Anand Saminathan - OpenGenus IQ: Computing Expertise & Legacy

Divide and Conquer Optimization in Dynamic Programming

Divide and conquer optimization is used to optimize the run-time of a subset of Dynamic Programming problems from O(N^2) to O(N logN). We have demonstrated it with an example.

Algorithms

2-SAT problem

Boolean satisfiability is a NP-complete problem but, a special case of it can be solved in polynomial time. This special case is called case 2-SAT or 2-Satisfiability. We have explained the basic knowledge to understand this problem with depth along with solution.

Algorithms

Knuth's optimization in Dynamic Programming

Knuth's optimization is used to optimize the run-time of a subset of Dynamic programming problems from O(N^3) to O(N^2). We have explained this with a sample problem.

Algorithms

String Matching using Bitset

In this article, we have explored how to solve string matching problem (find all occurences of string S1 in another string S2) using Bitset. It is a modification of the naive approach which takes O(L x P x logL) time complexity which improves to O(L x P / K) using bitset.

Algorithms

DFS vs BFS (in detail)

DFS and BFS are two fundamental graph traversal algorithms and both are significantly different each with its own applications. DFS is used Kosaraju's algorithm while BFS is used in shortest path algorithms.