Rima

Complexity of an algorithm is the measurement of analysis of complete algorithm. Analyzing an algorithm means predicting the resources where the algorithm needs such as memory, logic gates, communication bandwidth, and time. Most often this is computational time which is measured for getting a more suitable algorithm. It is known as time complexity of defined algorithm. The running time of a program is also described as a function of the size of its input. On a particular input, This is traditionally measured as the digit of primitive operations or steps executed.

The analysis of algorithm concerns on time complexity & space complexity. As compared to time analysis, the analysis of space need for an algorithm is usually easier, but wherever necessary, both these techniques are used. The space is concerned to as storage needed in addition to the space required storing the input data. The total amount of memory required by program to run to completion is concerned to as space complexity. For a given algorithm, time complexity mainly depends upon the size of the input, therefore, this is a function of input size ‘n’. And the amount of time required by an algorithm to run to its completion is referred as time complexity.

The best algorithm to solve a given programming problem is one which requires very less memory & takes very less time to execute. But in tradition it is not always likely to achieve both of these objectives. There can be more than one approach to solve a same problem. One such approach may need more space but mostly takes less time to complete its execution while the other approach requires less space but more time to complete its execution.