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The running time of algorithms refers to how long it takes for an algorithm to complete a task. It impacts the efficiency of computational processes by determining how quickly a program can produce results. Algorithms with shorter running times are more efficient as they can process data faster, leading to quicker outcomes and better performance.

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Q: What are the running times of algorithms and how do they impact the efficiency of computational processes?
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What is the significance of the master's theorem in analyzing the time complexity of algorithms?

The master's theorem is important in analyzing the time complexity of algorithms because it provides a way to easily determine the time complexity of divide-and-conquer algorithms. By using the master's theorem, we can quickly understand how the running time of an algorithm grows as the input size increases, which is crucial for evaluating the efficiency of algorithms.


How to find the running time of an algorithm?

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What are the implications of superpolynomial time complexity in algorithm design and computational complexity theory?

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By solving a problem in n log n time complexity, the efficiency of an algorithm can be improved because it means the algorithm's running time increases at a slower rate as the input size grows. This allows the algorithm to handle larger inputs more efficiently compared to algorithms with higher time complexities.