Pseudo-polynomial Algorithms What is Pseudo-polynomial? An algorithm whose worst case time complexity depends on numeric value of input (not number of inputs) is called Pseudo-polynomial algorithm. For example, consider the problem of counting frequencies of all elements in an array of positive numbers. A pseudo-polynomial time solution for this is to first find the maximum

# Tagbit

## Analysis of Algorithm | Set 4 (Solving Recurrences)

Analysis of Algorithm | Set 4 (Solving Recurrences) In the previous post, we discussed analysis of loop. Many algorithms are recursive in nature. When we analyze them, we get a recurrence relation for time complexity. We get running time on an input of size n as a function of n and the running time on inputs of

## Analysis of Algorithms | Set 4 (Analysis of Loops)

Analysis of Algorithms | Set 4 (Analysis of Loops) We have discussed asymtotic analysis, best worst and average cases and asymptotic notations in previous posts. In this post, analysis of iterative programs with simple examples is discussed. 1) O(1): Time complexity of a function (or set of statements) is considered as O(1) if it doesn’t contain

## Analysis of Algorithms | Set 2 (Worst, Average and Best Cases)

Analysis of Algorithms | Set 2 (Worst, Average and Best Cases) In the previous post, we discussed how Asymptotic analysis overcomes the problems of naive way of analyzing algorithms. In this post, we will take an example of Linear Search and analyze it using Asymptotic analysis. We can have three cases to analyze an algorithm:

## Asymptotic Analysis

Analysis of Algorithms | Set 1 (Asymptotic Analysis) Why performance analysis? There are many important things that should be taken care of, like user friendliness, modularity, security, maintainability, etc. Why to worry about performance? The answer to this is simple, we can have all the above things only if we have performance. So performance is