Define Time complexity and Space complexity

Time complexity deals with the amount of time required by an algorithm to complete its task. Space complexity is a measure of the amount of memory needed by the algorithm to complete its task.

Time complexity is an important metric for assessing the speed of algorithms, as it gives an indication of how well an algorithm scales in terms of the time taken for it to complete its task. Time complexity is measured in terms of Big O notation, which is used to compare the asymptotic growth of the running time of algorithms. Big O notation is a mathematical notation that measures the growth rate in terms of a function of the input data size.

Space complexity on the other hand is the amount of memory required by an algorithm to complete its job. As the input size increases, the amount of space required to store the data also increases and this is known as the space complexity of the algorithm. It is also measured in terms of Big O notation, which is used to compare the asymptotic growth of memory usage of algorithms.

It is important to understand the time and space complexity of an algorithm before making a decision to use it in a particular application. Knowing both of these complexits can help to determine which algorithm is most suitable for a given task and also be beneficial in optimizing and improving the efficiency of algorithms.