Please answer both questions.

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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Please answer both questions.

Using the master theorem, what is the solution to the recurrence relation \( T(1) = 42, T(n) = 4T\left(\frac{n}{2}\right) + n^2\log^2(n) \)?

- a. \(\Theta(n \log(n))\)
- b. \(\Theta(n^2)\)
- c. \(\Theta(n^2 \log^2(n))\)
- d. \(\Theta(n^2 \log^3(n))\)
- e. \(\Theta(n^3)\)
Transcribed Image Text:Using the master theorem, what is the solution to the recurrence relation \( T(1) = 42, T(n) = 4T\left(\frac{n}{2}\right) + n^2\log^2(n) \)? - a. \(\Theta(n \log(n))\) - b. \(\Theta(n^2)\) - c. \(\Theta(n^2 \log^2(n))\) - d. \(\Theta(n^2 \log^3(n))\) - e. \(\Theta(n^3)\)
**Question 6 Explanation**

If \( L \) is a list of size \( n \), what is the asymptotic runtime of the following function?

```
function f(L)
    if length(L) < 3
        return L
    A = []
    B = []
    for i from 0 to length(L) - 1
        if i % 3 <= 1
            A.append(3 * L[i])
        else
            B.append(L[i])
    return f(A) + f(B) # A concatenated with B
```

### Options

a. \( \Theta(n) \)

b. \( \Theta(n \log^2(n)) \)

c. \( \Theta(n^{\log_{3/2}(2)} \log(n)) \)

d. \( \Theta(n^2) \)

e. \( \Theta(n^{\log_{3/2}(2)}) \)

### Analysis

This function is a recursive function where the list \( L \) is split into two lists, \( A \) and \( B \). The splitting is determined by the index modulo 3. After splitting, the function \( f \) is recursively called on both \( A \) and \( B \).

- **Base Case**: If the length of \( L \) is less than 3, the function returns \( L \) immediately.

- **Splitting Logic**: For each element in \( L \), if its index modulo 3 is less than or equal to 1, it's added to list \( A \) (and multiplied by 3), otherwise, it's added to list \( B \).

- **Recursive Call**: The function then calls itself on the newly formed lists \( A \) and \( B \), and concatenates their results.

The question asks for the asymptotic runtime, which is a measure of how the runtime grows with input size. By understanding the splitting logic and recursive nature of the calls, we can determine the complexity.

The correct answer involves analyzing the recursive breakdown and size reduction pattern, common in algorithms evaluated using the Master Theorem or other recursive complexity analysis techniques.
Transcribed Image Text:**Question 6 Explanation** If \( L \) is a list of size \( n \), what is the asymptotic runtime of the following function? ``` function f(L) if length(L) < 3 return L A = [] B = [] for i from 0 to length(L) - 1 if i % 3 <= 1 A.append(3 * L[i]) else B.append(L[i]) return f(A) + f(B) # A concatenated with B ``` ### Options a. \( \Theta(n) \) b. \( \Theta(n \log^2(n)) \) c. \( \Theta(n^{\log_{3/2}(2)} \log(n)) \) d. \( \Theta(n^2) \) e. \( \Theta(n^{\log_{3/2}(2)}) \) ### Analysis This function is a recursive function where the list \( L \) is split into two lists, \( A \) and \( B \). The splitting is determined by the index modulo 3. After splitting, the function \( f \) is recursively called on both \( A \) and \( B \). - **Base Case**: If the length of \( L \) is less than 3, the function returns \( L \) immediately. - **Splitting Logic**: For each element in \( L \), if its index modulo 3 is less than or equal to 1, it's added to list \( A \) (and multiplied by 3), otherwise, it's added to list \( B \). - **Recursive Call**: The function then calls itself on the newly formed lists \( A \) and \( B \), and concatenates their results. The question asks for the asymptotic runtime, which is a measure of how the runtime grows with input size. By understanding the splitting logic and recursive nature of the calls, we can determine the complexity. The correct answer involves analyzing the recursive breakdown and size reduction pattern, common in algorithms evaluated using the Master Theorem or other recursive complexity analysis techniques.
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