What makes some problems computationally hard easy?

A problem is “hard” if it requires (or we think it requires) “large” computational resources to solve, and “easy” if it doesn’t. “Large” depends on context but, in most contexts, a problem that can be solved in polynomial time is considered “easy”.

What is a computationally hard problem?

Computationally hard: a problem for which there is no proven algorithm for solving in a reasonable time. The larger the problem set, the harder it is to solve by any means.

What does it mean if a problem is in NP?

NP-complete problem, any of a class of computational problems for which no efficient solution algorithm has been found.

What does NP stand for complexity?

nondeterministic polynomial time
In computational complexity theory, NP (nondeterministic polynomial time) is a complexity class used to classify decision problems.

What are the different types of computational problem?

Contents

• 1.1 Decision problem.
• 1.2 Search problem.
• 1.3 Counting problem.
• 1.4 Optimization problem.
• 1.5 Function problem.

What are computational problems explain with examples?

An example of a computational problem that is (thought to be) computationally difficult is the factoring (or factorization) problem: given an (odd) integer, determine its prime factors. The factorization problem cannot be solved efficiently by any known classical computing algorithm.

Which of the following is NP-hard problem?

NP-complete problems are the hardest problems in the NP set. A decision problem L is NP-complete if: 1) L is in NP (Any given solution for NP-complete problems can be verified quickly, but there is no efficient known solution).

Are all problems in NP?

A problem is said to be NP-hard if everything in NP can be transformed in polynomial time into it even though it may not be in NP. Conversely, a problem is NP-complete if it is both in NP and NP-hard. If some NP-complete problem has a polynomial time algorithm, all problems in NP do.

What is nondeterministic computer?

In computer programming, a nondeterministic algorithm is an algorithm that, even for the same input, can exhibit different behaviors on different runs, as opposed to a deterministic algorithm. There are several ways an algorithm may behave differently from run to run.