Code Optimisation Questions Medium
In code optimization, time complexity and space complexity are two important factors to consider.
Time complexity refers to the amount of time it takes for an algorithm to run as a function of the input size. It measures the efficiency of an algorithm in terms of the number of operations performed. Time complexity is usually expressed using big O notation, which provides an upper bound on the growth rate of the algorithm. It helps in understanding how the algorithm's performance scales with the input size. A lower time complexity indicates a more efficient algorithm.
Space complexity, on the other hand, refers to the amount of memory or space required by an algorithm to run as a function of the input size. It measures the efficiency of an algorithm in terms of the amount of memory it uses. Similar to time complexity, space complexity is also expressed using big O notation. It helps in understanding how the algorithm's memory usage scales with the input size. A lower space complexity indicates a more memory-efficient algorithm.
In summary, time complexity focuses on the efficiency of an algorithm in terms of the number of operations performed, while space complexity focuses on the efficiency in terms of the memory or space required. Both time and space complexity are crucial in code optimization as they help in identifying and improving the performance bottlenecks of an algorithm.