time and growsAssume we have a sorted list. Binary search algorithm takescase as n grows.the linear search algorithm in the worst-O(n), slower thanO(log (n)), faster thanO(log (n)), it dependsO(n), faster thannone of the cases are correctO(log (n)), slower thanO O(n/2), at the same speed as

Assume we have a sorted list. Binary search algorithm takes time and grows the linear search algorithm in the worst- case as n grows. O(n), slower than O(log (n)), faster than O(log (n)), it depends O(n), faster than none of the cases are correct O(log (n)), slower than O O(n/2), at the same speed as

Assume we have a sorted list. Binary search algorithm takes time and grows the linear search algorithm in the worst- case as n grows. O(n), slower than O(log (n)), faster than O(log (n)), it depends O(n), faster than none of the cases are correct O(log (n)), slower than O O(n/2), at the same speed as

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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7. Suppose that you have two different algorithms for solving a problem. To solve a problem of size n, the first algorithm uses exactly n(log2 n) operations and the second algorithm uses exactly n3/2 operations. As n grows, which algorithm uses fewer operations?
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Merge sort is an efficient sorting algorithm with a time complexity of O(n log n). This means that as the number of elements (chocolates or students) increases significantly, the efficiency of merge sort remains relatively stable compared to other sorting algorithms. Merge sort achieves this efficiency by recursively dividing the input array into smaller sub-arrays, sorting them individually, and then merging them back together. The efficiency of merge sort is primarily determined by its time complexity, which is , where n is the number of elements in the array. This time complexity indicates that the time taken by merge sort grows logarithmically with the size of the input array. Therefore, even as the number of chocolates or students increases significantly, merge sort maintains its relatively efficient performance. Regarding the distribution of a given set of x to y using iterative and recursive functions, the complexity analysis depends on the specific implementation of each…
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The ColumnChoice problem takes as input a two-dimensional array A[1..m, 1..n] of Os and 1s with m rows and n columns along with a non-negative integer k < n. It asks whether there exists a subset SC {1, ..., n} of k columns such that for each row i e {1,..., m} there exists at least one column j e S where A[i, j] = 1. Prove that ColumnChoice is NP- complete.
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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 binary search 3. if value exists in alist, print "successful" otherwise print "unsuccessful" Question options: a. O(log n) b. O(n) c. O(n log n) d. O(2^n) e. O(n^2) f. O(1)
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