In computer science, an optimization problem is the problem to find among all feasible solutions for some problem the best one. More formally, an optimization problem A is a four-tuple (I,f,m,g), where Wikibooks Wikiversity has more about this subject: School of Computer Science Open Directory Project: Computer Science Collection of Computer Science Bibliographies Belief that title science in computer science is inappropriate Categories: Computer science ...

• I is a set of instances;
• given an instance , f(x) is the set of feasible solutions;
• given an instance x and a feasible solution y of x, m(x,y) denotes the measure of y, which is usually a positive real.
• g is the goal function, and is either min or max.

The goal is then to find for some instance x an optimal solution, that is, a feasible solution y with

For each optimization problem, there is a corresponding decision problem that asks whether there is a feasible solution for some particular measure m₀. In logic, a decision problem is determining whether or not there exists a decision procedure or algorithm for a class S of questions requiring a Boolean value (i. ...

An NP optimization problem has the following further restrictions:

• I can be recognized in polynomial time, and
• the size of a feasible solution is polynomially bounded by the instance size.

This implies that the corresponding decision problem is in NP. Since interesting optimization problems usually fulfill these criteria, "optimization problem" is often used synonymous with "NP optimization problem". In computational complexity theory, Polynomial time refers to the computation time of a problem where the time, m(n), is no greater than a polynomial function of the problem size, n. ... In computational complexity theory, NP (Non-deterministic Polynomial time) is the set of decision problems solvable in polynomial time on a non-deterministic Turing machine. ...