The formal approach to determining the big-O complexity of an algorithm is to set up recurrence relations and solve them.The recurrence relation for the Linear Search algorithm is defined by:I. T(n)=T(n-1)+1II. T(n) = 2T(n/2)+1II. T(n)=T(n-1)+Theta(n)IV. T(n)=2T(n/2)+Theta(n)V. None of the aboveOvOII Suppose we need to write an efficient program to store N employee records for ABC Inc where each employee record is identified by unique employee id. Consider the following ways to store the records.1. An array list sorted by employee id2. A linked list sorted by employee id3. A linked list not sorted4. A balanced binary search tree with employee id as keyFor data structure above in 1-4, what is the average time to fınd an employee record given employee id as a key?A. O(log N), O(log N), O(N), O(1)B. O(log N), O(N), O(N), O(log N)C. O(N), O(log N). O(N), O(1)D. O(log N), O(log N), O(N), O(log N)E. O(log N), O(log N), O(log N), O(1)O AO DO EO B

- We need to choose for the recurrence of linear search and other time complexity in employee id…

The formal approach to determining the big-O complexity of an algorithm is to set up recurrence relations and solve them. The recurrence relation for the Linear Search algorithm is defined by: I. T(n)=T(n-1)+1 II. T(n) = 2T(n/2)+1 II. T(n)=T(n-1)+Theta(n) IV. T(n)=2T(n/2)+Theta(n) V. None of the above Ov O II

The formal approach to determining the big-O complexity of an algorithm is to set up recurrence relations and solve them. The recurrence relation for the Linear Search algorithm is defined by: I. T(n)=T(n-1)+1 II. T(n) = 2T(n/2)+1 II. T(n)=T(n-1)+Theta(n) IV. T(n)=2T(n/2)+Theta(n) V. None of the above Ov O II

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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