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Graphs of functions commonly used in the analysis of algorithms, showing the number of operations N versus input size n for each function
Graphs of functions commonly used in the analysis of algorithms, showing the number of operations N versus input size n for each function
在算法分析中常用的函数图，显示每个函数的操作数 n 与输入大小 n 的对应关系
在算法分析中常用的函数图，显示每个函数的操作数 N 与输入大小 n 的对应关系
In [[computer science]], the '''analysis of algorithms''' is the process of finding the [[computational complexity]] of algorithms – the amount of time, storage, or other resources needed to [[computation|execute them]]. Usually, this involves determining a [[Function (mathematics)|function]] that relates the length of an algorithm's input to the number of steps it takes (its [[time complexity]]) or the number of storage locations it uses (its [[space complexity]]). An algorithm is said to be efficient when this function's values are small, or grow slowly compared to a growth in the size of the input. Different inputs of the same length may cause the algorithm to have different behavior, so [[best, worst and average case]] descriptions might all be of practical interest. When not otherwise specified, the function describing the performance of an algorithm is usually an [[upper bound]], determined from the worst case inputs to the algorithm.
In [[computer science]], the '''analysis of algorithms''' is the process of finding the [[computational complexity]] of algorithms – the amount of time, storage, or other resources needed to [[computation|execute them]]. Usually, this involves determining a [[Function (mathematics)|function]] that relates the length of an algorithm's input to the number of steps it takes (its [[time complexity]]) or the number of storage locations it uses (its [[space complexity]]). An algorithm is said to be efficient when this function's values are small, or grow slowly compared to a growth in the size of the input. Different inputs of the same length may cause the algorithm to have different behavior, so [[best, worst and average case]] descriptions might all be of practical interest. When not otherwise specified, the function describing the performance of an algorithm is usually an [[upper bound]], determined from the worst case inputs to the algorithm.