What does NP stand for in complexity classes theory?

In complexity theory, a problem is said to be in the complexity class Nondeterministic polynomial-time (NP), if there is an efficient algorithm that can solve it, but whose running time depends on the input size rather than the worst-case complexity.

What does NP stand for in computer science?

This is a very popular question and one that has been asked for years. NP is a complexity class of essentially intractable problems. It is widely known as the class of problems that can be solved by a non-deterministic Turing machine in polynomial time. This is a very short and broad definition, so don’t be surprised if it doesn’t make much sense to you right now.

What is NP stands for in discrete math?

The complexity class NP is a set of counting problems. A problem is in NP if we can have an efficient algorithm to solve it, given that we are given an efficient solution to any instance of an easier problem in NP. Although NP is not a specific problem, it is a set of many problems that are generally thought to be easy for humans but hard for computers. One example is the problem of determining if a graph contains a cycle.

What does NP stand for in complexity theory?

The complexity class NP is the class of all problems that can be solved in polynomial time by a non-deterministic Turing machine. The problems in NP are often referred to as decision problems as the output of a problem is either true or false. A problem is referred to as a "yes" instance if the answer is "true" and a "no" instance if the answer is "false".

What does NP stand for in combinatorics?

The complexity class NP is the class of decision problems that can be verified by a non-deterministic, polynomial time Turing machine. In other words, an NP problem is a problem that can be solved by guessing the right solution and checking if it is correct using a deterministic polynomial time algorithm.