Based on the master theorem, what is the solution to T (n) = 3T (2/2) + n² (n²) oe (n² logn) (n³) e (2¹)
Based on the master theorem, what is the solution to T (n) = 3T (2/2) + n² (n²) oe (n² logn) (n³) e (2¹)
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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![### Master Theorem Application
**Question:**
Based on the master theorem, what is the solution to \( T(n) = 3T\left(\frac{n}{2}\right) + n^2 \)?
**Options:**
- \( \Theta(n^2) \)
- \( \Theta(n^2 \log n) \)
- \( \Theta(n^3) \)
- \( \Theta(2^n) \)
_Selected Answer:_
- \( \Theta(2^n) \)
**Explanation:**
The given recurrence relation is \( T(n) = 3T\left(\frac{n}{2}\right) + n^2 \).
To solve this using the master theorem for divide-and-conquer recurrences of the form \( T(n) = aT\left(\frac{n}{b}\right) + f(n) \):
- \( a = 3 \)
- \( b = 2 \)
- \( f(n) = n^2 \)
Calculate \( \log_b{a} \):
\[ \log_2{3} \approx 1.585 \]
Compare \( f(n) \) to \( n^{\log_b{a}} \):
- \( f(n) = n^2 \)
- \( n^{\log_2{3}} \approx n^{1.585} \)
Since \( f(n) = n^2 \) grows faster than \( n^{\log_2{3}} \), we fall into Case 3 of the master theorem, where \( f(n) = \Omega(n^{c}) \) for \( c > \log_b{a} \).
Thus, the solution is:
\[ T(n) = \Theta(f(n)) = \Theta(n^2) \]](/v2/_next/image?url=https%3A%2F%2Fcontent.bartleby.com%2Fqna-images%2Fquestion%2Fb4fac401-f688-4a8e-b637-a2500ce6ac46%2F494c5993-8e67-4b08-ba78-17157c3256af%2Fjk34zta_processed.jpeg&w=3840&q=75)
Transcribed Image Text:### Master Theorem Application
**Question:**
Based on the master theorem, what is the solution to \( T(n) = 3T\left(\frac{n}{2}\right) + n^2 \)?
**Options:**
- \( \Theta(n^2) \)
- \( \Theta(n^2 \log n) \)
- \( \Theta(n^3) \)
- \( \Theta(2^n) \)
_Selected Answer:_
- \( \Theta(2^n) \)
**Explanation:**
The given recurrence relation is \( T(n) = 3T\left(\frac{n}{2}\right) + n^2 \).
To solve this using the master theorem for divide-and-conquer recurrences of the form \( T(n) = aT\left(\frac{n}{b}\right) + f(n) \):
- \( a = 3 \)
- \( b = 2 \)
- \( f(n) = n^2 \)
Calculate \( \log_b{a} \):
\[ \log_2{3} \approx 1.585 \]
Compare \( f(n) \) to \( n^{\log_b{a}} \):
- \( f(n) = n^2 \)
- \( n^{\log_2{3}} \approx n^{1.585} \)
Since \( f(n) = n^2 \) grows faster than \( n^{\log_2{3}} \), we fall into Case 3 of the master theorem, where \( f(n) = \Omega(n^{c}) \) for \( c > \log_b{a} \).
Thus, the solution is:
\[ T(n) = \Theta(f(n)) = \Theta(n^2) \]
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