linear Search A nonempty array DATA with N numerical values is given. This algorithm finds the location LOC and required value of elements of DATA. The variable K is used as a counter. ALGORITHM 1. Set k := 1 & loc : = 0 Repeat step 3 & 4 while loc : = 0 &k < = n If (item = data[k]) loc : = k Else K = k + 1 If loc > = 0 or If (item = data[k]) then Print “loc is the location of item” Else Print “no. not found” Exit Binary Search Suppose DATA is an array that is sorted in increasing (or decreasing) numerical order or, equivalently, alphabetically. Then there is an extremely efficient searching algorithm, called binary search, which can be used to find the location LOC of a given ITEM of information in DATA. The binary search algorithm applied to our array DATA works as follows: During each stage of our algorithm, our search for ITEM is…
Problem 2: Search an Array Considering the search problem, we have a list of n integers 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 otherwise, when not found. a) Implement the algo hm in Python b Empirically, show the performance curve of the algorithm using time measurements. c Using the basics of the theoretical analysis, write the complexity of its worst-case time. d) Prove your algorithm is correct-it terminates with the valid output in every case
A unimodal array is an array that has a sequence of monotonically increasing integers followed by a sequence of monotonically decreasing integers, assuming all elements in the array are unique. Example: |- A= {4, 5, 8, 9, 10, 11, 7, 3, 2, 1}: A is a unimodal array because there is an increasing sequence followed by a decreasing sequence and the maximum element is 11. B= {11, 9, 8, 7, 5, 4, 3, 2, 1}: B is not a unimodal array because there is no increasing sequence It is simply a decreasing sequence and the maximum element is 11. C= {1, 2, 3, 4, 5, 7, 8, 9, 11: C is not a unimodal array because There is an increasing sequence, but there is no decreasing sequence and the maximum element is 11. a) Design an efficient algorithm with the lowest possible complexity to state whether a given array is unimodal or not, and explain why your algorithm is efficient. b) Analyze the complexity of your algorithm.
Short answer (5 points): The insert, update, delete, and search functions discussed in the slides are designed such that there are no holes assumed to be in the unsorted array. Your friend thinks that they can be coded to be equally as good in the worst case situations even if there are holes while still only using just the array and a “currentSize” variable. Is he right? Address this by doing/answering the following: Describe how the search algorithm changes if there are holes in the array. What is its Big O() and is it significantly worse, better, or the same as the no hole version? Describe how the update algorithm changes (if any) if there are holes in the array. What is its Big O() and is it significantly worse, better, or the same as the no hole version? Describe how the delete algorithm changes since it no longer needs to “fill the hole”. What is its Big O() and is it significantly worse, better, or the same as the no hole version? Now describe an insert algorithm that has…
Exercise 1:In this problem, we would like to implement a variation of the Bubble Sort algorithm. The algorithm differs from a bubble sort in that it sorts in both directions on each pass through the list. The algorithm is illustrated as in the following figure: For the first step, we perform bubble sort from the index 1 to n (n is thenumber of elements in the array). The next step, we perform a reserved bubble sort from the index n to 1. The process is repeated until all the array is sorted. Propose a pseudo-code to complete the Bubble Sort algorithm. Implement and test this algorithm in C/C++. Analyze and compute the complexity of this algorithm in the best, average and worst scenarios.Exercise 2:Re-implement Exercise 1 using a linear data structure: List, Stack, Queue. Justify your choice of data structure.
16. Given an n-element array with n > 100, is it possible to find an algorithm that can find an element that is neither the maximum nor the minimum with fewer than 10 comparisons? (a) yes (b) no
Question 2 You are doing the Tour de France and you are given a map with all the n places where you can refill your water bottles. Your bottles fit 2 liters of water with which are enough for m kilometres. Your goal is to stop as few times as possible to refill your bottles, since you care about a good time for the Tour de France. Design an efficient algorithm using dynamic programming, prove its correctness and analysis its run time. Give the one-dimensional array A. And the time complexity of filling your array