What is the largest n for which one can solve within a day using an algorithm that requires f (n) bit operations, where each bit operation is carried out in 10" seconds, with these functions f (n)? a) log n b) 1000n c) n²

Advanced Engineering Mathematics
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ISBN:9780470458365
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Chapter2: Second-order Linear Odes
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discrete math

### Bit Operation Problem Analysis

#### 16. Determining the Largest `n` for Daily Computation

**Context:**  
Given an algorithm that requires `f(n)` bit operations, each taking \(10^{-11}\) seconds, determine the largest `n` that can be solved within a day for the following functions:

- **a)** \(\log n\)
- **b)** \(1000n\)
- **c)** \(n^2\)
- **d)** \(1000n^2\)
- **e)** \(n^3\)
- **f)** \(2^n\)
- **g)** \(2^2n\)
- **h)** \(2^{2^n}\)

#### 17. Determining the Largest `n` for Minute Computation

**Context:**  
Given an algorithm that requires `f(n)` bit operations, each taking \(10^{-12}\) seconds, determine the largest `n` that can be solved within a minute for the following functions:

- **a)** \(\log \log n\)
- **b)** \(\log n\)
- **c)** \((\log n)^2\)

**Note:** These problems involve understanding computational limits and the complexity of algorithms given constraints on time and computational resources.
Transcribed Image Text:### Bit Operation Problem Analysis #### 16. Determining the Largest `n` for Daily Computation **Context:** Given an algorithm that requires `f(n)` bit operations, each taking \(10^{-11}\) seconds, determine the largest `n` that can be solved within a day for the following functions: - **a)** \(\log n\) - **b)** \(1000n\) - **c)** \(n^2\) - **d)** \(1000n^2\) - **e)** \(n^3\) - **f)** \(2^n\) - **g)** \(2^2n\) - **h)** \(2^{2^n}\) #### 17. Determining the Largest `n` for Minute Computation **Context:** Given an algorithm that requires `f(n)` bit operations, each taking \(10^{-12}\) seconds, determine the largest `n` that can be solved within a minute for the following functions: - **a)** \(\log \log n\) - **b)** \(\log n\) - **c)** \((\log n)^2\) **Note:** These problems involve understanding computational limits and the complexity of algorithms given constraints on time and computational resources.
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