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Computational complexity refers to the amount of time and resources required to solve a problem using a computer. It is a measure of the efficiency of an algorithm or solution.
Computational complexity is typically measured by the time and space (memory) required to solve a problem. This is often represented using big O notation, which describes the growth rate of the time or space as the input size increases.
Time complexity refers to the number of operations or steps required to solve a problem, while space complexity refers to the amount of memory or storage required to solve a problem. Both are important factors in determining the overall computational complexity of an algorithm.
The computational complexity of an algorithm can greatly impact the performance of a real-world application. A more efficient algorithm will require less time and resources to solve a problem, making it more practical and cost-effective to use.
Yes, computational complexity can be improved by designing more efficient algorithms or by optimizing existing algorithms. This can involve reducing the number of steps required to solve a problem, or finding more efficient ways to use memory and resources.