If an algorithm has a time complexity of O(n log n), what can you infer about its runtime as the input size doubles?
It increases by a factor of log n
It increases exponentially
It remains constant
It increases slightly more than double
How does profiling differ from benchmarking in the context of algorithm optimization?
Profiling and benchmarking are essentially the same and can be used interchangeably.
Profiling is used for theoretical analysis, while benchmarking is used for real-world performance evaluation.
Profiling identifies performance bottlenecks within an algorithm, while benchmarking compares different algorithms.
Profiling focuses on measuring the memory usage of an algorithm, while benchmarking measures execution time.
What does an algorithm with a time complexity of O(n) signify?
The runtime increases exponentially with the input size
The runtime is unpredictable
The runtime increases linearly with the input size
The runtime is constant regardless of input size
Which notation provides both an upper and lower bound on the growth of a function, implying the function grows at the same rate as the specified function?
Little-omega (ω)
Big Theta (Θ)
Big-O (O)
Big Omega (Ω)
In what scenario might an algorithm with a worse theoretical time complexity perform better in practice than one with a better complexity?
All of the above.
When the input data size is very small.
When the algorithm with better complexity has a very large constant factor hidden in its Big O notation.
When the algorithm with worse complexity is implemented in a more efficient programming language.
How does time complexity analysis contribute to selecting the most suitable algorithm for a problem?
It eliminates the need for empirical testing of algorithms
It provides a theoretical estimate of an algorithm's efficiency, aiding in informed decisions
It dictates the specific data structures that should be used in the algorithm
It guarantees the shortest possible execution time for any input
Which of the following asymptotic notations represents the tightest upper bound on the growth of a function?
Little-o (o)
What is the worst-case time complexity of deleting an element from an unsorted array?
O(n)
O(n log n)
O(1)
O(log n)
Why is it essential to profile code even after achieving a satisfactory time complexity theoretically?
Profiling helps identify hidden performance bottlenecks that might not be apparent from theoretical analysis.
Profiling is optional and doesn't contribute much after theoretical optimization.
Profiling is only necessary for algorithms with very high time complexity.
Theoretical analysis is not reliable and profiling always reveals further optimizations.
Which of the following statements is TRUE regarding the trade-off between code optimization and readability?
Excessive optimization can sometimes hinder code readability, making maintenance difficult.
Highly optimized code is always easier to read and maintain.
Code readability is irrelevant as long as the code achieves optimal performance.
There's no trade-off; optimal performance and readability always go hand-in-hand.