3. [7] I've developed a new, super cool sorting algorithm. Here's the procedure: Until sorted: 1. Randomly generate i = some number between e and n - 1 (n = size of array) 2. Randomly generate j = some number between i + 1 and n - 1 3. if array[i] > array[j], swap 4. Check to see if the array has been sorted List the algorithm's efficiency using all applicable measurements (e.g. Big Omega, Big 0, and Big Theta):

3. [7] I've developed a new, super cool sorting algorithm. Here's the procedure: Until sorted: 1. Randomly generate i = some number between e and n - 1 (n = size of array) 2. Randomly generate j = some number between i + 1 and n - 1 3. if array[i] > array[j], swap 4. Check to see if the array has been sorted List the algorithm's efficiency using all applicable measurements (e.g. Big Omega, Big 0, and Big Theta):

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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1- Consider the following function: f (n) = 10n +2n -20 For which positive integer m can you say that: a- f(n) is O(n") b- f(n) is 2(n") c- f(n) is O(n") 2- Sort the following integer array into increasing order using Quick Sort algorithm : 20 15
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Assume nums is an array of integers that looks like this: -3 12 9 0 2 -1 If we are sorting from smallest to largest, what will nums look like after 2 times through the outside loop if we are using bubble sort? Note: When you're entering the values, make sure you leave at least one space between them. Would it be possible to perform a linear search on a 2-D array? Answers: Yes, but you would be limited to using only one loop No, because a 2-D array is not stored in linear fashion in RAM Yes, as long as you used the proper nested loops No, you would have to use a binary search Yes, but only if the array's elements were sorted
PYTHON: Please provide a screenshot of your code in Python. Thank you!
def w19(X): Inputs: - X: A numpy array of shape (N, M) Returns: A numpy array S of shape (N, 1) where S[i, 0] is the sum of row i of X Hint: np.sum has an optional "keepdims" argument return None def w20(X): Inputs: - X: A numpy array of shape (N, M) Returns: A numpy array S of shape (N, 1) where S[i] is the L2 norm of row i of X I II return None Help write the following python functions in numpy please
Select all the statements that are false, bubble sort is defined below:* Quicksort is the fastest sorting algorithm If you are lucky, you can get O(n) time complexity in Mergesort On the worst case, Bubble sort will give you O(n logn) time complexity Average time complexity of insertion sort is much better than bubble sort Selection sort has the worst space complexity among all the sorting algorithm
231For the given array, find the kth largest element.** Note: Sorting is not allowed5Input format:Input:321564line 1 consist of array elements, separated by a spaceline 2 consist of k valueEg:output:Input:111output:
Every time it has to identify two arrays to combine, perform the following to create a bottom-up mergesort that benefits from array order: Locate the first entry in a sorted subarray, then the second, and finally merge them (by increasing a pointer until it finds an element in the array that is smaller than its predecessor). Consider the array size and the number of maximal ascending sequences in the array when calculating the algorithm's execution time.
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Question # 02: Let’s assume we want to find the largest number in a non-empty array A. A sample solution can be following: def max(A): answer = A[0] for j in range(1,len(A)): // in pseudo-code: for i=1,...,len(A)-1 if (A[j]>answer): answer = A[j] return answer Perform the code correction check including the loop invariance. Question # 03: illustrate the operation of merge sort on the array A = {3; 41; 52; 26; 38; 57; 9; 49}. Question # 04: Answer the following short questions: a) Is 2n+1 = O(2n)? b) Is 22n = O(2n)? c) f(n)=O(g(n)) implies g(n)=O(f(n)) d) f(n) = O((f(n))2)