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.
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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