

Can 'n' represent the number of possible states in a problem?
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what is n in big o notation
- How does 'n' relate to the upper bound of runtime complexity?
- Is 'n' always about the "amount" of input?
- How does 'n' relate to the efficiency of data access?
- Is 'n' always related to the size of the initial input?
- Is 'n' consistent in its interpretation across different problem domains (e.g., sorting, searching, graph problems)?
- Is 'n' relevant for algorithms with amortized time complexity?
- Can 'n' represent the number of random trials?
- Is 'n' consistent in its usage in different textbooks and research papers?
- Is 'n' considered in the best-case and average-case analysis?
- How does 'n' help in understanding the growth rate of an algorithm?
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