What is Big O notation and algorithm analysis?
Big O Notation is a tool used to describe the time complexity of algorithms.
It calculates the time taken to run an algorithm as the input grows.
In other words, it calculates the worst-case time complexity of an algorithm.
Big O Notation in Data Structure describes the upper bound of an algorithm's runtime.Is performance analysis of an algorithm and algorithm complexity the same?
Performance analysis of an algorithm depends upon two factors i.e. amount of memory used and amount of compute time consumed on any CPU.
Formally they are notified as complexities in terms of: Space Complexity.What is the difference between problem complexity and algorithm complexity?
Problem complexity (lower bounds)
Thus the complexity of a problem is not greater than the complexity of any algorithm that solves the problems.
It follows that every complexity of an algorithm, that is expressed with big O notation, is also an upper bound on the complexity of the corresponding problem.- Complexity is measured in two dimensions: time (how long a function takes to complete), and space (how much memory a function consumes while executing).
So a simple way to think about a function's complexity is to consider the number of things it does or creates (x) as multiplied by the size of the input (n).
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