For each of the following statement, identify if it is True or False: Big-O for worst case of Linear Search is linear. Big-O for worst case of Linear Search is logarithmic. Big-O for best cases of Linear Search and Binary Search are the same. Binary Search is better than Linear Search in the best case. Big-O for worst case and average case of Binary Search are the same. In Binary Search, Big-O for average case is better than Big-O for worst case. True False

For each of the following statement, identify if it is True or False: Big-O for worst case of Linear Search is linear. Big-O for worst case of Linear Search is logarithmic. Big-O for best cases of Linear Search and Binary Search are the same. Binary Search is better than Linear Search in the best case. Big-O for worst case and average case of Binary Search are the same. In Binary Search, Big-O for average case is better than Big-O for worst case. True False

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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Write a recurrence for this algorithm and solve it to obtain a tight upper bound on the worst case runtime of this algorithm. You can use any method you like for solving this recurrence.
I need the algorithm, proof of correctness and runtime analysis for the problem. No code necessary ONLY algorithm. And runtime should be O(n log n) as stated in the question.
There are n people who want to carpool during m days. On day i, some subset si ofpeople want to carpool, and the driver di must be selected from si . Each person j hasa limited number of days fj they are willing to drive. Give an algorithm to find a driverassignment di ∈ si each day i such that no person j has to drive more than their limit fj. (The algorithm should output “no” if there is no such assignment.) Hint: Use networkflow.For example, for the following input with n = 3 and m = 3, the algorithm could assignTom to Day 1 and Day 2, and Mark to Day 3. Person Day 1 Day 2 Day 3 Limit 1 (Tom) x x x 2 2 (Mark) x x 1 3 (Fred) x x 0
Appendix A 10-Fold Cross Validation for Parameter Selection Cross Validation is the standard method for evaluation in empirical machine learning. It can also be used for parameter selection if we make sure to use the training set only. To select parameter A of algorithm A(X) over an enumerated range d E [A1,..., A] using dataset D, we do the following: 1. Split the data D into 10 disjoint folds. 2. For each value of A e (A1,..., Ar]: (a) For i = 1 to 10 Train A(A) on all folds but ith fold Test on ith fold and record the error on fold i (b) Compute the average performance of A on the 10 folds. 3. Pick the value of A with the best average performance Now, in the above, D only includes the training data and the parameter A is chosen without the knowledge of the test data. We then re-train on the entire train set D using the chosen A and evaluate the result on the test set.
Answer only if you are 100% sure . Correct and detailed answer will be Upvoted else downvoted. Thank you!
Algorithm for LLP-GAN training algorithmInput: The training set L = {(Bi, pi)}n i=1; L: number of total iterations; λ: weight parameter.Input: The parameters of the final discriminator D.Set m to the total number of training data points
We can specify the PageRank algorithm's convergence threshold using proc networks. This figure has to be positive. Default value is 1E-9. When the difference between the PageRank scores of the current iteration and the previous iteration is less than or equal to the tolerance set, the PageRank algorithm ceases iterating. We can use the PAGERANKTOLERANCE= parameter to specify the convergence tolerance. Write The example code demonstrates how to calculate PageRank centrality using a proc network built on a directed graph with unweighted links.
Write a recurrence for this algorithm and solve it to obtain a tight upper bound on the worst case runtime of this algorithm. You can use any method you like for solving this recurrence.
Which type of best first search algorithm was used to predict the closeness of the end of path and its solution? a) Greedy BFS b) Divide and Conquer c) Heuristic BFS d) Combinatorial
Hat-check problem. Use indicator random variables to solve the following problem, which is known as the hat-check problem. Each of n customers gives a hat to a hat-check person at a restaurant. The hat-check person gives the hats back to the customers in a random order. What is the expected number of customers who get back their own hat?
A robot can move horizontally or vertically to any square in the same row or in the same column of a board. Find the number of the shortest paths by which a robot can move from one corner of a board to the diagonally opposite corner. The length of a path is measured by the number of squares it passes through, including the ﬁrst and the least squares. Write the recurrence relation if you solve the problem by a dynamic programming algorithm.
2. Recurrences (Master Method). Solve each recurrence below using the Master Method and write your answer using e-notation. Make sure that you show all your work including the corresponding case and the values of e or k used. If it is not possible to solve a recurrence using the Master Method, prove it by showing that the form is inapplicable or by showing that all 3 cases cannot be satisfied. (a) T(n)=37(n/2) + n² (b) T(n)=25T(n/5)+n (e) T(n)= T(3n/10)+n (d) T(n)= n³T(n) + 2n (e) T(n)= 3T(n/3) +n¹/3 (f) T(n)=9T(n/3) + n² lg n (g) T(n)=9T(n/3) + nlg²n (h) T(n)= T(n/3)+27 (n/5)+n (i) Bonus T(n) = 27 (√n) +logn