Time space trade off and Time Complexity

Time space trade off

The problem of trade off arises as time and memory space are costly and resources which have to be minimized. If sufficient storage space is available, optimum results will be obtained to use a algorithm using more space and less time. On the other hand if processing of a program requires huge time, it would be economical to use an algorithm involving more time and less space trade-off between time and space will be required till a continuous efficiency in performance in attached. So the space-time tradeoff refers to a choice between algorithmic solution of a date processing problem that allows one to decrease the running time of an algorithmic solution by increase the space to store the data and vice versa.

Time Complexity

Time complexity is commonly estimated by counting the number of elementary operations performed by the algorithm, where an elementary operation takes a fixed amount of time to perform. Thus the amount of time taken and the number of elementary operations performed by the algorithm differ by at most a constant factor.
Since an algorithm’s performance time may vary with different inputs of the same size, one commonly uses the worst-case time complexity of an algorithm, denoted as T(n), which is defined as the maximum amount of time taken on any input of size n. time complexities are classified by the nature of the function T(n). for instance, an algorithm with T(n) = O(n) is called a linear time algorithm, and an algorithm with T(n – O(2^n)) is said to be an exponential time algorithm.