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Questions and Answers
What is the main purpose of measuring computational complexity?
What is the term for the measure of the difficulty degree of an algorithm?
What is the focus of Learning Outcome L.O.1.2?
What is the trade-off involved in algorithm design?
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What is the main focus of algorithm design?
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What is the purpose of Big-O notation?
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What is the general format of algorithm efficiency?
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What is the value of f(n) in the linear loop for(i = 0; i < 1000; i++)?
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What is the value of f(n) in the logarithmic loop while(i = 1)?
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What is the formula to calculate the iterations in nested loops?
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What is the purpose of the variable 'n' in the algorithm efficiency?
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What is the difference between the linear loops for(i = 0; i < 1000; i++) and for(i = 0; i < 1000; i += 2)?
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Study Notes
Algorithm Complexity
- Computational complexity is a measure of an algorithm's difficulty degree in terms of time and/or space.
Algorithm Efficiency
- An algorithm's efficiency is measured by its computational complexity, considering the time and space required to solve a problem.
- The general format for expressing an algorithm's efficiency is f(n), where n is the size of the problem.
- The focus is on the key number that determines the size of the input data.
Linear Loops
- A linear loop's time complexity is directly proportional to the size of the input data (n).
- The number of times the body of the loop is replicated determines the time complexity.
- Examples: f(n) = n, f(n) = n/2.
Logarithmic Loops
- A logarithmic loop's time complexity is proportional to the logarithm of the size of the input data (log2 n).
- Multiply loops with divisions by 2 result in logarithmic time complexity.
- Examples: f(n) = log2 n.
Big-O Notation
- Not mentioned explicitly in this chapter, but it's a notation used to characterize the computational complexity of algorithms.
P and NP Problems
- Not mentioned explicitly in this chapter, but it's a concept related to computational complexity.
Learning Outcomes
- Define the concept of computational complexity and its special cases (best, average, and worst).
- Analyze algorithms using Big-O notation to characterize their computational complexity.
- List and compare complexity classes (e.g., constant, linear, etc.).
- Understand the trade-off between space and time in solutions.
- Describe strategies in algorithm design and problem solving.
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Description
This quiz covers Algorithm Complexity, including Big-O notation, problems and common complexities, and P and NP problems.
