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Measuring Algorithm's Performance

Most common data structures you may have encountered if you have done programming could be :

  • Arrays
  • Linked Lists
  • Stacks and Queues
  • Trees
  • Hash Tables

While considering any problem statement, We can have various solutions involving multiple algorithms. Algorithms are usually measured in terms of two factors :

  • Time Constraint
  • Space Constraint
While considering time constraints, Algorithmic performance can be measured with various asymptotic notations such as Big Omega,Big Theta or Big-O. 

Measuring Algorithmic Performance :

Big-O notation is used to describe algorithmic performance. The letter 'O' indicates the order of operation which in turn denotes the growth rate of algorithm complexity.

Big-O is considered generally in worst-case scenarios where it provides an upper bound for the run time of an algorithm. Since upper bound can easily be determined on time complexity of an algorithm, Big-O is the mostly commonly used notation.

Common Big-O terms :


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