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Using Big-0 notation, live the complexity of the following fragments assuming the problem size is represented by n in each case. d. for (int i = 0; i < n, i++) { // 10 lines of code } } for (int j = 0; j

Using Big-0 notation, live the complexity of the following fragments assuming the problem size is represented by n in each case. d. for (int i = 0; i < n, i++) { // 10 lines of code } } for (int j = 0; j

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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Concept explainers

Computational Systems

The computation systems can be defined as the systems that are capable of solving a problem that includes calculations either mathematical or logical, and are able to produce the result as an output. For example, a simple calculator that is given a set of numbers and operators as input and produces the result as an output can be defined as a computational system. The above example is a simple illustration only, the computational systems range from simple devices to complex devices such as the systems that control the SpaceX companies, Starlink satellites which are being quite recently seen in the skies at many places globally.

Question

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Now suppose that you are given two unsorted arrays A and B, containing m and n elements respectively. You are also given a black box O(n) selection algorithm SEL that can be called on A and B separately and, in addition, a small number (say, ten) of extra memory words that can be used to store some extra data. Using SEL as a subroutine, design an O(n + m) algorithm for finding the kth smallest element in the combined set of O(m + n) elements. You may not merge the two arrays in another array and call an algorithm (either SEL or something you wrote) on this third array. This is because there is no extra memory available to build this third array.
Get the time complexity function from the pseudocode for the addition of the 2 matrices below, and prove whether the big-oh is O(n^2) so that it satisfies the rule f(n) <= c g(n) for(i = 0 ; i < rows; i++){for(j = 0; j < columns; j++)matrix2[i][j] = matrix1[i][j] + matrix2[i][j];} for(i = 0 ; i < rows; i++){for(j = 0; j < columns; j++){printf("%d ", matrix2[i][j]);}printf("\n");}
In Python: Let n be a (large) positive integer of your choice. We want to know which of the two Quicksort algorithms methods (he Lomuto method and the Hoare method) is superior when sorting arrays containing n elements. To answer this question, you will run Quicksort on 10000 random permutations of [1,2,3, . . . , n], on each of your two Quicksort algorithms.
I want this HANDWRITTEN because i dont understand it typed compute the big O time complexity (worst case) of the code. Show a table of steps to n , and thenall the summations and all work to compute the time complexity
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What is the best case scenario? What is the exact number of operations for the best case scenario in terms of n and m? What is the big-oh for the best case scenario? What is the worst case scenario? What is the exact number of operations for the worst case scenario in terms of n and m? What is the big-oh for the worst case scenario? Consider the following algorithm tha finds a row where every entry is x in a 2-D array. Assume that (a) the array has n rows and m columns, and that (b) the comparison on line 3 counts towards the running time.