The following functions have different growth rate (i.e., as n increases by 1, T(n) may increase to different extents depending or what T(n) is). List the following functions by growth rate in increasing order. • T1 = 20n • T2 = n² + 2n T. = n2 + 101ogn

C++ for Engineers and Scientists
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Chapter6: Modularity Using Functions
Section6.2: Returning A Single Value
Problem 7E
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**Understanding Function Growth Rates**

The following functions have different growth rates (i.e., as \( n \) increases by 1, \( T(n) \) may increase to different extents depending on what \( T(n) \) is). List the following functions by growth rate in increasing order.

- \( T_1 = 20n \)
- \( T_2 = n^2 + 2n \)
- \( T_3 = n^2 + 10\log_2 n \)
- \( T_4 = 2^n \)

### Explanation:

1. **\( T_1 = 20n \)**
   - This function grows linearly with \( n \). As \( n \) increases, \( T_1 \) increases proportionally.

2. **\( T_2 = n^2 + 2n \)**
   - This function is a quadratic function because the \( n^2 \) term will dominate as \( n \) becomes large.

3. **\( T_3 = n^2 + 10\log_2 n \)**
   - Although this function has a logarithmic component, the \( n^2 \) term will dominate as \( n \) becomes large, making it essentially a quadratic function.

4. **\( T_4 = 2^n \)**
   - This function is exponential, which grows much faster than polynomial functions for large \( n \).

### Ordered Growth Rates:

From the slowest growth rate to the fastest:
\[ T_1 < T_2 \approx T_3 < T_4 \]

#### Detailed Explanation of Growth:

- **Linear Growth (\( T_1 = 20n \))**:
  The simplest growth pattern, where the function increases by a constant amount as \( n \) increases.
  
- **Quadratic Growth (\( T_2 \) and \( T_3 \))**:
  These functions increase much more quickly than linear functions because their dominant term is \( n^2 \). Although \( T_2 \) and \( T_3 \) may seem different, the additional \( 2n \) and \( 10\log_2 n \) terms are insignificant relative to the \( n^2 \) term for large \( n \).

- **Exponential Growth (\( T_4 = 2^n \))**:
  This
Transcribed Image Text:**Understanding Function Growth Rates** The following functions have different growth rates (i.e., as \( n \) increases by 1, \( T(n) \) may increase to different extents depending on what \( T(n) \) is). List the following functions by growth rate in increasing order. - \( T_1 = 20n \) - \( T_2 = n^2 + 2n \) - \( T_3 = n^2 + 10\log_2 n \) - \( T_4 = 2^n \) ### Explanation: 1. **\( T_1 = 20n \)** - This function grows linearly with \( n \). As \( n \) increases, \( T_1 \) increases proportionally. 2. **\( T_2 = n^2 + 2n \)** - This function is a quadratic function because the \( n^2 \) term will dominate as \( n \) becomes large. 3. **\( T_3 = n^2 + 10\log_2 n \)** - Although this function has a logarithmic component, the \( n^2 \) term will dominate as \( n \) becomes large, making it essentially a quadratic function. 4. **\( T_4 = 2^n \)** - This function is exponential, which grows much faster than polynomial functions for large \( n \). ### Ordered Growth Rates: From the slowest growth rate to the fastest: \[ T_1 < T_2 \approx T_3 < T_4 \] #### Detailed Explanation of Growth: - **Linear Growth (\( T_1 = 20n \))**: The simplest growth pattern, where the function increases by a constant amount as \( n \) increases. - **Quadratic Growth (\( T_2 \) and \( T_3 \))**: These functions increase much more quickly than linear functions because their dominant term is \( n^2 \). Although \( T_2 \) and \( T_3 \) may seem different, the additional \( 2n \) and \( 10\log_2 n \) terms are insignificant relative to the \( n^2 \) term for large \( n \). - **Exponential Growth (\( T_4 = 2^n \))**: This
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