# How are the complexity classes P and NP related?

## How are the complexity classes P and NP related?

The solutions of the NP class are hard to find since they are being solved by a non-deterministic machine but the solutions are easy to verify….Types of Complexity Classes | P, NP, CoNP, NP hard and NP complete.

Complexity Class Characteristic feature
P Easily solvable in polynomial time.
NP Yes, answers can be checked in polynomial time.

## What is the difference between P and NP complexity class?

P = the set of problems that are solvable in polynomial time by a Deterministic Turing Machine. NP = the set of decision problems (answer is either yes or no) that are solvable in nondeterministic polynomial time i.e can be solved in polynomial time by a Nondeterministic Turing Machine.

## Which are complexity classes?

Complexity classes are sets of related computational problems. They are defined in terms of the computational difficulty of solving the problems contained within them with respect to particular computational resources like time or memory.

## What do you mean by P complexity class?

In computational complexity theory, P, also known as PTIME or DTIME(n), is a fundamental complexity class. It contains all decision problems that can be solved by a deterministic Turing machine using a polynomial amount of computation time, or polynomial time.

## What is NP problem example?

An example of an NP-hard problem is the decision subset sum problem: given a set of integers, does any non-empty subset of them add up to zero? That is a decision problem and happens to be NP-complete.

## What is the relationship between the classes P and NP explain?

NP is set of problems that can be solved by a Non-deterministic Turing Machine in Polynomial time. P is subset of NP (any problem that can be solved by deterministic machine in polynomial time can also be solved by non-deterministic machine in polynomial time) but P≠NP.

## Is chess a NP?

As a decision problem, it’s complexity is characterized as EXPTIME-complete as the proof also requires an exponential time to check. Due to the same reason, it cannot be in NP. Chess comes under NP Hard problem.

## How many complexity classes are there?

Some common complexity classes are constant complexity (O(1)), logarithmic complexity (O(lgn)), linear complexity (O(n)), polynomial complexity (O(nk), for some fixed value of k and exponential complexity (e.g., O(2n)).

## What is P problem example?

P is the set of decision problems solvable in time polynomial in the size of the input, where time is typically measured in terms of the number of basic mathematical operations performed. An example would be basic multiplication of two numbers.

## What is the difference between P and NP?

Roughly speaking, P is a set of relatively easy problems, and NP is a set that includes what seem to be very, very hard problems, so P = NP would imply that the apparently hard problems actually have relatively easy solutions.

## What is NP-complete give example?

NP-complete problem, any of a class of computational problems for which no efficient solution algorithm has been found. Many significant computer-science problems belong to this class—e.g., the traveling salesman problem, satisfiability problems, and graph-covering problems.

## What is a complexity class in Computer Science?

Complexity Classes. Complexity classes are the heart of complexity theory which is a central topic in theoretical computer science. A complexity class contains a set of problems that take a similar range of space and time to solve, for example “all problems solvable in polynomial time with respect to input size,” “all problems solvable…

## How do you prove a problem is in a particular complexity class?

Problems are usually proven to be in a particular complexity class by running the problem on an abstract computational model, usually a Turing machine.

## What is the space complexity of an algorithm?

The space complexity of an algorithm describes how much memory the algorithm needs in order to operate. In terms of Turing machines, the space needed to solve a problem relates to the number of spaces on the Turing machine’s tape it needs to do the problem.