What does P NP imply?

What does P NP imply?

The statement P=NP means that if a problem takes polynomial time on a non-deterministic TM, then one can build a deterministic TM which would solve the same problem also in polynomial time. So far nobody has been able to show that it can be done, but nobody has been able to prove that it cannot be done, either.

What are P NP-hard and NP complete problems?

NP-Hard problems(say X) can be solved if and only if there is a NP-Complete problem(say Y) that can be reducible into X in polynomial time. NP-Complete problems can be solved by a non-deterministic Algorithm/Turing Machine in polynomial time. To solve this problem, it do not have to be in NP .

What do you understand by NP complete problems?

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.

Can NP algorithms be solved in polynomial time?

3.2. NP Algorithms The second set of problems cannot be solved in polynomial time. However, they can be verified (or certified) in polynomial time. We expect these algorithms to have an exponential complexity, which we’ll define as: where , and where , and are constants and is the input size. is a function of exponential-time when at least and .

What is the difference between P and NP problems?

P Problems. P is the set of all the decision problems solvable by deterministic algorithms in polynomial time. NP Problems. NP is the set of all the decision problems that are solvable by non – deterministic algorithms in polynomial time.

What is the difference between P and NP in machine learning?

P is the set of all the decision problems solvable by deterministic algorithms in polynomial time. NP is the set of all the decision problems that are solvable by non – deterministic algorithms in polynomial time. Since deterministic algorithms are just the special case of non – deterministic ones, so we can conclude that P is the subset of NP.

How to tell if an algorithm is exponential or polynomial time?

If the input size is going to be near , then the algorithm is going to behave more like an exponential. 3.2. NP Algorithms The second set of problems cannot be solved in polynomial time. However, they can be verified (or certified) in polynomial time.