Search results
Results From The WOW.Com Content Network
There is no known algorithm that efficiently solves each SAT problem, and it is generally believed that no such algorithm exists, but this belief has not been proven mathematically, and resolving the question of whether SAT has a polynomial-time algorithm is equivalent to the P versus NP problem, which is a famous open problem in the theory of ...
Faculty of Architecture, AKTU(Formerly: Lucknow College of Architecture & GCA) 2 052 Institute of Engineering and Technology(IET) 3 053 Azad Institute of Engineering & Technology 4 054 B.B.D National Institute of Tech. & Management 5 056 Babu Banarasi Das Northern India Institute of Technology 6 057
John Hopcroft brought everyone at the conference to a consensus that the question of whether NP-complete problems are solvable in polynomial time should be put off to be solved at some later date, since nobody had any formal proofs for their claims one way or the other. [citation needed] This is known as "the question of whether P=NP".
The Bellman–Ford algorithm is an algorithm that computes shortest paths from a single source vertex to all of the other vertices in a weighted digraph. [1] It is slower than Dijkstra's algorithm for the same problem, but more versatile, as it is capable of handling graphs in which some of the edge weights are negative numbers. [2]
The master theorem always yields asymptotically tight bounds to recurrences from divide and conquer algorithms that partition an input into smaller subproblems of equal sizes, solve the subproblems recursively, and then combine the subproblem solutions to give a solution to the original problem.
Since the ground-breaking 1965 paper by Juris Hartmanis and Richard E. Stearns [1] and the 1979 book by Michael Garey and David S. Johnson on NP-completeness, [2] the term "computational complexity" (of algorithms) has become commonly referred to as asymptotic computational complexity.
In linear algebra, the Strassen algorithm, named after Volker Strassen, is an algorithm for matrix multiplication.It is faster than the standard matrix multiplication algorithm for large matrices, with a better asymptotic complexity, although the naive algorithm is often better for smaller matrices.
A simple example of an approximation algorithm is one for the minimum vertex cover problem, where the goal is to choose the smallest set of vertices such that every edge in the input graph contains at least one chosen vertex.