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What is algorithm and why analysis of it is important?

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  • Difficulty Level : Easy
  • Last Updated : 05 Jun, 2022

In this article, we will discuss why algorithm and its analysis is important? In the analysis of the algorithm, it generally focused on CPU (time) usage, Memory usage, Disk usage, and Network usage. All are important, but the most concern is about the CPU time. Be careful to differentiate between:

  • Performance: How much time/memory/disk/etc. is used when a program is run. This depends on the machine, compiler, etc. as well as the code we write.
  • Complexity: How do the resource requirements of a program or algorithm scale, i.e. what happens as the size of the problem being solved by the code gets larger.

Note: Complexity affects performance but not vice-versa.

Algorithm Analysis:
Algorithm analysis is an important part of computational complexity theory, which provides theoretical estimation for the required resources of an algorithm to solve a specific computational problem. Analysis of algorithms is the determination of the amount of time and space resources required to execute it.

Why Analysis of Algorithms is important?

  • To predict the behavior of an algorithm without implementing it on a specific computer.
  • It is much more convenient to have simple measures for the efficiency of an algorithm than to implement the algorithm and test the efficiency every time a certain parameter in the underlying computer system changes.
  • It is impossible to predict the exact behavior of an algorithm. There are too many influencing factors.
  • The analysis is thus only an approximation; it is not perfect.
  • More importantly, by analyzing different algorithms, we can compare them to determine the best one for our purpose.

Types of Algorithm Analysis:

  1. Best case 
  2. Worst case
  3. Average case
  • Best case: Define the input for which algorithm takes less time or minimum time. In the best case calculate the lower bound of an algorithm. Example: In the linear search when search data is present at the first location of large data then the best case occurs.
  • Worst Case: Define the input for which algorithm takes a long time or maximum time. In the worst calculate the upper bound of an algorithm. Example: In the linear search when search data is not present at all then the worst case occurs.
  • Average case: In the average case take all random inputs and calculate the computation time for all inputs.
    And then we divide it by the total number of inputs.

          Average case = all random case time / total no of case

           

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